Add 5090 dry run stage to PR test workflow (#17022)
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@@ -9,6 +9,7 @@ from sglang.test.test_utils import CustomTestCase
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# Triton kernel unit test for KV indices creation
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register_cuda_ci(est_time=10, suite="stage-b-test-small-1-gpu")
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register_cuda_ci(est_time=7, suite="stage-b-test-small-1-gpu-5090")
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register_amd_ci(est_time=10, suite="stage-b-test-small-1-gpu-amd")
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@@ -15,6 +15,7 @@ from sglang.srt.server_args import ServerArgs, set_global_server_args_for_schedu
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from sglang.test.ci.ci_register import register_cuda_ci
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register_cuda_ci(est_time=9, suite="stage-b-test-small-1-gpu")
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register_cuda_ci(est_time=7, suite="stage-b-test-small-1-gpu-5090")
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class TestMamba(unittest.TestCase):
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@@ -15,6 +15,7 @@ from sglang.test.test_utils import (
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# RadixAttention server integration tests
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register_cuda_ci(est_time=100, suite="stage-b-test-small-1-gpu")
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register_cuda_ci(est_time=79, suite="stage-b-test-small-1-gpu-5090")
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register_amd_ci(est_time=100, suite="stage-b-test-small-1-gpu-amd")
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@@ -21,6 +21,7 @@ from sglang.test.ci.ci_register import register_cuda_ci
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# CPU-based unit test, runs quickly on any GPU runner
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register_cuda_ci(est_time=5, suite="stage-b-test-small-1-gpu")
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register_cuda_ci(est_time=7, suite="stage-b-test-small-1-gpu-5090")
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import time
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import unittest
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@@ -19,6 +19,7 @@ from sglang.test.test_utils import (
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# Torch native attention backend integration test with MMLU eval
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register_cuda_ci(est_time=169, suite="stage-b-test-small-1-gpu")
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register_cuda_ci(est_time=120, suite="stage-b-test-small-1-gpu-5090")
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register_amd_ci(est_time=150, suite="stage-b-test-small-1-gpu-amd")
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