331 lines
11 KiB
Python
331 lines
11 KiB
Python
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 types import SimpleNamespace
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from typing import List, Optional
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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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popen_launch_server,
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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(unittest.TestCase):
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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": 5,
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}
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NUM_CONFIGS = 3
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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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# Disable cuda graph
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{**self.BASE_CONFIG, "disable_cuda_graph": True},
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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,
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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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]
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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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self.assertGreater(acc_length, 3.6)
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class TestEAGLEEngineTokenMap(unittest.TestCase):
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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": 5,
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}
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NUM_CONFIGS = 1
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class TestEAGLEServer(unittest.TestCase):
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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_gsm8k(self):
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server_info = 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, 2.9)
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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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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",
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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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64,
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],
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)
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class TestEAGLEServerTriton(TestEAGLEServer):
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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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"--attention-backend",
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"triton",
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"--max-running-requests",
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8,
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],
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)
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if __name__ == "__main__":
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unittest.main()
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