162 lines
4.8 KiB
Python
162 lines
4.8 KiB
Python
import os
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import unittest
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from types import SimpleNamespace
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import requests
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from sglang.srt.utils import get_device_sm, kill_process_tree
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from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
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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_MODEL_NAME_FOR_TEST,
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DEFAULT_MODEL_NAME_FOR_TEST_MLA,
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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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GSM_DATASET_PATH = None
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# Default server arguments shared across all tests
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DEFAULT_SERVER_ARGS = [
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"--trust-remote-code",
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"--cuda-graph-max-bs",
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"8",
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"--prefill-attention-backend",
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"fa3",
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"--decode-attention-backend",
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"flashinfer",
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]
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@unittest.skipIf(get_device_sm() < 90, "Test requires CUDA SM 90 or higher")
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class TestHybridAttnBackendBase(CustomTestCase):
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model = DEFAULT_MODEL_NAME_FOR_TEST
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base_url = DEFAULT_URL_FOR_TEST
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accuracy_threshold = 0.65 # derived tests need to override this
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speculative_decode = False
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spec_decode_threshold = 2.2 # derived spec decoding tests need to override this
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@classmethod
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def get_server_args(cls):
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"""Return the arguments for the server launch. Override in subclasses."""
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return DEFAULT_SERVER_ARGS
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@classmethod
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def setUpClass(cls):
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# disable deep gemm precompile to make launch server faster
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# please don't do this if you want to make your inference workload faster
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os.environ["SGL_JIT_DEEPGEMM_PRECOMPILE"] = "false"
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os.environ["SGL_ENABLE_JIT_DEEPGEMM"] = "false"
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if cls.speculative_decode:
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model = DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST
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else:
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model = cls.model
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cls.process = popen_launch_server(
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model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=cls.get_server_args(),
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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 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=4,
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num_questions=100,
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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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data_path=GSM_DATASET_PATH,
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)
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metrics = run_eval_few_shot_gsm8k(args)
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print(f"{metrics=}")
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# Use the appropriate metric key based on the test class
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metric_key = "accuracy"
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self.assertGreater(metrics[metric_key], self.accuracy_threshold)
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if self.speculative_decode:
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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()["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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self.assertGreater(avg_spec_accept_length, self.spec_decode_threshold)
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class TestHybridAttnBackendMLA(TestHybridAttnBackendBase):
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accuracy_threshold = 0.60
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model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
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@classmethod
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def get_server_args(cls):
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return DEFAULT_SERVER_ARGS
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class TestHybridAttnBackendTorchCompile(TestHybridAttnBackendBase):
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accuracy_threshold = 0.65
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@classmethod
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def get_server_args(cls):
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return DEFAULT_SERVER_ARGS + ["--enable-torch-compile"]
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class TestHybridAttnBackendSpeculativeDecodingPrefillBackend(TestHybridAttnBackendBase):
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speculative_decode = True
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# This eagle test uses a very small model, so the accuracy is low.
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accuracy_threshold = 0.2
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@classmethod
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def get_server_args(cls):
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return DEFAULT_SERVER_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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"3",
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"--speculative-eagle-topk",
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"2",
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"--speculative-num-draft-tokens",
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"4",
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"--speculative-attention-mode",
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"prefill",
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]
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class TestHybridAttnBackendSpeculativeDecodingDecodeBackend(TestHybridAttnBackendBase):
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speculative_decode = True
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# This eagle test uses a very small model, so the accuracy is low.
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accuracy_threshold = 0.2
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@classmethod
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def get_server_args(cls):
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return DEFAULT_SERVER_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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"3",
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"--speculative-eagle-topk",
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"2",
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"--speculative-num-draft-tokens",
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"4",
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"--speculative-attention-mode",
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"decode",
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]
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if __name__ == "__main__":
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unittest.main()
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