Split the eagle test into two files (#7170)
This commit is contained in:
Lianmin Zheng
433
test/srt/test_eagle_infer_b.py
Normal file
433
test/srt/test_eagle_infer_b.py
Normal file
@@ -0,0 +1,433 @@
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
import threading
|
||||
import time
|
||||
import unittest
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from functools import partial
|
||||
from types import SimpleNamespace
|
||||
|
||||
import numpy as np
|
||||
import requests
|
||||
import torch
|
||||
|
||||
from sglang.srt.utils import kill_process_tree
|
||||
from sglang.test.few_shot_gsm8k import run_eval
|
||||
from sglang.test.test_utils import (
|
||||
DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
|
||||
DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
|
||||
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
DEFAULT_URL_FOR_TEST,
|
||||
CustomTestCase,
|
||||
popen_launch_server,
|
||||
run_logprob_check,
|
||||
)
|
||||
|
||||
torch_dtype = torch.float16
|
||||
prefill_tolerance = 5e-2
|
||||
decode_tolerance: float = 5e-2
|
||||
|
||||
|
||||
class TestEAGLEServer(CustomTestCase):
|
||||
PROMPTS = [
|
||||
"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nToday is a sunny day and I like[/INST]"
|
||||
'[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]',
|
||||
"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nSummarize Russell Brunson's Perfect Webinar Script...[/INST]",
|
||||
"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nwho are you?[/INST]",
|
||||
"[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nwhere are you from?[/INST]",
|
||||
]
|
||||
|
||||
@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,
|
||||
"--chunked-prefill-size",
|
||||
128,
|
||||
"--max-running-requests",
|
||||
8,
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
kill_process_tree(cls.process.pid)
|
||||
|
||||
def send_request(self):
|
||||
time.sleep(random.uniform(0, 2))
|
||||
for prompt in self.PROMPTS:
|
||||
url = self.base_url + "/generate"
|
||||
data = {
|
||||
"text": prompt,
|
||||
"sampling_params": {
|
||||
"temperature": 0,
|
||||
"max_new_tokens": 1024,
|
||||
},
|
||||
}
|
||||
response = requests.post(url, json=data)
|
||||
assert response.status_code == 200
|
||||
|
||||
def send_requests_abort(self):
|
||||
for prompt in self.PROMPTS:
|
||||
try:
|
||||
time.sleep(random.uniform(0, 2))
|
||||
url = self.base_url + "/generate"
|
||||
data = {
|
||||
"model": "base",
|
||||
"text": prompt,
|
||||
"sampling_params": {
|
||||
"temperature": 0,
|
||||
"max_new_tokens": 1024,
|
||||
},
|
||||
}
|
||||
# set timeout = 1s, mock disconnected
|
||||
requests.post(url, json=data, timeout=1)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
|
||||
def test_request_abort(self):
|
||||
concurrency = 4
|
||||
threads = [
|
||||
threading.Thread(target=self.send_request) for _ in range(concurrency)
|
||||
] + [
|
||||
threading.Thread(target=self.send_requests_abort)
|
||||
for _ in range(concurrency)
|
||||
]
|
||||
for worker in threads:
|
||||
worker.start()
|
||||
for p in threads:
|
||||
p.join()
|
||||
|
||||
def test_max_token_one(self):
|
||||
requests.get(self.base_url + "/flush_cache")
|
||||
|
||||
args = SimpleNamespace(
|
||||
num_shots=5,
|
||||
data_path=None,
|
||||
num_questions=200,
|
||||
max_new_tokens=1,
|
||||
parallel=128,
|
||||
host="http://127.0.0.1",
|
||||
port=int(self.base_url.split(":")[-1]),
|
||||
)
|
||||
|
||||
# Just run and check it does not hang
|
||||
metrics = run_eval(args)
|
||||
self.assertGreater(metrics["output_throughput"], 50)
|
||||
|
||||
def test_gsm8k(self):
|
||||
requests.get(self.base_url + "/flush_cache")
|
||||
|
||||
args = SimpleNamespace(
|
||||
num_shots=5,
|
||||
data_path=None,
|
||||
num_questions=200,
|
||||
max_new_tokens=512,
|
||||
parallel=128,
|
||||
host="http://127.0.0.1",
|
||||
port=int(self.base_url.split(":")[-1]),
|
||||
)
|
||||
|
||||
metrics = run_eval(args)
|
||||
print(f"{metrics=}")
|
||||
self.assertGreater(metrics["accuracy"], 0.20)
|
||||
|
||||
server_info = requests.get(self.base_url + "/get_server_info").json()
|
||||
avg_spec_accept_length = server_info["internal_states"][0][
|
||||
"avg_spec_accept_length"
|
||||
]
|
||||
print(f"{avg_spec_accept_length=}")
|
||||
|
||||
speculative_eagle_topk = server_info["speculative_eagle_topk"]
|
||||
|
||||
if speculative_eagle_topk == 1:
|
||||
self.assertGreater(avg_spec_accept_length, 2.5)
|
||||
else:
|
||||
self.assertGreater(avg_spec_accept_length, 3.5)
|
||||
|
||||
# Wait a little bit so that the memory check happens.
|
||||
time.sleep(4)
|
||||
|
||||
def test_logprob_start_len(self):
|
||||
logprob_start_len = 4
|
||||
new_tokens = 4
|
||||
prompts = [
|
||||
"I have a very good idea on",
|
||||
"Today is a sunndy day and",
|
||||
]
|
||||
|
||||
response = requests.post(
|
||||
self.base_url + "/generate",
|
||||
json={
|
||||
"text": prompts,
|
||||
"sampling_params": {
|
||||
"temperature": 0,
|
||||
"max_new_tokens": new_tokens,
|
||||
},
|
||||
"return_logprob": True,
|
||||
"top_logprobs_num": 5,
|
||||
"logprob_start_len": logprob_start_len,
|
||||
},
|
||||
)
|
||||
response_json = response.json()
|
||||
print(json.dumps(response_json, indent=2))
|
||||
|
||||
for res in response_json:
|
||||
self.assertEqual(
|
||||
res["meta_info"]["prompt_tokens"],
|
||||
logprob_start_len + len(res["meta_info"]["input_token_logprobs"]),
|
||||
)
|
||||
|
||||
self.assertEqual(res["meta_info"]["completion_tokens"], new_tokens)
|
||||
self.assertEqual(len(res["meta_info"]["output_token_logprobs"]), new_tokens)
|
||||
|
||||
def test_logprob_match(self):
|
||||
"""Test the output logprobs are close to the input logprobs if we run a prefill again."""
|
||||
|
||||
def run_generate(
|
||||
prompt, return_logprob=False, max_new_tokens=512, logprob_start_len=-1
|
||||
):
|
||||
|
||||
if isinstance(prompt, str):
|
||||
prompt_kwargs = {"text": prompt}
|
||||
else:
|
||||
prompt_kwargs = {"input_ids": prompt}
|
||||
|
||||
response = requests.post(
|
||||
self.base_url + "/generate",
|
||||
json={
|
||||
**prompt_kwargs,
|
||||
"sampling_params": {
|
||||
"temperature": 1.0,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"ignore_eos": True,
|
||||
},
|
||||
"return_logprob": return_logprob,
|
||||
"return_text_in_logprobs": True,
|
||||
"logprob_start_len": logprob_start_len,
|
||||
},
|
||||
)
|
||||
return response.json()
|
||||
|
||||
prompt = "I have a very good idea on how to"
|
||||
|
||||
gen = run_generate(prompt, return_logprob=True, logprob_start_len=0)
|
||||
output_logprobs = np.array(
|
||||
[x[0] for x in gen["meta_info"]["output_token_logprobs"]]
|
||||
)
|
||||
num_prompts_tokens = gen["meta_info"]["prompt_tokens"]
|
||||
|
||||
input_tokens = [x[1] for x in gen["meta_info"]["input_token_logprobs"]]
|
||||
output_tokens = [x[1] for x in gen["meta_info"]["output_token_logprobs"]]
|
||||
|
||||
new_prompt = input_tokens + output_tokens
|
||||
score = run_generate(
|
||||
new_prompt, return_logprob=True, logprob_start_len=0, max_new_tokens=0
|
||||
)
|
||||
output_logprobs_score = np.array(
|
||||
[
|
||||
x[0]
|
||||
for x in score["meta_info"]["input_token_logprobs"][num_prompts_tokens:]
|
||||
]
|
||||
)
|
||||
|
||||
print(f"{output_logprobs[-10:]=}")
|
||||
print(f"{output_logprobs_score[-10:]=}")
|
||||
|
||||
diff = np.abs(output_logprobs - output_logprobs_score)
|
||||
max_diff = np.max(diff)
|
||||
self.assertLess(max_diff, 0.25)
|
||||
|
||||
def test_logprob_mixed(self):
|
||||
args = []
|
||||
temperature = 0
|
||||
# input_len, output_len, temperature, logprob_start_len, return_logprob, top_logprobs_num
|
||||
# Llama 2 context length seems to be only 2k, so we can only test small length.
|
||||
for input_len in [200, 500, 1000, 2000]:
|
||||
for output_len in [4, 8]:
|
||||
for logprob_start_len in [0, 100, 300, 800, 1998]:
|
||||
for return_logprob in [True, False]:
|
||||
for top_logprobs_num in [0, 5]:
|
||||
|
||||
if logprob_start_len >= input_len:
|
||||
continue
|
||||
|
||||
args.append(
|
||||
(
|
||||
input_len,
|
||||
output_len,
|
||||
temperature,
|
||||
logprob_start_len,
|
||||
return_logprob,
|
||||
top_logprobs_num,
|
||||
)
|
||||
)
|
||||
|
||||
random.shuffle(args)
|
||||
|
||||
func = partial(run_logprob_check, self)
|
||||
with ThreadPoolExecutor(8) as executor:
|
||||
list(executor.map(func, args))
|
||||
|
||||
def run_decode(self, sampling_params):
|
||||
return_logprob = True
|
||||
top_logprobs_num = 5
|
||||
return_text = True
|
||||
n = 1
|
||||
|
||||
response = requests.post(
|
||||
self.base_url + "/generate",
|
||||
json={
|
||||
"text": "Human: Write a travel blog post to Hawaii.\n\nAssistant:",
|
||||
"sampling_params": {
|
||||
"max_new_tokens": 48,
|
||||
"n": n,
|
||||
"temperature": 0.7,
|
||||
**sampling_params,
|
||||
},
|
||||
"return_logprob": return_logprob,
|
||||
"top_logprobs_num": top_logprobs_num,
|
||||
"return_text_in_logprobs": return_text,
|
||||
"logprob_start_len": 0,
|
||||
},
|
||||
)
|
||||
self.assertEqual(response.status_code, 200)
|
||||
print(json.dumps(response.json()))
|
||||
print("=" * 100)
|
||||
|
||||
def test_penalty_mixed(self):
|
||||
args = [
|
||||
{},
|
||||
{},
|
||||
{},
|
||||
{"frequency_penalty": 2},
|
||||
{"presence_penalty": 1},
|
||||
{"min_new_tokens": 16},
|
||||
{"frequency_penalty": 0.2},
|
||||
{"presence_penalty": 0.4},
|
||||
{"min_new_tokens": 8},
|
||||
{"frequency_penalty": 0.4, "presence_penalty": 0.8},
|
||||
{"frequency_penalty": 0.4, "min_new_tokens": 12},
|
||||
{"presence_penalty": 0.8, "min_new_tokens": 12},
|
||||
{"presence_penalty": -0.3, "frequency_penalty": 1.3, "min_new_tokens": 32},
|
||||
{"presence_penalty": 0.3, "frequency_penalty": -1.3, "min_new_tokens": 32},
|
||||
]
|
||||
random.shuffle(args * 5)
|
||||
with ThreadPoolExecutor(8) as executor:
|
||||
list(executor.map(self.run_decode, args))
|
||||
|
||||
def test_constrained_decoding(self):
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Give me a json"},
|
||||
]
|
||||
|
||||
response = requests.post(
|
||||
self.base_url + "/v1/chat/completions",
|
||||
json={
|
||||
"model": DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
|
||||
"messages": messages,
|
||||
"temperature": 0,
|
||||
"response_format": {"type": "json_object"},
|
||||
},
|
||||
)
|
||||
self.assertEqual(response.status_code, 200)
|
||||
res = response.json()
|
||||
|
||||
# Validate response structure
|
||||
self.assertIn("choices", res)
|
||||
self.assertEqual(len(res["choices"]), 1)
|
||||
self.assertIn("message", res["choices"][0])
|
||||
self.assertIn("content", res["choices"][0]["message"])
|
||||
|
||||
# Validate JSON content
|
||||
content_json = res["choices"][0]["message"]["content"]
|
||||
is_valid_json = True
|
||||
try:
|
||||
content = json.loads(content_json)
|
||||
self.assertIsInstance(content, dict)
|
||||
except Exception:
|
||||
print(f"parse JSON failed: {content_json}")
|
||||
is_valid_json = False
|
||||
self.assertTrue(is_valid_json)
|
||||
|
||||
|
||||
class TestEAGLERetract(TestEAGLEServer):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
# These config helps find a leak.
|
||||
os.environ["SGLANG_CI_SMALL_KV_SIZE"] = "4500"
|
||||
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,
|
||||
"--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()
|
||||
Reference in New Issue
Block a user