326 lines
10 KiB
Python
326 lines
10 KiB
Python
import json
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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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import numpy as np
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import requests
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from sglang.srt.environ import envs
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.few_shot_gsm8k import run_eval as run_gsm8k_eval
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from sglang.test.server_fixtures.eagle_fixture import EagleServerBase
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from sglang.test.test_utils import (
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DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
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run_logprob_check,
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)
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register_cuda_ci(est_time=473, suite="stage-b-test-small-1-gpu")
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class TestEAGLEServerBasic(EagleServerBase):
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extra_args = ["--chunked-prefill-size", 128, "--max-running-requests", 8]
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# FIXME(lsyin): move the test methods to kits
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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_gsm8k_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_gsm8k_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").json()
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avg_spec_accept_length = server_info["internal_states"][0][
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"avg_spec_accept_length"
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]
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print(f"{avg_spec_accept_length=}")
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speculative_eagle_topk = server_info["speculative_eagle_topk"]
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if speculative_eagle_topk == 1:
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self.assertGreater(avg_spec_accept_length, 2.5)
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else:
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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,
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return_logprob=False,
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max_new_tokens=512,
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logprob_start_len=-1,
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temperature=1.0,
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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": temperature,
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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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"temp_scaled_logprobs": True,
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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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for temperature in [1.0]:
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gen = run_generate(
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prompt,
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return_logprob=True,
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logprob_start_len=0,
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temperature=temperature,
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)
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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,
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return_logprob=True,
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logprob_start_len=0,
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max_new_tokens=0,
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temperature=temperature,
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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"][
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num_prompts_tokens:
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]
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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, 0.255)
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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 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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def test_constrained_decoding(self):
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Give me a json"},
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]
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response = requests.post(
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self.base_url + "/v1/chat/completions",
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json={
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"model": DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
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"messages": messages,
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"temperature": 0,
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"response_format": {"type": "json_object"},
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},
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)
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self.assertEqual(response.status_code, 200)
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res = response.json()
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# Validate response structure
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self.assertIn("choices", res)
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self.assertEqual(len(res["choices"]), 1)
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self.assertIn("message", res["choices"][0])
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self.assertIn("content", res["choices"][0]["message"])
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# Validate JSON content
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content_json = res["choices"][0]["message"]["content"]
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is_valid_json = True
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try:
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content = json.loads(content_json)
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self.assertIsInstance(content, dict)
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except Exception:
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print(f"parse JSON failed: {content_json}")
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is_valid_json = False
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self.assertTrue(is_valid_json)
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class TestEAGLERetract(TestEAGLEServerBasic):
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extra_args = ["--chunked-prefill-size", 128, "--max-running-requests", 64]
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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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# FIXME(lsyin): use override context manager
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envs.SGLANG_CI_SMALL_KV_SIZE.set(4500)
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super().setUpClass()
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class TestEAGLEServerTriton(TestEAGLEServerBasic):
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extra_args = ["--attention-backend=triton", "--max-running-requests=8"]
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class TestEAGLEServerPageSize(TestEAGLEServerBasic):
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spec_steps = 5
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spec_topk = 1
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spec_tokens = 6
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extra_args = [
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"--chunked-prefill-size=128",
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"--max-running-requests=8",
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"--page-size=4",
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"--attention-backend=flashinfer",
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]
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class TestEAGLEServerPageSizeTopk(TestEAGLEServerBasic):
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# default topk=8 and tokens=64
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extra_args = [
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"--chunked-prefill-size=128",
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"--max-running-requests=8",
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"--page-size=4",
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"--attention-backend=flashinfer",
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]
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if __name__ == "__main__":
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unittest.main()
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