55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
"""GSM8K accuracy test for Ling-2.6-flash (BailingMoELinearForCausalLM).
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Guards the hybrid linear / full attention dispatcher: Ling-2.5/2.6
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has 32 layers with `layer_group_size=8`, so layers {7, 15, 23, 31}
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are full attention (MLA) and the rest are linear (Lightning seg_la).
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Runs nightly on the 8-GPU H200 runner with TP=4.
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"""
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import unittest
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
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from sglang.test.server_fixtures.default_fixture import DefaultServerBase
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register_cuda_ci(est_time=600, suite="nightly-8-gpu-common", nightly=True)
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class TestLing26Flash(GSM8KMixin, DefaultServerBase):
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model = "inclusionAI/Ling-2.6-flash"
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# Native 128K context (no YaRN) — avoids the
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# SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN env-var dance and keeps the
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# coverage focused on the dispatcher / hybrid-attention path.
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other_args = [
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"--tp-size",
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"4",
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"--trust-remote-code",
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"--mamba-scheduler-strategy",
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"extra_buffer",
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"--mem-fraction-static",
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"0.75",
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"--max-running-requests",
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"64",
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"--max-mamba-cache-size",
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"256",
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# MTP path also exercises the dispatcher (draft + target verify),
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# so keep it on to maximize coverage.
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"--speculative-algorithm",
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"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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# Observed 0.825 on H200 TP=4 + NEXTN MTP with default 200-question GSM8K
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# (the model card's 0.96 is from full 1319-question runs of the 1T model).
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gsm8k_accuracy_thres = 0.825
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if __name__ == "__main__":
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unittest.main(verbosity=3)
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