adapt to sglang v0.5.2rc1 on dcu
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maxiao
246
test/srt/test_skip_tokenizer_init.py
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246
test/srt/test_skip_tokenizer_init.py
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"""
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python3 -m unittest test_skip_tokenizer_init.TestSkipTokenizerInit.test_parallel_sample
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python3 -m unittest test_skip_tokenizer_init.TestSkipTokenizerInit.run_decode_stream
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"""
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import json
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import unittest
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from io import BytesIO
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import requests
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from PIL import Image
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from transformers import AutoProcessor, AutoTokenizer
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from sglang.lang.chat_template import get_chat_template_by_model_path
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from sglang.srt.utils import kill_process_tree
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from sglang.test.test_utils import (
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DEFAULT_IMAGE_URL,
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DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
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DEFAULT_SMALL_VLM_MODEL_NAME_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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)
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class TestSkipTokenizerInit(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=["--skip-tokenizer-init", "--stream-output"],
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)
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cls.eos_token_id = [119690]
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cls.tokenizer = AutoTokenizer.from_pretrained(
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DEFAULT_SMALL_MODEL_NAME_FOR_TEST, use_fast=False
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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 run_decode(
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self,
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prompt_text="The capital of France is",
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max_new_tokens=32,
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return_logprob=False,
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top_logprobs_num=0,
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n=1,
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):
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input_ids = self.get_input_ids(prompt_text)
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request = self.get_request_json(
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input_ids=input_ids,
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return_logprob=return_logprob,
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top_logprobs_num=top_logprobs_num,
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max_new_tokens=max_new_tokens,
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stream=False,
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n=n,
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)
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response = requests.post(
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self.base_url + "/generate",
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json=request,
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)
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ret = response.json()
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print(json.dumps(ret, indent=2))
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def assert_one_item(item):
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if item["meta_info"]["finish_reason"]["type"] == "stop":
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self.assertEqual(
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item["meta_info"]["finish_reason"]["matched"],
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self.tokenizer.eos_token_id,
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)
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elif item["meta_info"]["finish_reason"]["type"] == "length":
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self.assertEqual(
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len(item["output_ids"]), item["meta_info"]["completion_tokens"]
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)
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self.assertEqual(len(item["output_ids"]), max_new_tokens)
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self.assertEqual(item["meta_info"]["prompt_tokens"], len(input_ids))
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if return_logprob:
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num_input_logprobs = len(input_ids) - request["logprob_start_len"]
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if num_input_logprobs > len(input_ids):
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num_input_logprobs -= len(input_ids)
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self.assertEqual(
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len(item["meta_info"]["input_token_logprobs"]),
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num_input_logprobs,
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f'{len(item["meta_info"]["input_token_logprobs"])} mismatch with {len(input_ids)}',
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)
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self.assertEqual(
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len(item["meta_info"]["output_token_logprobs"]),
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max_new_tokens,
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)
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# Determine whether to assert a single item or multiple items based on n
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if n == 1:
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assert_one_item(ret)
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else:
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self.assertEqual(len(ret), n)
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for i in range(n):
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assert_one_item(ret[i])
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print("=" * 100)
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def run_decode_stream(self, return_logprob=False, top_logprobs_num=0, n=1):
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max_new_tokens = 32
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input_ids = self.get_input_ids("The capital of France is")
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requests.post(self.base_url + "/flush_cache")
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response = requests.post(
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self.base_url + "/generate",
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json=self.get_request_json(
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input_ids=input_ids,
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max_new_tokens=max_new_tokens,
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return_logprob=return_logprob,
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top_logprobs_num=top_logprobs_num,
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stream=False,
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n=n,
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),
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)
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ret = response.json()
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print(json.dumps(ret))
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output_ids = ret["output_ids"]
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print("output from non-streaming request:")
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print(output_ids)
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print(self.tokenizer.decode(output_ids, skip_special_tokens=True))
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requests.post(self.base_url + "/flush_cache")
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response_stream = requests.post(
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self.base_url + "/generate",
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json=self.get_request_json(
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input_ids=input_ids,
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return_logprob=return_logprob,
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top_logprobs_num=top_logprobs_num,
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stream=True,
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n=n,
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),
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)
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response_stream_json = []
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for line in response_stream.iter_lines():
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print(line)
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if line.startswith(b"data: ") and line[6:] != b"[DONE]":
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response_stream_json.append(json.loads(line[6:]))
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out_stream_ids = []
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for x in response_stream_json:
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out_stream_ids += x["output_ids"]
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print("output from streaming request:")
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print(out_stream_ids)
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print(self.tokenizer.decode(out_stream_ids, skip_special_tokens=True))
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assert output_ids == out_stream_ids
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def test_simple_decode(self):
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self.run_decode()
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def test_parallel_sample(self):
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self.run_decode(n=3)
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def test_logprob(self):
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for top_logprobs_num in [0, 3]:
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self.run_decode(return_logprob=True, top_logprobs_num=top_logprobs_num)
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def test_eos_behavior(self):
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self.run_decode(max_new_tokens=256)
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def test_simple_decode_stream(self):
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self.run_decode_stream()
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def get_input_ids(self, prompt_text) -> list[int]:
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input_ids = self.tokenizer(prompt_text, return_tensors="pt")["input_ids"][
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0
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].tolist()
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return input_ids
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def get_request_json(
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self,
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input_ids,
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max_new_tokens=32,
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return_logprob=False,
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top_logprobs_num=0,
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stream=False,
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n=1,
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):
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return {
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"input_ids": input_ids,
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"sampling_params": {
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"temperature": 0 if n == 1 else 0.5,
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"max_new_tokens": max_new_tokens,
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"n": n,
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"stop_token_ids": self.eos_token_id,
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},
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"stream": stream,
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"return_logprob": return_logprob,
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"top_logprobs_num": top_logprobs_num,
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"logprob_start_len": 0,
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}
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class TestSkipTokenizerInitVLM(TestSkipTokenizerInit):
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@classmethod
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def setUpClass(cls):
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cls.image_url = DEFAULT_IMAGE_URL
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response = requests.get(cls.image_url)
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cls.image = Image.open(BytesIO(response.content))
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cls.model = DEFAULT_SMALL_VLM_MODEL_NAME_FOR_TEST
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cls.tokenizer = AutoTokenizer.from_pretrained(cls.model, use_fast=False)
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cls.processor = AutoProcessor.from_pretrained(cls.model, trust_remote_code=True)
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=["--skip-tokenizer-init"],
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)
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cls.eos_token_id = [cls.tokenizer.eos_token_id]
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def get_input_ids(self, _prompt_text) -> list[int]:
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chat_template = get_chat_template_by_model_path(self.model)
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text = f"{chat_template.image_token}What is in this picture?"
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inputs = self.processor(
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text=[text],
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images=[self.image],
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return_tensors="pt",
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)
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return inputs.input_ids[0].tolist()
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def get_request_json(self, *args, **kwargs):
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ret = super().get_request_json(*args, **kwargs)
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ret["image_data"] = [self.image_url]
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ret["logprob_start_len"] = (
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-1
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) # Do not try to calculate logprobs of image embeddings.
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return ret
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def test_simple_decode_stream(self):
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# TODO mick
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pass
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if __name__ == "__main__":
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unittest.main()
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