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178 行
5.5 KiB
Python
178 行
5.5 KiB
Python
import os
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import shutil
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import unittest
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from types import SimpleNamespace
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from sglang.srt.utils import kill_process_tree
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.run_eval import run_eval
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from sglang.test.server_fixtures.disaggregation_fixture import (
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PDDisaggregationServerBase,
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)
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from sglang.test.test_utils import (
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DEFAULT_MODEL_NAME_FOR_TEST,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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popen_launch_pd_server,
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)
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# Registering the test for CUDA CI with appropriate parameters
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# Increasing estimated time since we run evaluation twice
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register_cuda_ci(
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est_time=600,
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stage="base-b",
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runner_config="2-gpu-large",
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)
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class TestDisaggregationDecodeOffload(PDDisaggregationServerBase):
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"""
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Test class for verifying KV cache offloading on the decode side in a
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prefill-decode disaggregation setup.
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"""
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@classmethod
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def setUpClass(cls):
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# Set environment variable to make offloading more frequent for testing purposes
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cls.old_stride = os.environ.get("SGLANG_HICACHE_DECODE_OFFLOAD_STRIDE")
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cls.hicache_dir = "/tmp/hicache_test"
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os.environ["SGLANG_HICACHE_FILE_BACKEND_STORAGE_DIR"] = cls.hicache_dir
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os.environ["SGLANG_HICACHE_DECODE_OFFLOAD_STRIDE"] = "16"
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# Ensure a clean cache directory
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if os.path.exists(cls.hicache_dir):
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shutil.rmtree(cls.hicache_dir)
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os.makedirs(cls.hicache_dir, exist_ok=True)
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super().setUpClass()
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cls.model = DEFAULT_MODEL_NAME_FOR_TEST
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# Non-blocking start of prefill and decode servers
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cls.start_prefill()
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cls.start_decode()
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# Wait for both servers to be ready before proceeding
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cls.wait_server_ready(cls.prefill_url + "/health")
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cls.wait_server_ready(cls.decode_url + "/health")
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cls.launch_lb()
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@classmethod
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def tearDownClass(cls):
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# Restore the original environment variable state
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super().tearDownClass()
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if cls.old_stride is not None:
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os.environ["SGLANG_HICACHE_DECODE_OFFLOAD_STRIDE"] = cls.old_stride
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else:
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os.environ.pop("SGLANG_HICACHE_DECODE_OFFLOAD_STRIDE", None)
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os.environ.pop("SGLANG_HICACHE_FILE_BACKEND_STORAGE_DIR", None)
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# Clean up the cache directory
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if os.path.exists(cls.hicache_dir):
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shutil.rmtree(cls.hicache_dir)
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@classmethod
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def start_prefill(cls):
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prefill_args = [
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"--trust-remote-code",
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"--disaggregation-mode",
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"prefill",
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"--disaggregation-bootstrap-port",
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cls.bootstrap_port,
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"--tp",
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"1",
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"--page-size",
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"16",
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"--enable-hierarchical-cache",
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"--hicache-storage-backend",
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"file",
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"--hicache-ratio",
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"2",
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]
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prefill_args += cls.transfer_backend + cls.rdma_devices
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cls.process_prefill = popen_launch_pd_server(
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cls.model,
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cls.prefill_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=prefill_args,
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)
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@classmethod
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def start_decode(cls):
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decode_args = [
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"--trust-remote-code",
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"--disaggregation-mode",
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"decode",
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"--disaggregation-bootstrap-port",
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cls.bootstrap_port,
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"--tp",
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"1",
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"--base-gpu-id",
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"1",
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"--disaggregation-decode-enable-offload-kvcache",
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"--num-reserved-decode-tokens",
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"128",
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"--hicache-ratio",
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"2",
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"--page-size",
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"16",
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"--hicache-storage-backend",
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"file",
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]
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decode_args += cls.transfer_backend + cls.rdma_devices
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cls.process_decode = popen_launch_pd_server(
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cls.model,
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cls.decode_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=decode_args,
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)
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def test_mmlu_double_eval(self):
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"""
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Run two rounds of MMLU evaluation:
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1. First round: Decode node offloads KV cache back to disk (HiCache).
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2. Restart All Nodes to clear memory cache.
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3. Second round: Prefill node loads KV cache from disk (HiCache).
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Verify that both rounds produce consistent scores.
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"""
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args = SimpleNamespace(
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base_url=f"http://{self.base_host}:{self.lb_port}",
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model=self.model,
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eval_name="mmlu",
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num_examples=64,
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num_threads=32,
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)
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metrics1 = run_eval(args)
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# Ensure all offloads are committed to disk
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import time
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time.sleep(10)
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kill_process_tree(self.process_prefill.pid)
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kill_process_tree(self.process_decode.pid)
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kill_process_tree(self.process_lb.pid)
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self.process_prefill.wait()
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self.process_decode.wait()
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self.process_lb.wait()
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self.start_prefill()
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self.start_decode()
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self.launch_lb()
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self.wait_server_ready(self.prefill_url + "/health")
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self.wait_server_ready(self.decode_url + "/health")
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metrics2 = run_eval(args)
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# Assert score is above a minimum threshold for both rounds
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self.assertGreater(metrics1["score"], 0.65)
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self.assertGreater(metrics2["score"], 0.65)
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# Score should be consistent: round 2 should be >= round 1, or at least within a 0.05 margin if slightly lower
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self.assertGreaterEqual(metrics2["score"], metrics1["score"] - 0.05)
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if __name__ == "__main__":
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unittest.main()
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