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[None][test] Adjust perf test cases to avoid OOM and remove outdated test cases #17303

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Merged
yufeiwu-nv merged 5 commits into from
Aug 6, 2026
Merged

[None][test] Adjust perf test cases to avoid OOM and remove outdated test cases #17303

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c9900de
[None][refactor] Clean up model paths and remove deprecated configura…
yufeiwu-nv Jul 30, 2026
24b9bda
Update llm_perf_core.yml to refine test cases and conditions for qwen…
yufeiwu-nv Aug 5, 2026
b699db0
Merge branch 'main' into fp4
yufeiwu-nv Aug 5, 2026
fab1c97
Refactor performance test configurations by removing deprecated model…
yufeiwu-nv Aug 5, 2026
15f4ccf
Merge branch 'main' into fp4
yufeiwu-nv Aug 6, 2026
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13 changes: 0 additions & 13 deletions tests/integration/defs/perf/_model_paths.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,12 +22,7 @@
"llama_v3.3_70b_instruct": "llama-3.3-models/Llama-3.3-70B-Instruct",
"llama_v3.3_70b_instruct_fp8": "modelopt-hf-model-hub/Llama-3.3-70B-Instruct-fp8",
"llama_v3.3_70b_instruct_fp4": "modelopt-hf-model-hub/Llama-3.3-70B-Instruct-fp4",
"llama_v3.1_nemotron_nano_8b_fp8": "Llama-3.1-Nemotron-Nano-8B-v1-FP8",
"llama_v3.3_nemotron_super_49b": "nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1",
"llama_v3.3_nemotron_super_49b_fp8": "nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1-FP8",
"llama_v3.3_nemotron_super_49b_v1.5_fp8": "nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1_5-FP8",
"llama_v3.1_nemotron_ultra_253b": "nemotron-nas/Llama-3_1-Nemotron-Ultra-253B-v1",
"llama_v3.1_nemotron_ultra_253b_fp8": "nemotron-nas/Llama-3_1-Nemotron-Ultra-253B-v1-FP8",
"llama_v4_scout_17b_16e_instruct": "llama4-models/Llama-4-Scout-17B-16E-Instruct",
"llama_v4_scout_17b_16e_instruct_fp8": "llama4-models/Llama-4-Scout-17B-16E-Instruct-FP8",
"llama_v4_scout_17b_16e_instruct_fp4": "llama4-models/Llama-4-Scout-17B-16E-Instruct-FP4",
Expand All @@ -42,8 +37,6 @@
"gemma_3_1b_it": "gemma/gemma-3-1b-it",
"gemma_4_26b_a4b_nvfp4": "gemma/nvidia-Gemma-4-26B-A4B-NVFP4",
"gemma_4_31b_it_nvfp4": "gemma/nvidia-Gemma-4-31B-IT-NVFP4",
"deepseek_r1_fp8": "DeepSeek-R1/DeepSeek-R1",
"deepseek_r1_nvfp4": "DeepSeek-R1/DeepSeek-R1-FP4",
"deepseek_r1_0528_fp8": "DeepSeek-R1/DeepSeek-R1-0528/",
"deepseek_r1_0528_fp4": "DeepSeek-R1/DeepSeek-R1-0528-FP4/",
"deepseek_r1_0528_fp4_v2": "DeepSeek-R1/DeepSeek-R1-0528-FP4-v2/",
Expand Down Expand Up @@ -85,9 +78,6 @@
# Nemotron-3-Nano-Omni-30B (text + image multimodal)
"nemotron_3_nano_omni_nvfp4": "NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4",
"nemotron_3_nano_omni_nvfp4_image": "NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4",
"kimi_k2_nvfp4": "Kimi-K2-Thinking-NVFP4",
# MiniMax M2.5 (FP8 block-scale, ~230B MoE)
"minimax_m2.5_fp8": "MiniMax-M2.5",
# MiniMax M3 (block-sparse MoE, MXFP8 weights, BF16 activations + KV cache)
"minimax_m3_mxfp8": "MiniMax-M3-MXFP8",
# Qwen3.5 dense + MoE
Expand All @@ -99,9 +89,6 @@
"qwen3.5_397b_a17b_fp4": "Qwen3.5-397B-A17B-NVFP4",
# Qwen3.6 (GDN linear-attn MoE, NVFP4)
"qwen3.6_35b_a3b_fp4": "Qwen3.6-35B-A3B-NVFP4",
# DeepSeek V3.2 (671B MoE)
"deepseek_v3.2_fp8": "DeepSeek-V3.2-hf",
"deepseek_v3.2_fp4": "DeepSeek-V3.2-NVFP4",
# DeepSeek V4
"deepseek_v4_pro_fp4": "DeepSeek-V4-Pro",
"deepseek_v4_flash": "DeepSeek-V4-Flash",
Expand Down
153 changes: 1 addition & 152 deletions tests/integration/defs/perf/pytorch_model_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,7 @@ def get_model_yaml_config(model_label: str,
pattern_configs = [
# Deepseek default cases
{
'patterns': ['deepseek_r1', 'kimi_k2_nvfp4'],
'patterns': ['deepseek_r1'],
'config': {
'enable_attention_dp': True,
}
Expand Down Expand Up @@ -291,151 +291,10 @@ def get_model_yaml_config(model_label: str,
},
}
},
# DeepSeek R1 models with MTP speculative decoding
{
'patterns': [
'deepseek_r1-bench-pytorch-float16-maxbs:1-maxnt:8192-input_output_len:1000,2000-reqs:10-ep:4-gpus:8',
'deepseek_r1_nvfp4-bench-pytorch-float16-maxbs:1-maxnt:8192-input_output_len:1000,2000-reqs:10-ep:4-tp:8-gpus:8'
],
'config': {
'enable_attention_dp': True,
'cuda_graph_config': {},
'speculative_config': {
'decoding_type': 'MTP',
'max_draft_len': 3
}
}
},
{
'patterns': [
'deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:32-maxnt:32768-input_output_len:8192,1024-reqs:20-con:1-ep:1-gpus:4'
],
'config': {
'enable_iter_perf_stats': True,
'print_iter_log': False,
'cuda_graph_config': {
'max_batch_size': 16,
'enable_padding': False
},
'moe_config': {
'max_num_tokens': 32768
},
'speculative_config': {
'decoding_type': 'MTP',
'max_draft_len': 3
},
'disable_overlap_scheduler': True,
'enable_autotuner': True,
'kv_cache_config': {
'free_gpu_memory_fraction': 0.6,
'enable_block_reuse': True,
'enable_partial_reuse': False
},
'enable_chunked_prefill': True
}
},
# DeepSeek R1 models with large batch sizes and cuda graph padding
{
'patterns': [
'deepseek_r1_fp8-bench-pytorch-float16-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:49152-con:3072-ep:8-gpus:8',
'deepseek_r1_nvfp4-bench-pytorch-float16-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:49152-con:3072-ep:8-gpus:8'
],
'config': {
'enable_attention_dp': True,
'cuda_graph_config': {
'enable_padding': True,
'batch_sizes': [1, 2, 4, 8, 16, 32, 64, 128, 256, 384]
}
}
},
# DeepSeek R1 model with specific batch size 128
{
'patterns':
'deepseek_r1_fp8-bench-pytorch-float16-maxbs:128-maxnt:1127-input_output_len:1000,2000-reqs:5120-con:1024-ep:8-gpus:8',
'config': {
'enable_attention_dp': True,
'cuda_graph_config': {
'batch_sizes': [128]
}
}
},
# Deepseek R1 model with chunked prefill
{
'patterns': [
'deepseek_r1_fp8-bench-pytorch-float8-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:5000,500',
'deepseek_r1_fp8-bench-pytorch-float8-maxbs:256-maxnt:1024-kv_frac:0.85-input_output_len:2000,2000',
'deepseek_v3_lite_fp8-bench-pytorch-float8-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:5000,500',
'deepseek_v3_lite_nvfp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:5000,500',
'deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:5000,500',
'deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:256-maxnt:1024-kv_frac:0.85-input_output_len:2000,2000',
],
'config': {
'enable_attention_dp': True,
'enable_chunked_prefill': True,
}
},
# Deepseek R1 NVFP4 with chunked prefill, large seq len, and fp8 KV cache
{
'patterns': [
'deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:32-maxnt:4096-kv_frac:0.80-input_output_len:8192,512-reqs:3000-ep:2-tp:4-gpus:4',
],
'config': {
'enable_attention_dp': True,
'enable_chunked_prefill': True,
'max_num_tokens': 4096,
'max_batch_size': 32,
'max_seq_len': 81920,
'kv_cache_config': {
'dtype': 'fp8',
'free_gpu_memory_fraction': 0.80,
'enable_block_reuse': False,
},
'cuda_graph_config': {
'enable_padding': True,
'max_batch_size': 32,
},
}
},
# Deepseek R1 model with CUTLASS backend
{
'patterns': [
'deepseek_r1_nvfp4-bench-pytorch-streaming-float4-maxbs:512-maxnt:5220-input_output_len:4000,2000',
],
'config': {
'enable_attention_dp': True,
'moe_config': {
'backend': 'CUTLASS',
'max_num_tokens': 3072,
},
'kv_cache_config': {
'dtype': 'fp8',
'free_gpu_memory_fraction': 0.5,
},
'cuda_graph_config': {
'enable_padding': True,
'batch_sizes': [1, 2, 4, 8, 16, 32, 64],
},
}
},
# Deepseek_v3_lite_cases
{
'patterns':
'deepseek_v3_lite_nvfp4-bench-pytorch-streaming-float4-maxbs:2048-maxnt:8192-input_output_len:256,256-reqs:200',
'config': {
'print_iter_log': True,
'cuda_graph_config': {
'enable_padding': True,
'batch_sizes': [1, 512, 1024, 2048]
}
}
},
# Model-specific cases with attention_dp disabled to prevent hangs
{
'patterns': [
'deepseek_r1_distill_llama_70b',
'llama_v3.1_nemotron_ultra_253b_fp8-bench-pytorch-float8',
'llama_v3.3_nemotron_super_49b_fp8-bench-pytorch-float8',
'llama_v3.3_nemotron_super_49b-bench-pytorch-bfloat16'
],
'config': {
# True causes hang, needs model-specific fix.
Expand Down Expand Up @@ -465,16 +324,6 @@ def get_model_yaml_config(model_label: str,
},
}
},
# MiniMax-M2.5 FP8: every perf case must route MoE through attention DP.
# TP=8: intermediate_size=1536 is not block-scale divisible (1536/8=192, %128!=0).
# TP=4: trtllm-gen FP8 block-scale MoE kernel IMAs during CUDA-graph warmup
# on the 1536/4=384 N-shard (Blackwell B200/B300).
{
'patterns': ['minimax_m2.5_fp8'],
'config': {
'enable_attention_dp': True,
}
},
# MiniMax-M3 MXFP8 block-sparse MoE: sparse backend, no KV reuse, trust_remote_code, capped max_seq_len to avoid the 1M-default CUDA-graph OOM.
{
'patterns': ['minimax_m3_mxfp8'],
Expand Down
5 changes: 0 additions & 5 deletions tests/integration/defs/perf/test_perf.py
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Expand Up @@ -55,11 +55,6 @@
}

TRUST_REMOTE_CODE_MODELS = { # these models require explicit trust_remote_code=True
"llama_v3.3_nemotron_super_49b",
"llama_v3.3_nemotron_super_49b_fp8",
"llama_v3.1_nemotron_ultra_253b",
"llama_v3.1_nemotron_ultra_253b_fp8",
"kimi_k2_nvfp4",
"kimi_k2.5_fp4",
"minimax_m3_fp4",
"nemotron_3_super_120b_nvfp4",
Expand Down
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