330 lines
10 KiB
Python
330 lines
10 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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import torch
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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_MODEL_NAME_FOR_TEST,
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3,
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DEFAULT_MODEL_NAME_FOR_TEST_LOCAL_ATTENTION,
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DEFAULT_MODEL_NAME_FOR_TEST_MLA,
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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popen_launch_server,
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)
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GSM_DATASET_PATH = None
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# In case of some machine lack internet connection, we can set OFFLINE_MODE to True.
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OFFLINE_MODE = False
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# Change the path below when OFFLINE_MODE is True.
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OFFLINE_PATH_DICT = {
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DEFAULT_MODEL_NAME_FOR_TEST: "/shared/public/elr-models/meta-llama/Meta-Llama-3.1-8B-Instruct",
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3: "/shared/public/elr-models/jamesliu1/sglang-EAGLE3-Llama-3.1-Instruct-8B",
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DEFAULT_MODEL_NAME_FOR_TEST_MLA: "/shared/public/sharing/deepseek/dsv3-test/snapshots/",
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN: "/shared/public/sharing/deepseek/dsv3-test-NextN/snapshots/",
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GSM_DATASET_PATH: "/shared/public/data/gsm8k/test.jsonl",
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}
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if OFFLINE_MODE:
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DEFAULT_MODEL_NAME_FOR_TEST = OFFLINE_PATH_DICT[DEFAULT_MODEL_NAME_FOR_TEST]
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3 = OFFLINE_PATH_DICT[
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3
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]
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DEFAULT_MODEL_NAME_FOR_TEST_MLA = OFFLINE_PATH_DICT[DEFAULT_MODEL_NAME_FOR_TEST_MLA]
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN = OFFLINE_PATH_DICT[
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN
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]
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GSM_DATASET_PATH = OFFLINE_PATH_DICT[GSM_DATASET_PATH]
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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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"--enable-torch-compile",
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"--cuda-graph-max-bs",
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"2",
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"--attention-backend",
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"fa3",
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]
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"""
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Integration test for python/sglang/srt/layers/attention/flashattention_backend.py
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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 BaseFlashAttentionTest(unittest.TestCase):
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"""Base class for testing FlashAttention3."""
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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 = 1.0 # 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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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=cls.get_server_args(),
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env=os.environ,
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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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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(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()["avg_spec_accept_length"]
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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 TestFlashAttention3MLA(BaseFlashAttentionTest):
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"""Test FlashAttention3 with MLA, e.g. deepseek v3 test model"""
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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 TestFlashAttention3LocalAttn(BaseFlashAttentionTest):
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"""Test FlashAttention3 with Model with local attention, e.g. Llama 4."""
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accuracy_threshold = 0.70
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model = DEFAULT_MODEL_NAME_FOR_TEST_LOCAL_ATTENTION
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@classmethod
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def get_server_args(cls):
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cloned_args = DEFAULT_SERVER_ARGS.copy()
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# remove --enable-torch-compile from cloned_args since llama4 does not support it for now
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cloned_args.remove("--enable-torch-compile")
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# we cannot use scout's 10m context due to this bug: https://github.com/sgl-project/sglang/issues/5755
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cloned_args.extend(["--tp", "4", "--context-length", "1000000"])
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return cloned_args
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class TestFlashAttention3SpeculativeDecode(BaseFlashAttentionTest):
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"""Test FlashAttention3 with speculative decode enabled with Llama 3.1 8B and its eagle3 model"""
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model = DEFAULT_MODEL_NAME_FOR_TEST
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accuracy_threshold = 0.65
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speculative_decode = True
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spec_decode_threshold = 1.5
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@classmethod
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def get_server_args(cls):
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args = DEFAULT_SERVER_ARGS
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args.extend(
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[
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"--cuda-graph-max-bs",
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"2",
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"--speculative-algorithm",
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"EAGLE3",
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"--speculative-draft",
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3,
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"--speculative-num-steps",
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"3",
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"--speculative-eagle-topk",
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"1",
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"--speculative-num-draft-tokens",
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"4",
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"--dtype",
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"float16",
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]
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)
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return args
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class TestFlashAttention3SpeculativeDecodeTopk(BaseFlashAttentionTest):
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"""Tests FlashAttention3 with enhanced speculative decoding using Llama 3.1 8B and EAGLE3.
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This test will be using top-k value > 1 which would verify the other branches of the FA3 code
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"""
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model = DEFAULT_MODEL_NAME_FOR_TEST
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accuracy_threshold = 0.65
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speculative_decode = True
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spec_decode_threshold = 1.5
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@classmethod
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def get_server_args(cls):
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args = DEFAULT_SERVER_ARGS
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args.extend(
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[
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"--cuda-graph-max-bs",
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"2",
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"--speculative-algorithm",
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"EAGLE3",
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"--speculative-draft",
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3,
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"--speculative-num-steps",
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"5",
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"--speculative-eagle-topk",
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"4",
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"--speculative-num-draft-tokens",
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"8",
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"--dtype",
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"float16",
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]
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)
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return args
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class TestFlashAttention3SpeculativeDecodeTopk(BaseFlashAttentionTest):
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"""Test FlashAttention3 with speculative decode enabled, topk > 1"""
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model = DEFAULT_MODEL_NAME_FOR_TEST
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@classmethod
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def get_server_args(cls):
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args = super().get_server_args()
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args.extend(
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[
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"--cuda-graph-max-bs",
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"2",
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"--speculative-algorithm",
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"EAGLE3",
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"--speculative-draft",
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DEFAULT_MODEL_NAME_FOR_TEST_EAGLE3,
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"--speculative-num-steps",
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"5",
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"--speculative-eagle-topk",
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"4",
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"--speculative-num-draft-tokens",
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"8",
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"--dtype",
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"float16",
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]
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)
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return args
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def test_gsm8k(self):
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"""
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Override the test_gsm8k to further test for average speculative accept length.
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"""
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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=GSM_DATASET_PATH,
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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_eval_few_shot_gsm8k(args)
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print(metrics)
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self.assertGreater(metrics["accuracy"], 0.60)
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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()["avg_spec_accept_length"]
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print(f"{avg_spec_accept_length=}")
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self.assertGreater(avg_spec_accept_length, 1.8)
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class TestFlashAttention3MLASpeculativeDecode(BaseFlashAttentionTest):
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"""Test FlashAttention3 with speculative decode enabled with deepseek v3 test model and its nextN model"""
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model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
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accuracy_threshold = 0.60
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speculative_decode = True
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spec_decode_threshold = 1.5
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@classmethod
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def get_server_args(cls):
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args = DEFAULT_SERVER_ARGS
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args.extend(
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[
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"--cuda-graph-max-bs",
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"2",
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"--speculative-algorithm",
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"EAGLE",
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"--speculative-draft",
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN,
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"--speculative-num-steps",
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"3",
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"--speculative-eagle-topk",
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"1",
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"--speculative-num-draft-tokens",
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"4",
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]
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)
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return args
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class TestFlashAttention3MLASpeculativeDecodeTopk(BaseFlashAttentionTest):
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"""Test FlashAttention3 with speculative decode enabled with deepseek v3 test model and its nextN model
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This test will be using top-k value > 1 which would verify the other branches of the FA3 code
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"""
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model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
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accuracy_threshold = 0.60
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speculative_decode = True
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spec_decode_threshold = 1.5
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@classmethod
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def get_server_args(cls):
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args = DEFAULT_SERVER_ARGS
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args.extend(
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[
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"--cuda-graph-max-bs",
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"2",
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"--speculative-algorithm",
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"EAGLE",
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"--speculative-draft",
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN,
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"--speculative-num-steps",
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"5",
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"--speculative-eagle-topk",
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"4",
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"--speculative-num-draft-tokens",
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"8",
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]
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)
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return args
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if __name__ == "__main__":
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unittest.main()
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