110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
import os
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import unittest
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from sglang.srt.environ import envs
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from sglang.test.accuracy_test_runner import AccuracyTestParams
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.performance_test_runner import PerformanceTestParams
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from sglang.test.run_combined_tests import run_combined_tests
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from sglang.test.test_utils import ModelLaunchSettings, is_blackwell_system
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# Runs on both H200 and B200 via nightly-8-gpu-common suite
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# Note: trtllm_mla backend may have hardware-specific behavior
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register_cuda_ci(est_time=3000, suite="nightly-8-gpu-common", nightly=True)
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MISTRAL_LARGE3_FP8_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512"
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MISTRAL_LARGE3_NVFP4_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4"
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MISTRAL_LARGE3_EAGLE_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512-Eagle"
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@unittest.skipIf(not is_blackwell_system(), "Requires B200")
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class TestMistralLarge3(unittest.TestCase):
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"""Unified test class for Mistral-Large-3 performance and accuracy.
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Three variants:
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- basic: FP8 model + TP=8 + trtllm_mla backend
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- eagle: basic + EAGLE speculative decoding with draft model
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- nvfp4: NVFP4 model + TP=8 + trtllm_mla backend
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Each variant runs BOTH:
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- Performance test (using NightlyBenchmarkRunner)
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- Accuracy test (using run_eval with mgsm_en)
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"""
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@classmethod
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def setUpClass(cls):
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# Set environment variable to disable JIT DeepGemm
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os.environ["SGLANG_ENABLE_JIT_DEEPGEMM"] = "0"
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@classmethod
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def tearDownClass(cls):
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# Clean up environment variable
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if "SGLANG_ENABLE_JIT_DEEPGEMM" in os.environ:
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del os.environ["SGLANG_ENABLE_JIT_DEEPGEMM"]
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def test_mistral_large3_all_variants(self):
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"""Run performance and accuracy for all Mistral-Large-3 variants."""
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base_args = [
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"--tp=8",
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"--attention-backend=trtllm_mla",
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"--moe-runner-backend=flashinfer_trtllm",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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"--chat-template=mistral",
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]
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eagle_args = [
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"--speculative-algorithm=EAGLE",
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f"--speculative-draft-model-path={MISTRAL_LARGE3_EAGLE_MODEL_PATH}",
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"--speculative-num-steps=3",
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"--speculative-eagle-topk=1",
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"--speculative-num-draft-tokens=4",
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"--kv-cache-dtype=auto",
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]
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variants = [
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# Variant: "basic" - FP8 model + TP=8 + trtllm_mla backend
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ModelLaunchSettings(
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MISTRAL_LARGE3_FP8_MODEL_PATH,
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tp_size=8,
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extra_args=base_args,
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variant="TP8",
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),
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# Variant: "eagle" - FP8 model + TP=8 + trtllm_mla + EAGLE with draft model
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ModelLaunchSettings(
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MISTRAL_LARGE3_FP8_MODEL_PATH,
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tp_size=8,
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extra_args=base_args + eagle_args,
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variant="TP8+MTP",
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),
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# Variant: "nvfp4" - NVFP4 model + TP=8 + trtllm_mla backend
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ModelLaunchSettings(
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MISTRAL_LARGE3_NVFP4_MODEL_PATH,
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tp_size=8,
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extra_args=base_args,
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variant="NVFP4",
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),
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]
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# The TP8+MTP variant trips `NaN detected! draft_forward step 0` during
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# EAGLE draft CUDA-graph capture, on the flashinfer-autotune warmup batch
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# rather than in real decoding: the last nightly that ran with the probe
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# off (2026-06-06) reported accept_len 2.45 and gsm8k 0.960. The probe
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# went live in CI via #27461 and turned that into a startup abort.
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# Suppressed the same way the Nemotron-3 nightlies do, pending a verdict
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# on whether warmup batches should be probed at all.
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with envs.SGLANG_ENABLE_ASYNC_ASSERT.override(0):
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run_combined_tests(
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models=variants,
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test_name="Mistral-Large-3",
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accuracy_params=AccuracyTestParams(
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dataset="gsm8k", baseline_accuracy=0.85
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),
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performance_params=PerformanceTestParams(
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result_dir="performance_results_mistral_large3",
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),
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)
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if __name__ == "__main__":
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unittest.main()
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