repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
sglang | test/registered/amd/accuracy/mi35x/test_gpt_oss_eval_mi35x.py | .py | """MI35x GPT-OSS GSM8K Completion Evaluation Test (8-GPU)
Tests GPT-OSS models (openai/gpt-oss-20b, openai/gpt-oss-120b) using
few-shot completion benchmark on MI35x.
Note: MI35x uses openai/* paths, not lmsys/* paths like MI300X.
Registry: nightly-amd-8-gpu-mi35x suite
"""
import ast
import os
import re
import tim... | 263 | 8,423 |
sglang | test/registered/amd/accuracy/mi35x/test_qwen3_coder_next_eval_mi35x.py | .py | """MI35x Qwen3-Coder-Next GSM8K Completion Evaluation Test (8-GPU)
Tests Qwen3-Coder-Next model with basic and MTP configurations
using few-shot completion benchmark on MI35x.
Registry: nightly-amd-8-gpu-mi35x-qwen3-coder-next suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses impo... | 269 | 8,736 |
sglang | test/registered/amd/accuracy/mi35x/test_deepseek_r1_mxfp4_tp4_mtp_mi35x.py | .py | """MI35x DeepSeek-R1-MXFP4 TP=4 EAGLE GSM8K regression.
This mirrors the production-style TP=4 launch recipe with EAGLE speculative
decoding, overlap plan stream, FP8 KV cache, long context, and full GSM8K
client pressure enabled.
Registry: nightly-amd-8-gpu-mi35x-deepseek-r1-mxfp4-tp4 suite
"""
import ast
import os... | 202 | 6,335 |
sglang | test/registered/amd/accuracy/mi35x/test_kimi_k27_code_mxfp4_eval_mi35x.py | .py | """MI35x Kimi-K2.7-Code-MXFP4 aiter MLA backend accuracy tests (4-GPU)
Tests Kimi-K2.7-Code-MXFP4 with the aiter unified attention backend on MI35x.
This model uses mixed quantization: mxfp4 for MoE layers and fp8 per-channel
for attention projections (q_a_proj, q_b_proj, kv_a_proj_with_mqa, kv_b_proj,
o_proj). The pe... | 185 | 6,326 |
sglang | test/registered/amd/accuracy/mi35x/test_deepseek_v32_mtp_eval_mi35x.py | .py | """MI35x DeepSeek-V3.2 TP+MTP GSM8K Accuracy Evaluation Test (8-GPU)
Tests DeepSeek-V3.2 with TP=8 + MTP (EAGLE speculative decoding) using few-shot
completion benchmark on MI35x.
Registry: nightly-amd-accuracy-8-gpu-mi35x-deepseek-v32-mtp suite
"""
import unittest
from types import SimpleNamespace
import requests
... | 139 | 4,356 |
sglang | test/registered/amd/accuracy/mi35x/test_minimax_m25_eval_mi35x.py | .py | """MI35x MiniMax-M2.5 GSM8K Completion Evaluation Test (8-GPU)
Tests MiniMax-M2.5 with TP=8 + EP=8 configuration using few-shot completion
benchmark on MI35x.
Registry: nightly-amd-8-gpu-mi35x-minimax-m25 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
from typi... | 246 | 7,793 |
sglang | test/registered/amd/accuracy/mi35x/test_minimax_m27_eval_mi35x.py | .py | """MI35x MiniMax-M2.7 GSM8K Completion Evaluation Test (8-GPU)
Tests MiniMax-M2.7 with TP=8 + EP=8 configuration using few-shot completion
benchmark on MI35x.
Registry: nightly-amd-8-gpu-mi35x-minimax-m27 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
from typi... | 246 | 7,793 |
sglang | test/registered/amd/accuracy/mi35x/test_gpt_oss_w4a8_mxfp4_eval_mi35x.py | .py | """MI35x GPT-OSS W4A8 MXFP4-FP8 GSM8K Completion Evaluation Test (8-GPU)
Tests the AMD Quark `gpt-oss-120b-w-mxfp4-a-fp8` checkpoint (MXFP4
weights + static per-tensor FP8 activations) using few-shot completion
benchmark on MI35x.
Registry: nightly-amd-8-gpu-mi35x suite
"""
import ast
import os
# Set HF cache for M... | 252 | 8,492 |
sglang | test/registered/amd/accuracy/mi35x/test_kimi_k26_eval_mi35x.py | .py | """MI35x Kimi-K2.6 GSM8K Completion Evaluation Test (8-GPU)
Tests moonshotai/Kimi-K2.6 with GSM8K few-shot benchmark on MI35x.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI35x test.
Regist... | 111 | 3,481 |
sglang | test/registered/amd/accuracy/mi30x/test_deepseek_v32_tc_eval_amd.py | .py | """AMD DeepSeek-V3.2 TC GSM8K Accuracy Evaluation Test (8-GPU)
Tests DeepSeek-V3.2 with Torch Compile configuration using few-shot
completion benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-deepseek-v32-tc suite
"""
import os
import unittest
from types import SimpleNamespace
from sglang.srt.utils im... | 124 | 3,605 |
sglang | test/registered/amd/accuracy/mi30x/test_deepseek_v32_eval_amd.py | .py | """AMD DeepSeek-V3.2 GSM8K Completion Evaluation Test (8-GPU)
Tests DeepSeek-V3.2 with basic configuration using few-shot completion
benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-deepseek-v32 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
from... | 249 | 8,044 |
sglang | test/registered/amd/accuracy/mi30x/test_grok2_eval_amd.py | .py | """AMD GROK2 GSM8K Completion Evaluation Test (8-GPU)
Tests Grok-2 model using few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu-grok2 suite
"""
import ast
import os
import re
import time
import unittest
import numpy as np
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_reg... | 164 | 4,950 |
sglang | test/registered/amd/accuracy/mi30x/test_glm5_eval_amd.py | .py | """AMD GLM-5 GSM8K Completion Evaluation Test (8-GPU)
Tests GLM-5 with DSA attention backend using few-shot completion
benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-glm5 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass, field
from typing import ... | 249 | 7,937 |
sglang | test/registered/amd/accuracy/mi30x/test_grok1_int4_eval_amd.py | .py | """AMD GROK1-INT4 GSM8K Completion Evaluation Test (8-GPU)
Tests Grok-1 INT4 (W4A8KV8) model using few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu-grok1-int4 suite
"""
import ast
import os
import re
import time
import unittest
import numpy as np
from sglang.srt.utils import kill_process_tree
f... | 164 | 5,027 |
sglang | test/registered/amd/accuracy/mi30x/test_grok_eval_amd.py | .py | """AMD GROK GSM8K Completion Evaluation Test (8-GPU)
Tests GROK models (Grok-1 FP8, Grok-1 INT4, Grok-2) using
few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu-grok suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
from typing import List, Opti... | 291 | 9,015 |
sglang | test/registered/amd/accuracy/mi30x/test_grok1_fp8_eval_amd.py | .py | """AMD GROK1-FP8 GSM8K Completion Evaluation Test (8-GPU)
Tests Grok-1 FP8 model using few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu-grok1-fp8 suite
"""
import ast
import os
import re
import time
import unittest
import numpy as np
from sglang.srt.utils import kill_process_tree
from sglang.te... | 164 | 4,995 |
sglang | test/registered/amd/accuracy/mi30x/test_minimax_m25_eval_amd.py | .py | """AMD MiniMax-M2.5 GSM8K Completion Evaluation Test (8-GPU)
Tests MiniMax-M2.5 with TP=8 + EP=8 configuration using few-shot completion
benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-minimax-m25 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
f... | 246 | 7,797 |
sglang | test/registered/amd/accuracy/mi30x/test_deepseek_v31_eval_amd.py | .py | """AMD DeepSeek-V3.1 GSM8K Completion Evaluation Test (8-GPU)
Tests DeepSeek-V3.1 model using few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu-deepseek-v31 suite
"""
import ast
import os
import re
import time
import unittest
import numpy as np
from sglang.srt.utils import kill_process_tree
from... | 161 | 4,842 |
sglang | test/registered/amd/accuracy/mi30x/test_gpt_oss_eval_amd.py | .py | """AMD GPT-OSS GSM8K Completion Evaluation Test (8-GPU)
Tests GPT-OSS models (lmsys/gpt-oss-20b-bf16, lmsys/gpt-oss-120b-bf16) using
few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
from typing ... | 251 | 7,905 |
sglang | test/registered/amd/accuracy/mi30x/test_qwen35_eval_amd.py | .py | """AMD Qwen 3.5 GSM8K lm-eval Evaluation Test (8-GPU)
Tests Qwen/Qwen3.5-397B-A17B (MoE, Hybrid Attention with Gated Delta Networks)
with lm-eval GSM8K benchmark on MI325/MI300X, matching the AMD Day 0 article.
Registry: nightly-amd-accuracy-8-gpu-qwen35 suite
"""
import os
import unittest
from pathlib import Path
... | 106 | 3,377 |
sglang | test/registered/amd/accuracy/mi30x/test_deepseek_v32_mtp_eval_amd.py | .py | """AMD DeepSeek-V3.2 TP+MTP GSM8K Accuracy Evaluation Test (8-GPU)
Tests DeepSeek-V3.2 with TP=8 + MTP (EAGLE speculative decoding) using few-shot
completion benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-deepseek-v32-mtp suite
"""
import os
import unittest
from types import SimpleNamespace
import ... | 143 | 4,452 |
sglang | test/registered/amd/accuracy/mi30x/test_kimi_k25_eval_amd.py | .py | """AMD Kimi-K2.5 GSM8K Completion Evaluation Test (8-GPU)
Tests moonshotai/Kimi-K2.5 with GSM8K few-shot benchmark on MI325.
Registry: nightly-amd-accuracy-8-gpu-kimi-k25 suite
"""
import os
import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import kill_process_tree
from sglang... | 105 | 3,160 |
sglang | test/registered/amd/accuracy/mi30x/test_deepseek_v32_dp_eval_amd.py | .py | """AMD DeepSeek-V3.2 DP GSM8K Accuracy Evaluation Test (8-GPU)
Tests DeepSeek-V3.2 with DP=8 + TP=8 + dp-attention using few-shot
completion benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-deepseek-v32-dp suite
"""
import os
import unittest
from types import SimpleNamespace
from sglang.srt.utils imp... | 123 | 3,558 |
sglang | test/registered/amd/accuracy/mi30x/test_minimax_m27_eval_amd.py | .py | """AMD MiniMax-M2.7 GSM8K Completion Evaluation Test (8-GPU)
Tests MiniMax-M2.7 with TP=8 + EP=8 configuration using few-shot completion
benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-minimax-m27 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass
f... | 246 | 7,798 |
sglang | test/registered/amd/accuracy/mi30x/test_vlms_mmmu_eval_amd.py | .py | """
AMD VLM MMMU Evaluation Test - MI30x Only
This test evaluates Vision-Language Models (VLMs) on the MMMU benchmark on AMD GPUs.
Models are selected based on compatibility with AMD/ROCm platform.
VLMs tested here:
- Qwen VL series (Qwen2-VL-7B, Qwen2.5-VL-7B, Qwen3-VL-30B)
- InternVL2 series (InternVL2_5-2B)
- Mini... | 385 | 13,486 |
sglang | test/registered/amd/accuracy/mi30x/test_glm51_eval_amd.py | .py | """AMD GLM-5.1 GSM8K Completion Evaluation Test (8-GPU)
Tests GLM-5.1-FP8 with DSA attention backend using few-shot
completion benchmark on MI325/MI300X.
Registry: nightly-amd-accuracy-8-gpu-glm51 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import dataclass, field
from typin... | 239 | 7,508 |
sglang | test/registered/amd/accuracy/mi30x/test_deepseek_r1_eval_amd.py | .py | """AMD DeepSeek-R1 GSM8K Completion Evaluation Test (8-GPU)
Tests DeepSeek-R1-0528 with multiple configurations (basic, MTP, DP, TC)
using few-shot completion benchmark on MI300X.
Registry: nightly-amd-8-gpu-deepseek-r1 suite
"""
import ast
import os
import re
import time
import unittest
from dataclasses import data... | 313 | 9,736 |
sglang | test/registered/amd/accuracy/mi30x/test_kimi_k2_eval_amd.py | .py | """AMD Kimi-K2 GSM8K Completion Evaluation Test (8-GPU)
Tests moonshotai/Kimi-K2-Instruct-0905 with GSM8K few-shot benchmark on MI325.
Registry: nightly-amd-accuracy-8-gpu-kimi-k2 suite
"""
import os
import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import kill_process_tree
fr... | 102 | 3,130 |
sglang | test/registered/amd/accuracy/mi30x/test_glm51_hisparse_eval_mi30x.py | .py | """AMD GLM-5.1 HiSparse GSM8K evaluation test (8-GPU MI30x)."""
import unittest
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_URL_FOR_TES... | 91 | 2,608 |
sglang | test/registered/amd/accuracy/mi30x/test_gsm8k_eval_amd.py | .py | """
AMD GSM8K Evaluation Test (Migrated from test/srt/nightly/)
This test evaluates instruction-tuned models on the gsm8k benchmark using chat completions.
Models are tested with various TP configurations on AMD GPUs.
Registry: nightly-amd suite (2-GPU tests)
"""
import json
import os
import time
import unittest
imp... | 340 | 13,451 |
sglang | test/registered/amd/accuracy/mi30x/test_kimi_k26_eval_amd.py | .py | """AMD Kimi-K2.6 GSM8K Completion Evaluation Test (8-GPU)
Tests moonshotai/Kimi-K2.6 with GSM8K few-shot benchmark on MI325.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI30x test.
Registry... | 109 | 3,345 |
sglang | test/registered/amd/perf/mi35x/test_deepseek_v32_basic_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for DeepSeek-V3.2 model (basic variant).
This test benchmarks the DeepSeek-V3.2 model with basic TP=8 configuration on 8 GPUs.
The model path can be configured via DEEPSEEK_V32_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-mi35x-deepseek-v32-basic suite
Example... | 138 | 5,042 |
sglang | test/registered/amd/perf/mi35x/test_glm5_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for GLM-5.
Tests GLM-5 with DSA attention backend using bench_one_batch on 8 GPUs.
Registry: nightly-perf-8-gpu-mi35x-glm5 suite
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.nightly_bench_utils im... | 139 | 4,933 |
sglang | test/registered/amd/perf/mi35x/test_grok1_int4_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for Grok-1 INT4 (W4A8KV8).
This test benchmarks Grok-1 (314B MOE) with INT4 weight quantization on 8 GPUs.
Registry: nightly-perf-8-gpu-mi35x-grok1-int4 suite
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci_register import register_amd_ci
from sgla... | 137 | 5,029 |
sglang | test/registered/amd/perf/mi35x/test_deepseek_r1_mxfp4_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model.
This test benchmarks the DeepSeek-R1-MXFP4 quantized model on MI35x with 8 GPUs.
Registry: nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4 suite
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci_register import register_amd_ci... | 135 | 5,064 |
sglang | test/registered/amd/perf/mi35x/test_qwen35_fp8_ar_fusion_mi35x.py | .py | """MI35x PR-CI accuracy coverage for Qwen3.5-FP8 aiter AR-fusion.
PR-specific fused AR+RMSNorm+per-group-quant accuracy check. The file runs in
the 8-GPU MI35x stage-c suite and launches two TP4 servers in parallel:
* GPUs 0-3: fused AR+RMSNorm+per-group FP8 quant enabled.
* GPUs 4-7: same launch with SGLANG_DISABLE_... | 235 | 7,877 |
sglang | test/registered/amd/perf/mi35x/test_minimax_m27_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for MiniMax-M2.7 (8-GPU).
This test benchmarks MiniMax-M2.7 with TP=8 + EP=8 configuration on MI35x.
The model path can be configured via MINIMAX_M27_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-mi35x-minimax-m27 suite
Example usage:
python -m pytest test_... | 142 | 4,913 |
sglang | test/registered/amd/perf/mi35x/test_deepseek_v32_mtp_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for DeepSeek-V3.2 model (MTP variant).
This test benchmarks the DeepSeek-V3.2 model with MTP (EAGLE speculative decoding)
configuration on 8 GPUs.
The model path can be configured via DEEPSEEK_V32_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-mi35x-deepseek-v32-... | 200 | 7,207 |
sglang | test/registered/amd/perf/mi35x/test_glm51_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for GLM-5.1.
Tests GLM-5.1-FP8 with DSA attention backend using bench_one_batch
on 8 GPUs with TP=8, FP8 KV cache.
Registry: nightly-perf-8-gpu-mi35x-glm51 suite
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci_register import register_amd_ci
from s... | 142 | 5,000 |
sglang | test/registered/amd/perf/mi35x/test_minimax_m25_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for MiniMax-M2.5 (8-GPU).
This test benchmarks MiniMax-M2.5 with TP=8 + EP=8 configuration on MI35x.
The model path can be configured via MINIMAX_M25_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-mi35x-minimax-m25 suite
Example usage:
python -m pytest test_... | 142 | 4,913 |
sglang | test/registered/amd/perf/mi35x/test_kimi_k26_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for Kimi-K2.6 model.
This test benchmarks moonshotai/Kimi-K2.6 with TP=8 on MI35x.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI35x accuracy test (mix... | 148 | 5,501 |
sglang | test/registered/amd/perf/mi35x/test_glm5_mxfp4_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for GLM-5-MXFP4 model.
Benchmarks the AMD Quark MXFP4-quantized GLM-5 model on MI35x with 8 GPUs.
Model: amd/GLM-5-MXFP4 (MOE-only MXFP4 quantization of zai-org/GLM-5)
Reference: https://huggingface.co/amd/GLM-5-MXFP4
Registry: nightly-perf-8-gpu-mi35x-glm5-mxfp4 suite
"""
imp... | 155 | 5,453 |
sglang | test/registered/amd/perf/mi35x/test_qwen35_fp8_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for Qwen3.5-397B-A17B FP8.
Tests Qwen3.5-397B-A17B-FP8 (MoE, Hybrid Attention with Gated Delta Networks)
on 8 GPUs with triton attention backend.
Registry: nightly-perf-8-gpu-mi35x-qwen35-fp8 suite
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci_re... | 135 | 4,722 |
sglang | test/registered/amd/perf/mi35x/test_deepseek_r1_mxfp4_ar_fusion_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model with AIter AllReduce Fusion.
This test benchmarks the DeepSeek-R1-MXFP4 quantized model on MI35x with 8 GPUs
using --enable-aiter-allreduce-fusion.
Registry: nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4-ar-fusion suite
"""
import os
import unittest
fro... | 135 | 5,044 |
sglang | test/registered/amd/perf/mi35x/test_deepseek_r1_mxfp4_kv_fp8_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model with KV Cache FP8.
This test benchmarks the DeepSeek-R1-MXFP4 quantized model on MI35x with 8 GPUs
using --kv-cache-dtype fp8_e4m3.
Registry: nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4-kv-fp8 suite
"""
import os
import unittest
from typing import Lis... | 136 | 5,010 |
sglang | test/registered/amd/perf/mi35x/test_grok2_perf_mi35x.py | .py | """MI35x Nightly performance benchmark for Grok-2.
This test benchmarks Grok-2 with FP8 quantization on 8 GPUs.
Registry: nightly-perf-8-gpu-mi35x-grok2 suite
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.nightly_bench_utils import Benc... | 137 | 4,947 |
sglang | test/registered/amd/perf/mi30x/test_deepseek_v3_perf.py | .py | """Nightly performance benchmark for DeepSeek-V3 model.
This test benchmarks the DeepSeek-V3 model with basic and MTP configurations on 8 GPUs.
The model path can be configured via DEEPSEEK_V3_MODEL_PATH environment variable.
Example usage:
DEEPSEEK_V3_MODEL_PATH=deepseek-ai/DeepSeek-V3-0324 python -m pytest tes... | 146 | 5,469 |
sglang | test/registered/amd/perf/mi30x/test_minimax_m27_perf_amd.py | .py | """Nightly performance benchmark for MiniMax-M2.7 on MI325/MI300X (8-GPU).
This test benchmarks MiniMax-M2.7 with TP=8 + EP=8 configuration.
The model path can be configured via MINIMAX_M27_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-minimax-m27 suite
Example usage:
python -m pytest test_minim... | 140 | 4,884 |
sglang | test/registered/amd/perf/mi30x/test_grok2_perf.py | .py | """Nightly performance benchmark for Grok-2.
This test benchmarks Grok-2 with FP8 quantization on 8 GPUs.
Model paths can be configured via environment variables:
- GROK2_MODEL_PATH: Path to Grok-2 model (default: xai-org/grok-2)
- GROK2_TOKENIZER_PATH: Path to Grok-2 tokenizer (default: alvarobartt/grok-2-tokenizer)... | 146 | 5,184 |
sglang | test/registered/amd/perf/mi30x/test_deepseek_v31_perf.py | .py | """Nightly performance benchmark for DeepSeek-V3.1 model.
This test benchmarks the DeepSeek-V3.1 model with basic and MTP configurations on 8 GPUs.
The model path can be configured via DEEPSEEK_V31_MODEL_PATH environment variable.
Example usage:
DEEPSEEK_V31_MODEL_PATH=deepseek-ai/DeepSeek-V3.1 python -m pytest ... | 156 | 5,792 |
sglang | test/registered/amd/perf/mi30x/test_vlms_perf_amd.py | .py | """AMD Nightly performance benchmark for VLM models (2-GPU).
This test benchmarks Vision-Language Models on AMD MI30x/MI35x with 2 GPUs.
Registry: nightly-amd-perf-vlm-2-gpu suite
Example usage:
python -m pytest test_vlms_perf_amd.py -v
"""
import os
import unittest
import warnings
from typing import List
from... | 146 | 5,321 |
sglang | test/registered/amd/perf/mi30x/test_deepseek_v32_basic_perf_amd.py | .py | """AMD Nightly performance benchmark for DeepSeek-V3.2 model (basic variant).
This test benchmarks the DeepSeek-V3.2 model with basic TP=8 configuration on 8 GPUs.
The model path can be configured via DEEPSEEK_V32_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-deepseek-v32-basic suite
Example usage:
... | 143 | 5,293 |
sglang | test/registered/amd/perf/mi30x/test_glm5_perf_amd.py | .py | """Nightly performance benchmark for GLM-5 on MI30x.
Tests GLM-5 with DSA attention backend using bench_one_batch on 8 GPUs.
Model paths can be configured via environment variables:
- GLM5_MODEL_PATH: Path to GLM-5 model (default: zai-org/GLM-5-FP8)
Example usage:
python -m pytest test_glm5_perf_amd.py -v
"""
i... | 140 | 4,839 |
sglang | test/registered/amd/perf/mi30x/test_kimi_k26_perf_amd.py | .py | """AMD Nightly performance benchmark for Kimi-K2.6 model.
This test benchmarks moonshotai/Kimi-K2.6 with TP=8 on MI325/MI300X.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI30x accuracy test... | 148 | 5,477 |
sglang | test/registered/amd/perf/mi30x/test_deepseek_v32_mtp_perf_amd.py | .py | """AMD Nightly performance benchmark for DeepSeek-V3.2 model (MTP variant).
This test benchmarks the DeepSeek-V3.2 model with MTP (EAGLE speculative decoding)
configuration on 8 GPUs.
The model path can be configured via DEEPSEEK_V32_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-deepseek-v32-mtp suit... | 150 | 5,558 |
sglang | test/registered/amd/perf/mi30x/test_grok1_int4_perf.py | .py | """Nightly performance benchmark for Grok-1 INT4 (W4A8KV8).
This test benchmarks Grok-1 (314B MOE) with INT4 weight quantization on 8 GPUs.
Model paths can be configured via environment variables:
- GROK1_MODEL_PATH: Path to Grok-1 INT4 model (default: amd/grok-1-W4A8KV8)
- GROK1_TOKENIZER_PATH: Path to Grok-1 tokeni... | 144 | 5,293 |
sglang | test/registered/amd/perf/mi30x/test_grok1_fp8_perf.py | .py | """Nightly performance benchmark for Grok-1 FP8.
This test benchmarks Grok-1 (314B MOE) with FP8 quantization on 8 GPUs.
Model paths can be configured via environment variables:
- GROK1_MODEL_PATH: Path to Grok-1 model (default: lmzheng/grok-1)
- GROK1_TOKENIZER_PATH: Path to Grok-1 tokenizer (default: Xenova/grok-1-... | 134 | 4,925 |
sglang | test/registered/amd/perf/mi30x/test_glm51_perf_amd.py | .py | """Nightly performance benchmark for GLM-5.1 on MI30x.
Tests GLM-5.1-FP8 with DSA attention backend using bench_one_batch
on 8 GPUs with TP=8, FP8 KV cache.
Model path can be configured via GLM51_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-glm51 suite
"""
import os
import unittest
from typing impo... | 138 | 4,787 |
sglang | test/registered/amd/perf/mi30x/test_minimax_m25_perf_amd.py | .py | """Nightly performance benchmark for MiniMax-M2.5 on MI325/MI300X (8-GPU).
This test benchmarks MiniMax-M2.5 with TP=8 + EP=8 configuration.
The model path can be configured via MINIMAX_M25_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-minimax-m25 suite
Example usage:
python -m pytest test_minim... | 140 | 4,884 |
sglang | test/registered/amd/perf/mi30x/test_qwen35_fp8_perf_amd.py | .py | """Nightly performance benchmark for Qwen3.5-397B-A17B FP8.
Tests Qwen3.5-397B-A17B-FP8 (MoE, Hybrid Attention with Gated Delta Networks)
on 8 GPUs with triton attention backend.
Model path can be configured via environment variable:
- QWEN35_FP8_MODEL_PATH: Path to Qwen3.5-FP8 model
(default: Qwen/Qwen3.5-397B-A17... | 139 | 4,870 |
sglang | test/registered/amd/perf/mi30x/test_text_models_perf_amd.py | .py | """AMD Nightly performance benchmark for text models (2-GPU).
This test benchmarks text models on AMD MI30x/MI35x with 2 GPUs.
Registry: nightly-amd-perf-text-2-gpu suite
Example usage:
python -m pytest test_text_models_perf_amd.py -v
"""
import os
import unittest
from typing import List
from sglang.test.ci.ci... | 133 | 4,905 |
sglang | test/registered/utils/test_log_utils.py | .py | import io
import json
import re
import tempfile
import unittest
import uuid
from contextlib import redirect_stdout
from pathlib import Path
from sglang.srt.utils.log_utils import create_log_targets, log_json
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=6, suite="base-a-test-cpu")
re... | 85 | 3,076 |
sglang | test/registered/utils/test_model_file_verifier.py | .py | import hashlib
import json
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
import warnings
from contextlib import nullcontext
from io import StringIO
import requests
from huggingface_hub import snapshot_download
from sglang.srt.utils import kill_process_tree
from sglang.srt.utils.... | 350 | 11,974 |
sglang | test/registered/utils/test_bench_typebaseddispatcher.py | .py | import timeit
from typing import Any, Callable, List, Tuple, Type
from sglang.test.ci.ci_register import register_amd_ci
from sglang.utils import TypeBasedDispatcher
register_amd_ci(est_time=10, suite="stage-b-test-1-gpu-small-amd")
class TypeBasedDispatcherList:
def __init__(self, mapping: List[Tuple[Type, Cal... | 265 | 7,414 |
sglang | test/registered/utils/test_socket_utils.py | .py | import os
import socket
import unittest
from unittest.mock import patch
from sglang.srt.utils.network import (
_get_addrinfos_for_bind,
bind_port,
get_free_port,
get_open_port,
is_port_available,
try_bind_socket,
)
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_uti... | 226 | 8,217 |
sglang | test/registered/utils/test_numa_utils.py | .py | import ctypes
import os
import unittest
from contextlib import ExitStack
from unittest.mock import MagicMock, patch
from sglang.srt.environ import envs
from sglang.srt.utils.numa_utils import (
_handle_numa_bind_failure,
_is_numa_available,
_node_cpus,
_numactl_cpu_mem_args,
_probe_numactl_args,
... | 594 | 25,202 |
sglang | test/registered/utils/test_phase_checker.py | .py | """Unit tests for srt/utils/phase_checker.py — SimplePhaseChecker."""
from __future__ import annotations
import subprocess
import sys
import textwrap
import unittest
from enum import IntEnum
import torch
from sglang.srt.utils.phase_checker import SimplePhaseChecker
from sglang.test.ci.ci_register import register_am... | 486 | 20,599 |
sglang | test/registered/utils/test_stale_shm_cleanup.py | .py | import os
import subprocess
import sys
import unittest
from multiprocessing import shared_memory
from unittest.mock import patch
from sglang.srt.utils.stale_shm_cleanup import (
_creator_pid,
cleanup_stale_shm,
make_shm_name,
)
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_tim... | 154 | 5,583 |
sglang | test/registered/utils/test_network_address.py | .py | import socket
import unittest
from unittest.mock import patch
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils.network import NetworkAddress, is_zmq_endpoint_ipv6
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=7, suite="base-a-test-cpu")
register_cpu_ci(es... | 343 | 12,632 |
sglang | test/registered/utils/test_type_based_dispatcher.py | .py | # tests/benchmarks/test_type_dispatcher_e2e.py
"""
E2E test for TypeBasedDispatcher optimization.
Tests real-world scenarios with actual request types.
"""
import timeit
import unittest
from sglang.srt.managers.io_struct import SamplingParams
from sglang.test.ci.ci_register import register_amd_ci, register_cpu_ci
fro... | 228 | 8,510 |
sglang | test/registered/sampling/test_sampling_mask.py | .py | import math
import unittest
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.test_utils import (
DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTes... | 283 | 10,397 |
sglang | test/registered/sampling/test_deterministic_gumbel_u1.py | .py | """The u == 1.0 gumbel bucket must neither go +inf (NaN -> samples vocab-masked
tokens) nor exceed the hash spacing's natural maximum (dominates every row)."""
import unittest
import torch
from sglang.srt.layers.sampler import sampling_from_probs_torch
from sglang.test.ci.ci_register import register_amd_ci, register... | 52 | 1,771 |
sglang | test/registered/sampling/test_original_logprobs.py | .py | """Test original log probability alignment between SGLang and Hugging Face.
This test suite verifies the correctness of the `origin_logprobs` output (temperature=1)
and the `logprobs` output (temperature=0.5) in SGLang by comparing it against
raw logit-based probabilities computed directly from a reference Hugging Fac... | 200 | 7,944 |
sglang | test/registered/sampling/test_penalty.py | .py | import json
import random
import re
import unittest
from concurrent.futures import ThreadPoolExecutor
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.test_utils import (
DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
... | 266 | 10,009 |
sglang | test/registered/sampling/test_pytorch_sampling_backend.py | .py | import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_TIMEOUT_... | 102 | 3,016 |
sglang | test/registered/quant/test_fp8_utils.py | .py | import unittest
from types import SimpleNamespace
from unittest.mock import patch
import torch
from sglang.srt.layers.quantization.fp8_utils import (
inverse_transform_scale_ue8m0,
quant_weight_ue8m0,
transform_scale_ue8m0,
)
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_ut... | 314 | 11,785 |
sglang | test/registered/quant/test_gptqmodel_dynamic.py | .py | import time
import unittest
import requests
import torch
from sglang.srt.server_args import set_global_server_args_for_scheduler
from sglang.srt.utils import get_device, kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH... | 194 | 6,540 |
sglang | test/registered/quant/test_quark_mxfp4.py | .py | import io
import os
import re
import unittest
from sglang.test.ci.ci_register import register_amd_ci
register_amd_ci(est_time=106, suite="stage-b-test-1-gpu-small-amd-mi35x")
import os
import time
from types import SimpleNamespace
import requests
import torch
from sglang.srt.utils import kill_process_tree
from sgla... | 361 | 13,569 |
sglang | test/registered/quant/test_int4fp8_moe.py | .py | from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_URL_FOR_TEST,
CustomTestCase,
popen_launch_server,
)
register_amd_ci(est_time=313, ... | 65 | 1,596 |
sglang | test/registered/quant/test_nvfp4_embedding.py | .py | #!/usr/bin/env python3
import unittest
import torch
from sglang.srt.layers.quantization.modelopt_quant import (
ModelOptFp4Config,
ModelOptNvFp4EmbeddingMethod,
)
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
register_cpu_ci(est_time=5, suite="base-... | 125 | 4,029 |
sglang | test/registered/quant/test_block_int8.py | .py | import itertools
import unittest
import torch
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.moe.moe_runner.triton_utils.fused_moe import fused_moe
from sglang.srt.layers.moe.topk import TopKConfig, select_experts
from sglang.srt.server_args import ServerArgs, set_global_server_args_for_sc... | 237 | 8,309 |
sglang | test/registered/quant/test_fp8kv_triton.py | .py | import unittest
from types import SimpleNamespace
from urllib.parse import urlparse
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNC... | 60 | 1,707 |
sglang | test/registered/quant/test_is_layer_skipped.py | .py | import unittest
from sglang.srt.layers.quantization.utils import is_layer_skipped
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
register_cpu_ci(est_time=5, suite="base-a-test-cpu")
# Qwen3-Next FP8 actually publishes the equivalent of this in
# packed_modul... | 64 | 2,674 |
sglang | test/registered/quant/test_w8a8_quantization.py | .py | import time
import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR... | 115 | 3,222 |
sglang | test/registered/quant/test_llama8b_nvfp4_kv_cache_sm120.py | .py | import unittest
from sglang.srt.utils.common import is_sm120_supported
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.server_fixtures.default_fixture import DefaultServerBase
register_cuda_ci(
est_time=300,
stage="extra-a",
... | 44 | 1,238 |
sglang | test/registered/quant/test_gguf.py | .py | import unittest
from huggingface_hub import hf_hub_download
import sglang as sgl
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_utils import CustomTestCase
register_cuda_ci(est_time=76, stage="base-b", runner_config="1-gpu-small")
class TestGGUF(CustomTestCase):
def test_models(s... | 33 | 917 |
sglang | test/registered/quant/test_autoround_quantization.py | .py | """
Usage:
python3 -m unittest test_autoround_quantization
"""
import os
import shutil
import tempfile
import unittest
from types import SimpleNamespace
from sglang.srt.configs.device_config import DeviceConfig
from sglang.srt.configs.load_config import LoadConfig
from sglang.srt.configs.model_config import ModelConf... | 99 | 3,143 |
sglang | test/registered/quant/test_kvfp4_quant_dequant.py | .py | #!/usr/bin/env python3
import sys
import time
import numpy as np
import pytest
import torch
from sglang.srt.layers.quantization.kvfp4_tensor import FP4MXBlock16KVQuantizeUtil
from sglang.test.ci.ci_register import register_cuda_ci
register_cuda_ci(est_time=30, stage="base-b", runner_config="1-gpu-large")
def calc... | 121 | 3,631 |
sglang | test/registered/quant/test_fp8_kernel.py | .py | import unittest
import torch
from sglang.kernels.ops.quantization.fp8_kernel import (
per_token_group_quant_fp8,
w8a8_block_fp8_matmul,
)
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_utils import CustomTestCase
register_cuda_ci(est_time=10, stage="base-b", runner_config="1-gp... | 146 | 4,478 |
sglang | test/registered/quant/test_fused_rms_fp8_group_quant.py | .py | import itertools
import unittest
import torch
import torch.nn.functional as F
from sglang.srt.layers.quantization.fp8_utils import (
materialize_bpreshuffle_fp8_scale,
view_aiter_fused_rms_transposed_fp8_scale,
)
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.test_utils import CustomT... | 193 | 6,877 |
sglang | test/registered/quant/test_awq_dequant.py | .py | # Adapted from https://github.com/vllm-project/vllm/blob/main/tests/kernels/quantization/test_awq_triton.py
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
unittest version of the AWQ Triton kernel tests.
Run with:
python -m unittest test_awq_dequant.p... | 181 | 5,730 |
sglang | test/registered/quant/test_marlin_moe.py | .py | import itertools
import unittest
from typing import Optional
import torch
from sgl_kernel.scalar_type import scalar_types
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.moe.fused_moe_triton.fused_marlin_moe import fused_marlin_moe
from sglang.srt.server_args import ServerArgs, set_global_s... | 442 | 15,100 |
sglang | test/registered/quant/test_quant_config_parsing.py | .py | import unittest
from unittest.mock import MagicMock
from sglang.srt.configs.model_config import ModelConfig
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
register_cpu_ci(est_time=15, suite="base-a-test-cpu")
register_cpu_ci(est_time=8, suite="base-c-test-cpu"... | 78 | 2,939 |
sglang | test/registered/quant/test_modelopt_fp8.py | .py | import unittest
from types import SimpleNamespace
from urllib.parse import urlparse
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_UR... | 84 | 2,638 |
sglang | test/registered/quant/test_awq.py | .py | import unittest
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_AWQ_MOE_MODEL_NAME_FOR_TEST,
DEFAULT_TIMEOUT_FOR_SERVE... | 85 | 2,400 |
sglang | test/registered/quant/test_nvfp4_gemm_sm120.py | .py | import unittest
from types import SimpleNamespace
from urllib.parse import urlparse
from sglang.srt.utils import get_device_sm, kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.test_utils import (
D... | 72 | 2,084 |
sglang | test/registered/quant/test_int8_kernel.py | .py | import itertools
import unittest
import torch
from sglang.kernels.ops.quantization.int8_kernel import per_token_quant_int8
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.moe.moe_runner.triton_utils.fused_moe import fused_moe
from sglang.srt.layers.moe.topk import TopKConfig, select_experts... | 177 | 6,265 |
sglang | test/registered/quant/test_triton_scaled_mm.py | .py | import unittest
from typing import Optional
import torch
import torch.testing
from sglang.kernels.ops.quantization.fp8_kernel import triton_scaled_mm
from sglang.srt.utils.common import get_device
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.test_utils import CustomTestCas... | 114 | 4,295 |
sglang | test/registered/ops/test_aiter_greedy_sample_amd.py | .py | """Unit tests for aiter greedy_sample kernel and Sampler integration.
Validates that:
1. aiter.greedy_sample produces identical results to torch.argmax (kernel level)
2. Sampler.forward() correctly dispatches to aiter when _use_aiter=True
3. The fallback to torch.argmax works when _use_aiter=False
4. return_logprob pa... | 297 | 10,383 |
sglang | test/registered/ops/test_aiter_allgather_amd.py | .py | import os
import subprocess
import sys
import unittest
from pathlib import Path
import torch
from sglang.test.ci.ci_register import register_amd_ci
register_amd_ci(est_time=180, suite="stage-c-test-large-8-gpu-amd")
class TestAiterAllGatherAmd(unittest.TestCase):
@staticmethod
def _gpu_count():
re... | 117 | 3,542 |
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