540 lines
18 KiB
Python
540 lines
18 KiB
Python
import json
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import multiprocessing as mp
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import os
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import random
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import threading
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import time
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import unittest
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from concurrent.futures import ThreadPoolExecutor
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from functools import partial
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from types import SimpleNamespace
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from typing import List, Optional
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import numpy as np
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import requests
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import torch
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import sglang as sgl
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from sglang.srt.hf_transformers_utils import get_tokenizer
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from sglang.srt.utils import kill_process_tree
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from sglang.test.few_shot_gsm8k import run_eval
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from sglang.test.runners import DEFAULT_PROMPTS, SRTRunner
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from sglang.test.test_utils import (
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DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
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DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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popen_launch_server,
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run_logprob_check,
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)
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torch_dtype = torch.float16
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prefill_tolerance = 5e-2
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decode_tolerance: float = 5e-2
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class TestEAGLEEngine(CustomTestCase):
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BASE_CONFIG = {
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"model_path": DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
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"speculative_draft_model_path": DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
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"speculative_algorithm": "EAGLE",
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"speculative_num_steps": 5,
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"speculative_eagle_topk": 4,
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"speculative_num_draft_tokens": 8,
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"mem_fraction_static": 0.7,
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"cuda_graph_max_bs": 4,
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}
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NUM_CONFIGS = 2
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def setUp(self):
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self.prompt = "Today is a sunny day and I like"
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self.sampling_params = {"temperature": 0, "max_new_tokens": 8}
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ref_engine = sgl.Engine(
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model_path=self.BASE_CONFIG["model_path"], cuda_graph_max_bs=1
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)
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self.ref_output = ref_engine.generate(self.prompt, self.sampling_params)["text"]
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ref_engine.shutdown()
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def test_correctness(self):
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configs = [
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# Basic config
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self.BASE_CONFIG,
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# Chunked prefill
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{**self.BASE_CONFIG, "chunked_prefill_size": 4},
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]
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for i, config in enumerate(configs[: self.NUM_CONFIGS]):
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with self.subTest(i=i):
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print(f"{config=}")
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engine = sgl.Engine(**config, log_level="info", decode_log_interval=10)
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try:
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self._test_single_generation(engine)
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self._test_batch_generation(engine)
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self._test_eos_token(engine)
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self._test_acc_length(engine)
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finally:
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engine.shutdown()
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print("=" * 100)
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def _test_single_generation(self, engine):
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output = engine.generate(self.prompt, self.sampling_params)["text"]
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print(f"{output=}, {self.ref_output=}")
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self.assertEqual(output, self.ref_output)
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def _test_batch_generation(self, engine):
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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params = {"temperature": 0, "max_new_tokens": 50}
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outputs = engine.generate(prompts, params)
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for prompt, output in zip(prompts, outputs):
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print(f"Prompt: {prompt}")
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print(f"Generated: {output['text']}")
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print("-" * 40)
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print(f"{engine.get_server_info()=}")
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avg_spec_accept_length = engine.get_server_info()["avg_spec_accept_length"]
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print(f"{avg_spec_accept_length=}")
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self.assertGreater(avg_spec_accept_length, 1.9)
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def _test_eos_token(self, engine):
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prompt = "[INST] <<SYS>>\nYou are a helpful assistant.\n<</SYS>>\nToday is a sunny day and I like [/INST]"
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params = {
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"temperature": 0.1,
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"max_new_tokens": 1024,
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"skip_special_tokens": False,
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}
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tokenizer = get_tokenizer(DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST)
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output = engine.generate(prompt, params)["text"]
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print(f"{output=}")
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tokens = tokenizer.encode(output, truncation=False)
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self.assertNotIn(tokenizer.eos_token_id, tokens)
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def _test_acc_length(self, engine):
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prompt = [
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"Human: Give me a fully functional FastAPI server. Show the python code.\n\nAssistant:",
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] * 5 # test batched generation
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sampling_params = {"temperature": 0, "max_new_tokens": 512}
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output = engine.generate(prompt, sampling_params)
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output = output[0]
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if "spec_verify_ct" in output["meta_info"]:
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acc_length = (
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output["meta_info"]["completion_tokens"]
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/ output["meta_info"]["spec_verify_ct"]
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)
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else:
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acc_length = 1.0
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speed = (
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output["meta_info"]["completion_tokens"]
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/ output["meta_info"]["e2e_latency"]
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)
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print(f"{acc_length=}")
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if engine.server_args.model_path == DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST:
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self.assertGreater(acc_length, 3.6)
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else:
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self.assertGreater(acc_length, 2.6)
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class TestEAGLEEngineTokenMap(TestEAGLEEngine):
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BASE_CONFIG = {
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"model_path": "meta-llama/Meta-Llama-3-8B-Instruct",
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"speculative_draft_model_path": "lmsys/sglang-EAGLE-LLaMA3-Instruct-8B",
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"speculative_algorithm": "EAGLE",
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"speculative_num_steps": 5,
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"speculative_eagle_topk": 4,
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"speculative_num_draft_tokens": 8,
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"speculative_token_map": "thunlp/LLaMA3-Instruct-8B-FR-Spec/freq_32768.pt",
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"mem_fraction_static": 0.7,
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"cuda_graph_max_bs": 4,
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"dtype": "float16",
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}
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NUM_CONFIGS = 1
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class TestEAGLE3Engine(TestEAGLEEngine):
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BASE_CONFIG = {
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"model_path": "meta-llama/Llama-3.1-8B-Instruct",
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"speculative_draft_model_path": "jamesliu1/sglang-EAGLE3-Llama-3.1-Instruct-8B",
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"speculative_algorithm": "EAGLE3",
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"speculative_num_steps": 5,
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"speculative_eagle_topk": 16,
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"speculative_num_draft_tokens": 64,
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"mem_fraction_static": 0.7,
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"cuda_graph_max_bs": 4,
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"dtype": "float16",
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}
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NUM_CONFIGS = 1
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class TestEAGLEServer(CustomTestCase):
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PROMPTS = [
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"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nToday is a sunny day and I like[/INST]"
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'[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nWhat are the mental triggers in Jeff Walker\'s Product Launch Formula and "Launch" book?[/INST]',
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"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nSummarize Russell Brunson's Perfect Webinar Script...[/INST]",
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"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nwho are you?[/INST]",
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"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nwhere are you from?[/INST]",
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]
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@classmethod
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def setUpClass(cls):
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.process = popen_launch_server(
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DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=[
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"--speculative-algorithm",
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"EAGLE",
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"--speculative-draft-model-path",
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DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
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"--speculative-num-steps",
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5,
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"--speculative-eagle-topk",
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8,
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"--speculative-num-draft-tokens",
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64,
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"--mem-fraction-static",
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0.7,
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"--chunked-prefill-size",
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128,
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"--max-running-requests",
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8,
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],
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)
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@classmethod
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def tearDownClass(cls):
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kill_process_tree(cls.process.pid)
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def send_request(self):
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time.sleep(random.uniform(0, 2))
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for prompt in self.PROMPTS:
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url = self.base_url + "/generate"
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data = {
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"text": prompt,
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"sampling_params": {
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"temperature": 0,
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"max_new_tokens": 1024,
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},
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}
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response = requests.post(url, json=data)
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assert response.status_code == 200
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def send_requests_abort(self):
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for prompt in self.PROMPTS:
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try:
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time.sleep(random.uniform(0, 2))
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url = self.base_url + "/generate"
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data = {
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"model": "base",
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"text": prompt,
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"sampling_params": {
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"temperature": 0,
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"max_new_tokens": 1024,
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},
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}
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# set timeout = 1s, mock disconnected
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requests.post(url, json=data, timeout=1)
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except Exception as e:
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print(e)
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pass
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def test_request_abort(self):
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concurrency = 4
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threads = [
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threading.Thread(target=self.send_request) for _ in range(concurrency)
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] + [
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threading.Thread(target=self.send_requests_abort)
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for _ in range(concurrency)
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]
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for worker in threads:
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worker.start()
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for p in threads:
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p.join()
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def test_max_token_one(self):
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requests.get(self.base_url + "/flush_cache")
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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num_questions=200,
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max_new_tokens=1,
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parallel=128,
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host="http://127.0.0.1",
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port=int(self.base_url.split(":")[-1]),
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)
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# Just run and check it does not hang
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metrics = run_eval(args)
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self.assertGreater(metrics["output_throughput"], 50)
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def test_gsm8k(self):
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requests.get(self.base_url + "/flush_cache")
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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num_questions=200,
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max_new_tokens=512,
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parallel=128,
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host="http://127.0.0.1",
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port=int(self.base_url.split(":")[-1]),
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)
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metrics = run_eval(args)
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print(f"{metrics=}")
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self.assertGreater(metrics["accuracy"], 0.20)
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server_info = requests.get(self.base_url + "/get_server_info")
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avg_spec_accept_length = server_info.json()["avg_spec_accept_length"]
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print(f"{avg_spec_accept_length=}")
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self.assertGreater(avg_spec_accept_length, 3.5)
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# Wait a little bit so that the memory check happens.
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time.sleep(4)
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def test_logprob_start_len(self):
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logprob_start_len = 4
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new_tokens = 4
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prompts = [
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"I have a very good idea on",
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"Today is a sunndy day and",
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]
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response = requests.post(
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self.base_url + "/generate",
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json={
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"text": prompts,
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"sampling_params": {
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"temperature": 0,
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"max_new_tokens": new_tokens,
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},
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"return_logprob": True,
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"top_logprobs_num": 5,
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"logprob_start_len": logprob_start_len,
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},
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)
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response_json = response.json()
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print(json.dumps(response_json, indent=2))
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for res in response_json:
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self.assertEqual(
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res["meta_info"]["prompt_tokens"],
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logprob_start_len + len(res["meta_info"]["input_token_logprobs"]),
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)
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self.assertEqual(res["meta_info"]["completion_tokens"], new_tokens)
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self.assertEqual(len(res["meta_info"]["output_token_logprobs"]), new_tokens)
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def test_logprob_match(self):
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"""Test the output logprobs are close to the input logprobs if we run a prefill again."""
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def run_generate(
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prompt, return_logprob=False, max_new_tokens=512, logprob_start_len=-1
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):
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if isinstance(prompt, str):
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prompt_kwargs = {"text": prompt}
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else:
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prompt_kwargs = {"input_ids": prompt}
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response = requests.post(
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self.base_url + "/generate",
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json={
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**prompt_kwargs,
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"sampling_params": {
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"temperature": 1.0,
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"max_new_tokens": max_new_tokens,
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"ignore_eos": True,
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},
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"return_logprob": return_logprob,
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"return_text_in_logprobs": True,
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"logprob_start_len": logprob_start_len,
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},
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)
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return response.json()
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prompt = "I have a very good idea on how to"
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gen = run_generate(prompt, return_logprob=True, logprob_start_len=0)
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output_logprobs = np.array(
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[x[0] for x in gen["meta_info"]["output_token_logprobs"]]
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)
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num_prompts_tokens = gen["meta_info"]["prompt_tokens"]
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input_tokens = [x[1] for x in gen["meta_info"]["input_token_logprobs"]]
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output_tokens = [x[1] for x in gen["meta_info"]["output_token_logprobs"]]
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new_prompt = input_tokens + output_tokens
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score = run_generate(
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new_prompt, return_logprob=True, logprob_start_len=0, max_new_tokens=0
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)
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output_logprobs_score = np.array(
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[
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x[0]
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for x in score["meta_info"]["input_token_logprobs"][num_prompts_tokens:]
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]
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)
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print(f"{output_logprobs[-10:]=}")
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print(f"{output_logprobs_score[-10:]=}")
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diff = np.abs(output_logprobs - output_logprobs_score)
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max_diff = np.max(diff)
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self.assertLess(max_diff, 0.25)
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def test_logprob_mixed(self):
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args = []
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temperature = 0
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# input_len, output_len, temperature, logprob_start_len, return_logprob, top_logprobs_num
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# Llama 2 context length seems to be only 2k, so we can only test small length.
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for input_len in [200, 500, 1000, 2000]:
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for output_len in [4, 8]:
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for logprob_start_len in [0, 100, 300, 800, 1998]:
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for return_logprob in [True, False]:
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for top_logprobs_num in [0, 5]:
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if logprob_start_len >= input_len:
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continue
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args.append(
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(
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input_len,
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output_len,
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temperature,
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logprob_start_len,
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return_logprob,
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top_logprobs_num,
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)
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)
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random.shuffle(args)
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func = partial(run_logprob_check, self)
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with ThreadPoolExecutor(8) as executor:
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list(executor.map(func, args))
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def run_decode(self, sampling_params):
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return_logprob = True
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top_logprobs_num = 5
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return_text = True
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n = 1
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response = requests.post(
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self.base_url + "/generate",
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json={
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"text": "Human: Write a travel blog post to Hawaii.\n\nAssistant:",
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"sampling_params": {
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"max_new_tokens": 48,
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"n": n,
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"temperature": 0.7,
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**sampling_params,
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},
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"return_logprob": return_logprob,
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"top_logprobs_num": top_logprobs_num,
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"return_text_in_logprobs": return_text,
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"logprob_start_len": 0,
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},
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)
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self.assertEqual(response.status_code, 200)
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print(json.dumps(response.json()))
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print("=" * 100)
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def test_penalty_mixed(self):
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args = [
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{},
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{},
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{},
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{"frequency_penalty": 2},
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{"presence_penalty": 1},
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{"min_new_tokens": 16},
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{"frequency_penalty": 0.2},
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{"presence_penalty": 0.4},
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{"min_new_tokens": 8},
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{"frequency_penalty": 0.4, "presence_penalty": 0.8},
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{"frequency_penalty": 0.4, "min_new_tokens": 12},
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{"presence_penalty": 0.8, "min_new_tokens": 12},
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{"presence_penalty": -0.3, "frequency_penalty": 1.3, "min_new_tokens": 32},
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{"presence_penalty": 0.3, "frequency_penalty": -1.3, "min_new_tokens": 32},
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]
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random.shuffle(args * 5)
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with ThreadPoolExecutor(8) as executor:
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list(executor.map(self.run_decode, args))
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class TestEAGLERetract(TestEAGLEServer):
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@classmethod
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def setUpClass(cls):
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# These config helps find a leak.
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os.environ["SGLANG_CI_SMALL_KV_SIZE"] = "4500"
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.process = popen_launch_server(
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DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=[
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"--speculative-algorithm",
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"EAGLE",
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"--speculative-draft-model-path",
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DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
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"--speculative-num-steps",
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5,
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"--speculative-eagle-topk",
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8,
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"--speculative-num-draft-tokens",
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64,
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|
"--mem-fraction-static",
|
|
0.7,
|
|
"--chunked-prefill-size",
|
|
128,
|
|
"--max-running-requests",
|
|
64,
|
|
],
|
|
)
|
|
|
|
|
|
class TestEAGLEServerTriton(TestEAGLEServer):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.base_url = DEFAULT_URL_FOR_TEST
|
|
cls.process = popen_launch_server(
|
|
DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
|
|
cls.base_url,
|
|
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
|
other_args=[
|
|
"--speculative-algorithm",
|
|
"EAGLE",
|
|
"--speculative-draft-model-path",
|
|
DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
|
|
"--speculative-num-steps",
|
|
5,
|
|
"--speculative-eagle-topk",
|
|
8,
|
|
"--speculative-num-draft-tokens",
|
|
64,
|
|
"--mem-fraction-static",
|
|
0.7,
|
|
"--attention-backend",
|
|
"triton",
|
|
"--max-running-requests",
|
|
8,
|
|
],
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|