302 lines
8.4 KiB
Python
302 lines
8.4 KiB
Python
import unittest
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from types import SimpleNamespace
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from sglang.srt.environ import envs
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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_disaggregation_utils import TestDisaggregationBase
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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_MLA,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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popen_launch_pd_server,
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try_cached_model,
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)
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class TestDisaggregationMooncakePrefillLargerTP(TestDisaggregationBase):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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# Temporarily disable JIT DeepGEMM
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envs.SGLANG_ENABLE_JIT_DEEPGEMM.set(False)
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cls.model = try_cached_model(DEFAULT_MODEL_NAME_FOR_TEST_MLA)
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# Non blocking start servers
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cls.start_prefill()
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cls.start_decode()
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# Block until both
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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 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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"--tp",
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"4",
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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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"--tp",
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"2",
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"--base-gpu-id",
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"4",
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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_gsm8k(self):
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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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=f"http://{self.base_host}",
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port=int(self.lb_port),
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)
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metrics = run_eval_few_shot_gsm8k(args)
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print(f"Evaluation metrics: {metrics}")
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self.assertGreater(metrics["accuracy"], 0.60)
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class TestDisaggregationMooncakeDecodeLargerTP(TestDisaggregationBase):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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# Temporarily disable JIT DeepGEMM
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envs.SGLANG_ENABLE_JIT_DEEPGEMM.set(False)
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cls.model = try_cached_model(DEFAULT_MODEL_NAME_FOR_TEST_MLA)
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# Non blocking start servers
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cls.start_prefill()
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cls.start_decode()
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# Block until both
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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 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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"--tp",
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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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"--tp",
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"4",
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"--base-gpu-id",
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"4",
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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_gsm8k(self):
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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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=f"http://{self.base_host}",
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port=int(self.lb_port),
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)
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metrics = run_eval_few_shot_gsm8k(args)
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print(f"Evaluation metrics: {metrics}")
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self.assertGreater(metrics["accuracy"], 0.60)
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class TestDisaggregationMooncakeMHAPrefillLargerTP(TestDisaggregationBase):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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# Temporarily disable JIT DeepGEMM
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envs.SGLANG_ENABLE_JIT_DEEPGEMM.set(False)
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cls.model = try_cached_model(DEFAULT_MODEL_NAME_FOR_TEST)
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# Non blocking start servers
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cls.start_prefill()
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cls.start_decode()
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# Block until both
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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 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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"--tp",
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"4",
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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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"--tp",
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"2",
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"--base-gpu-id",
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"4",
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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_gsm8k(self):
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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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=f"http://{self.base_host}",
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port=int(self.lb_port),
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)
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metrics = run_eval_few_shot_gsm8k(args)
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print(f"Evaluation metrics: {metrics}")
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self.assertGreater(metrics["accuracy"], 0.60)
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class TestDisaggregationMooncakeMHADecodeLargerTP(TestDisaggregationBase):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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# Temporarily disable JIT DeepGEMM
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envs.SGLANG_ENABLE_JIT_DEEPGEMM.set(False)
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cls.model = try_cached_model(DEFAULT_MODEL_NAME_FOR_TEST)
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# Non blocking start servers
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cls.start_prefill()
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cls.start_decode()
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# Block until both
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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 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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"--tp",
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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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"--tp",
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"4",
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"--base-gpu-id",
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"4",
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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_gsm8k(self):
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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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=f"http://{self.base_host}",
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port=int(self.lb_port),
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)
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metrics = run_eval_few_shot_gsm8k(args)
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print(f"Evaluation metrics: {metrics}")
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self.assertGreater(metrics["accuracy"], 0.60)
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if __name__ == "__main__":
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unittest.main()
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