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sglang/test/srt/test_eagle_infer.py

69 lines
2.2 KiB
Python

import unittest
from transformers import AutoConfig, AutoTokenizer
import sglang as sgl
class TestEAGLEEngine(unittest.TestCase):
def test_eagle_accuracy(self):
prompt = "Today is a sunny day and I like"
target_model_path = "meta-llama/Llama-2-7b-chat-hf"
speculative_draft_model_path = "lmzheng/sglang-EAGLE-llama2-chat-7B"
sampling_params = {"temperature": 0, "max_new_tokens": 8}
engine = sgl.Engine(
model_path=target_model_path,
speculative_draft_model_path=speculative_draft_model_path,
speculative_algorithm="EAGLE",
speculative_num_steps=3,
speculative_eagle_topk=4,
speculative_num_draft_tokens=16,
)
out1 = engine.generate(prompt, sampling_params)["text"]
engine.shutdown()
engine = sgl.Engine(model_path=target_model_path)
out2 = engine.generate(prompt, sampling_params)["text"]
engine.shutdown()
print("==== Answer 1 ====")
print(out1)
print("==== Answer 2 ====")
print(out2)
self.assertEqual(out1, out2)
def test_eagle_end_check(self):
prompt = "[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nToday is a sunny day and I like [/INST]"
target_model_path = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(target_model_path)
speculative_draft_model_path = "lmzheng/sglang-EAGLE-llama2-chat-7B"
sampling_params = {
"temperature": 0,
"max_new_tokens": 1024,
"skip_special_tokens": False,
}
engine = sgl.Engine(
model_path=target_model_path,
speculative_draft_model_path=speculative_draft_model_path,
speculative_algorithm="EAGLE",
speculative_num_steps=3,
speculative_eagle_topk=4,
speculative_num_draft_tokens=16,
)
out1 = engine.generate(prompt, sampling_params)["text"]
engine.shutdown()
print("==== Answer 1 ====")
print(repr(out1))
tokens = tokenizer.encode(out1, truncation=False)
assert tokenizer.eos_token_id not in tokens
if __name__ == "__main__":
unittest.main()