[Bug]: Llama4 on B200 flashinfer produces garbage #28604
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
uv is set
==============================
System Info
==============================
OS : Ubuntu 24.04.3 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version : 18.1.3 (1ubuntu1)
CMake version : version 3.28.3
Libc version : glibc-2.39
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-6.8.0-85-generic-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.8.93
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA B200
GPU 1: NVIDIA B200
GPU 2: NVIDIA B200
GPU 3: NVIDIA B200
GPU 4: NVIDIA B200
GPU 5: NVIDIA B200
GPU 6: NVIDIA B200
GPU 7: NVIDIA B200
Nvidia driver version : 570.195.03
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) PLATINUM 8570
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 2
CPU(s) scaling MHz: 27%
CPU max MHz: 4000.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 600 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] efficientnet-pytorch==0.7.1
[pip3] flashinfer-python==0.5.2
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.16.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.2.1
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] open-clip-torch==2.32.0
[pip3] pytorch-lightning==2.5.2
[pip3] pyzmq==27.1.0
[pip3] segmentation-models-pytorch==0.4.0
[pip3] sentence-transformers==3.2.1
[pip3] terratorch==1.0.2
[pip3] torch==2.9.0+cu129
[pip3] torchaudio==2.9.0+cu129
[pip3] torchgeo==0.7.0
[pip3] torchmetrics==1.7.4
[pip3] torchvision==0.24.0+cu129
[pip3] transformers==4.57.1
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.5.0
[pip3] tritonclient==2.51.0
[pip3] vector-quantize-pytorch==1.21.2
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.1rc7.dev79+ga742134cc.d20251112 (git sha: a742134cc, date: 20251112)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 NIC10 NIC11 NIC12 NIC13NIC14 NIC15 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE NODE NODE PXB NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE NODE NODE NODE NODE NODE PXB NODE NODE SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE NODE NODE NODE NODE NODE NODE PXB NODE SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE NODE NODE NODE NODE NODE NODE PXB SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS PXB NODE NODE NODENODE NODE 56-111,168-223 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE PXB NODE NODE 56-111,168-223 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODEPXB NODE 56-111,168-223 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODENODE PXB 56-111,168-223 1 N/A
NIC0 NODE NODE NODE NODE SYS SYS SYS SYS X PIX PIX PIX NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC1 NODE NODE NODE NODE SYS SYS SYS SYS PIX X PIX PIX NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC2 NODE NODE NODE NODE SYS SYS SYS SYS PIX PIX X PIX NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC3 NODE NODE NODE NODE SYS SYS SYS SYS PIX PIX PIX X NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC4 PXB NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE X NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC5 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE NODE X PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC6 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE NODE PIX X NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC7 NODE PXB NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE NODE X NODE NODE SYS SYS SYS SYS SYS SYS
NIC8 NODE NODE PXB NODE SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE NODE NODE X NODE SYS SYS SYS SYS SYS SYS
NIC9 NODE NODE NODE PXB SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE NODE NODE NODE X SYS SYS SYS SYS SYS SYS
NIC10 SYS SYS SYS SYS PXB NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS X NODE NODE NODENODE NODE
NIC11 SYS SYS SYS SYS NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE X PIX NODENODE NODE
NIC12 SYS SYS SYS SYS NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE PIX X NODENODE NODE
NIC13 SYS SYS SYS SYS NODE PXB NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE X NODE NODE
NIC14 SYS SYS SYS SYS NODE NODE PXB NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE X NODE
NIC15 SYS SYS SYS SYS NODE NODE NODE PXB SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODENODE X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: mlx5_0
NIC1: mlx5_1
NIC2: mlx5_2
NIC3: mlx5_3
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6
NIC7: mlx5_7
NIC8: mlx5_8
NIC9: mlx5_9
NIC10: mlx5_10
NIC11: mlx5_11
NIC12: mlx5_12
NIC13: mlx5_13
NIC14: mlx5_14
NIC15: mlx5_15
==============================
Environment Variables
==============================
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
🐛 Describe the bug
test_fusions_e2e.py::test_attn_quant is broken for llama4. Issue can be reproduced with the following simple command:
# no fusion also broken
python examples/offline_inference/basic/generate.py --model=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 --kv-cache-dtype=fp8 --max-model-len=1024
# original repro
python examples/offline_inference/basic/generate.py --model=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 --kv-cache-dtype=fp8 --max-model-len=1024 -O.enable_noop=true -O.enable_attn_fusion=true -O.use_inductor_partition=true
# works
VLLM_ATTENTION_BACKEND=TRITON_ATTN python examples/offline_inference/basic/generate.py --model=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 --kv-cache-dtype=fp8 --max-model-len=1024 -O.enable_noop=true -O.enable_attn_fusion=true -O.use_inductor_partition=true
Outputs:
--------------------------------------------------
Prompt: 'Hello, my name is'
Generated text: ' Christine and; 디지털을 that. new2ed micbedd: 왔으니\n pack'
--------------------------------------------------
Prompt: 'The president of the United States is'
Generated text: ' the дме,,,,5ccfed rightmostdd* wr ~ Raff'
--------------------------------------------------
Prompt: 'The capital of France is'
Generated text: ' Paris・地図笑着 해봤습니다,ed मुताबarzyseding:superseded_{ vib: 왔으니'
--------------------------------------------------
Prompt: 'The future of AI is'
Generated text: ' here Tail dimension*& 숙소에서,,,, Pathced: 왔으니:海晨'
--------------------------------------------------
The test also sometimes produces an IMA - it happens on #27126 but not on main, not sure why.
Full logs
``` INFO 11-12 19:56:47 [utils.py:253] non-default args: {'kv_cache_dtype': 'fp8', 'max_model_len': 1024, 'num_redundant_experts': None, 'eplb_window_size': None, 'eplb_step_interval': None, 'eplb_log_balancedness': None, 'enable_lora': None, 'reasoning_parser_plugin': '', 'model': 'nvidia/Llama-4-Scout-17B-16E-Instruct-FP8'} INFO 11-12 19:56:47 [model.py:630] Resolved architecture: Llama4ForConditionalGeneration INFO 11-12 19:56:47 [model.py:1735] Using max model len 1024 INFO 11-12 19:56:47 [cache.py:180] Using fp8 data type to store kv cache. It reduces the GPU memory footprint and boosts the performance. Meanwhile, it may cause accuracy drop without a proper scaling factor. INFO 11-12 19:56:48 [scheduler.py:254] Chunked prefill is enabled with max_num_batched_tokens=16384. WARNING 11-12 19:56:48 [modelopt.py:102] Detected ModelOpt fp8 checkpoint. Please note that the format is experimental and could change. WARNING 11-12 19:56:48 [vllm.py:712] There is a latency regression when using chunked local attention with the hybrid KV cache manager. Disabling it, by default. To enable it, set the environment VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1. (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:49 [core.py:94] Initializing a V1 LLM engine (v0.11.1rc7.dev79+ga742134cc.d20251112) with config: model='nvidia/Llama-4-Scout-17B-16E-Instruct-FP8', speculative_config=None, tokenizer='nvidia/Llama-4-Scout-17B-16E-Instruct-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=1024, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=modelopt, enforce_eager=False, kv_cache_dtype=fp8, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8, enable_prefix_caching=True, chunked_prefill_enabled=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::kda_attention', 'vllm::sparse_attn_indexer'], 'compile_mm_encoder': True, 'use_inductor': None, 'compile_sizes': [], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {}, 'max_cudagraph_capture_size': 512, 'local_cache_dir': None} [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0 [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0 [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0 [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0 [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0 [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0 (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:51 [parallel_state.py:1325] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0 (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:57 [gpu_model_runner.py:3047] Starting to load model nvidia/Llama-4-Scout-17B-16E-Instruct-FP8... (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:57 [layer.py:569] MultiHeadAttention attn_backend: AttentionBackendEnum.TORCH_SDPA, use_upstream_fa: False (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:57 [cuda.py:408] Valid backends: ['FLASHINFER', 'TRITON_ATTN'] (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:57 [cuda.py:417] Using FLASHINFER backend. (EngineCore_DP0 pid=1812885) INFO 11-12 19:56:57 [selector.py:186] Using HND KV cache layout for FLASHINFER backend. Loading safetensors checkpoint shards: 0% Completed | 0/26 [00:00 /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvCausalP16VarSeqQ128Kv128PersistentContext.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:41,484 - INFO - cubin_loader.py:209 - flashinfer.jit: Fetching cubin 463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen//fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvVarSeqQ8Kv128StaticSwapsAbForGen.cubin from https://edge.urm.nvidia.com/artifactory/sw-kernelinferencelibrary-public-generic-local/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvVarSeqQ8Kv128StaticSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:41,485 - INFO - cubin_loader.py:81 - flashinfer.jit: Acquired lock for /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvVarSeqQ8Kv128StaticSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:41,645 - INFO - cubin_loader.py:111 - flashinfer.jit: File downloaded successfully: https://edge.urm.nvidia.com/artifactory/sw-kernelinferencelibrary-public-generic-local/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvVarSeqQ8Kv128StaticSwapsAbForGen.cubin -> /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvVarSeqQ8Kv128StaticSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:41,646 - INFO - cubin_loader.py:209 - flashinfer.jit: Fetching cubin 463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen//fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16VarSeqQ8Kv128PersistentSwapsAbForGen.cubin from https://edge.urm.nvidia.com/artifactory/sw-kernelinferencelibrary-public-generic-local/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16VarSeqQ8Kv128PersistentSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:41,646 - INFO - cubin_loader.py:81 - flashinfer.jit: Acquired lock for /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16VarSeqQ8Kv128PersistentSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:41,811 - INFO - cubin_loader.py:111 - flashinfer.jit: File downloaded successfully: https://edge.urm.nvidia.com/artifactory/sw-kernelinferencelibrary-public-generic-local/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16VarSeqQ8Kv128PersistentSwapsAbForGen.cubin -> /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16VarSeqQ8Kv128PersistentSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:42,131 - INFO - cubin_loader.py:209 - flashinfer.jit: Fetching cubin 463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen//fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvCgaVarSeqQ8Kv128StaticSwapsAbForGen.cubin from https://edge.urm.nvidia.com/artifactory/sw-kernelinferencelibrary-public-generic-local/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvCgaVarSeqQ8Kv128StaticSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:42,131 - INFO - cubin_loader.py:81 - flashinfer.jit: Acquired lock for /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvCgaVarSeqQ8Kv128StaticSwapsAbForGen.cubin (EngineCore_DP0 pid=1812885) 2025-11-12 19:58:42,301 - INFO - cubin_loader.py:111 - flashinfer.jit: File downloaded successfully: https://edge.urm.nvidia.com/artifactory/sw-kernelinferencelibrary-public-generic-local/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvCgaVarSeqQ8Kv128StaticSwapsAbForGen.cubin -> /home/ProExpertProg/.cache/flashinfer/cubins/463def7494c9fc6792b5aa5b5beef34025e247ac/fmha/trtllm-gen/fmhaSm100fKernel_QkvE4m3OBfloat16H128PagedKvDenseP16MultiCtasKvCgaVarSeqQ8Kv128StaticSwapsAbForGen.cubin Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51/51 [00:09<00:00, 5.14it/s] Capturing CUDA graphs (decode, FULL): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51/51 [00:08<00:00, 5.90it/s] (EngineCore_DP0 pid=1812885) INFO 11-12 19:59:01 [gpu_model_runner.py:4032] Graph capturing finished in 19 secs, took 5.64 GiB (EngineCore_DP0 pid=1812885) INFO 11-12 19:59:01 [core.py:247] init engine (profile, create kv cache, warmup model) took 88.87 seconds INFO 11-12 19:59:03 [llm.py:353] Supported tasks: ['generate'] WARNING 11-12 19:59:03 [model.py:1558] Default sampling parameters have been overridden by the model's Hugging Face generation config recommended from the model creator. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. Adding requests: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:05<00:00, 1.32s/it] Processed prompts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 16.85it/s, est. speed input: 109.59 toks/s, output: 269.77 toks/s] -------------------------------------------------- Prompt: 'Hello, my name is' Generated text: ' Christine and; 디지털을 that. new2ed micbedd: 왔으니\n pack' -------------------------------------------------- Prompt: 'The president of the United States is' Generated text: ' the дме,,,,5ccfed rightmostdd* wr ~ Raff' -------------------------------------------------- Prompt: 'The capital of France is' Generated text: ' Paris・地図笑着 해봤습니다,ed मुताबarzyseding:superseded_{ vib: 왔으니' -------------------------------------------------- Prompt: 'The future of AI is' Generated text: ' here Tail dimension*& 숙소에서,,,, Pathced: 왔으니:海晨' -------------------------------------------------- ```Before submitting a new issue...
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