[Bug]: llama 4 family is incompatible with CUDA graph FULL_AND_PIECEWISE mode #25960
Description
Your current environment
The output of python collect_env.py
==============================
System Info
==============================
OS : Ubuntu 24.04.3 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.39
==============================
PyTorch Info
==============================
PyTorch version : 2.8.0+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-5.14.0-284.118.1.el9_2.x86_64-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3
Nvidia driver version : 570.148.08
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): 64
On-line CPU(s) list: 0-63
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6430
CPU family: 6
Model: 143
Thread(s) per core: 1
Core(s) per socket: 32
Socket(s): 2
Stepping: 8
CPU(s) scaling MHz: 76%
CPU max MHz: 3400.0000
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 ds_cpl 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 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 128 MiB (64 instances)
L3 cache: 120 MiB (2 instances)
NUMA node(s): 8
NUMA node0 CPU(s): 0-7
NUMA node1 CPU(s): 8-15
NUMA node2 CPU(s): 16-23
NUMA node3 CPU(s): 24-31
NUMA node4 CPU(s): 32-39
NUMA node5 CPU(s): 40-47
NUMA node6 CPU(s): 48-55
NUMA node7 CPU(s): 56-63
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 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] flashinfer-python==0.3.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==2.2.6
[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-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-ml-py==13.580.82
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.3.24
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0+cu129
[pip3] torchaudio==2.8.0+cu129
[pip3] torchvision==0.23.0+cu129
[pip3] transformers==4.56.2
[pip3] triton==3.4.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.0rc2.dev73+gfa7e254a7 (git sha: fa7e254a7)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX SYS SYS SYS SYS 0-7 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 PXB SYS SYS SYS SYS 0-7 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 SYS PXB NODE SYS SYS 16-23 2 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 SYS PIX NODE SYS SYS 16-23 2 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS PXB SYS 32-39 4 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS PIX SYS 32-39 4 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS PXB 48-55 6 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS PIX 48-55 6 N/A
NIC0 PIX PXB SYS SYS SYS SYS SYS SYS X SYS SYS SYS SYS
NIC1 SYS SYS PXB PIX SYS SYS SYS SYS SYS X NODE SYS SYS
NIC2 SYS SYS NODE NODE SYS SYS SYS SYS SYS NODE X SYS SYS
NIC3 SYS SYS SYS SYS PXB PIX SYS SYS SYS SYS SYS X SYS
NIC4 SYS SYS SYS SYS SYS SYS PXB PIX SYS SYS SYS SYS 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
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=/var/run/nvidia-container-devices
VLLM_USE_DEEP_GEMM=1
NVIDIA_GDRCOPY=enabled
LD_LIBRARY_PATH=/app/.venv/lib/python3.12/site-packages/nvidia/nvshmem/lib:/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:/usr/lib
VLLM_NO_USAGE_STATS=1
VLLM_LOGGING_LEVEL=INFO
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
VLLM_WORKER_MULTIPROC_METHOD=spawn
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
Phenomenon
For the current main(0.11.0rc2.dev73+gfa7e254a7), llama 4 model family suffers from gibberish outputs for long context (beyond torch.compile length, that is):
# Launch llama 4 maverick server with --max-num-batched-tokens 16K
vllm serve meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 \
--tensor-parallel-size 8 \
--max-model-len 512K \
--max-num-batched-tokens 16K \
--max-num-seqs 16 \
--enable-auto-tool-choice \
--tool-call-parser pythonic \
--mm-encoder-tp-mode data \
--limit-mm-per-prompt {"image":5} import json
import uuid
from transformers import AutoTokenizer
from openai import OpenAI
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8")
print("\n====needle-in-a-haystack test====\n")
# Test NIAH type problem with input length 8K and 32K, resp.
for context_len in [8 * 1024, 32 * 1024]:
uuid_puzzle = {str(uuid.uuid4()): str(uuid.uuid4())}
uuid_q, uuid_a = next(iter(uuid_puzzle.items()))
puzzle_template = "JSON data:\n{uuid_puzzle}\nQ: \nKey: \"{uuid_q}\"\nThe value associated with the specified key is: "
messages = [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": puzzle_template.format(uuid_q=uuid_q, uuid_puzzle=json.dumps(uuid_puzzle))}]
while len(tokenizer.apply_chat_template(messages)) < context_len:
for _ in range(8):
uuid_puzzle[str(uuid.uuid4())] = str(uuid.uuid4())
messages = [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": puzzle_template.format(uuid_q=uuid_q, uuid_puzzle=json.dumps(uuid_puzzle))}]
print(f"{context_len=}, messages:", json.dumps(messages, indent=2, f"{uuid_q=}, {uuid_a=}", sep="\n\n")
for t in client.chat.completions.create(model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", messages=messages, stream=True):
if t.choices:
d = t.choices[0].delta
print(getattr(d, "reasoning_content", "") or d.content or "", end="", flush=True)
print("\n\n")For input length 8K, the model answers correctly:
...
\"727302a0-f517-4305-937c-e0f1d47f9306\"\nThe value associated with the specified key is: "
}
]
uuid_q='727302a0-f517-4305-937c-e0f1d47f9306', uuid_a='0b0693fd-72a6-4e56-ab2f-a39eba5912b8'
The value associated with the key "727302a0-f517-4305-937c-e0f1d47f9306" is "0b0693fd-72a6-4e56-ab2f-a39eba5912b8".
But for input length 32K, the model generates gibberish instead:
...
7f-4296-a474-e4be8c97be5b\"}\nQ: \nKey: \"9b28c3c0-ac2c-4b5f-aa84-4e6c4822e83e\"\nThe value associated with the specified key is: "
}
]
uuid_q='9b28c3c0-ac2c-4b5f-aa84-4e6c4822e83e', uuid_a='e237febc-1566-4ef0-b295-1a0b7d761064'
The8b0c7d4d0b9d9f86d3e4e4d3e4b1e7d5e5e8e3e4e3a4d3e3f8e4e1e6d8f9f0e0e6c1e1f0e1e5f...
Analysis
After #25444, the default CUDA graph mode for most models has been changed from PIECEWISE to FULL_AND_PIECEWISE. This works okay for most other models, but for llama 4 family it seems not. We can recover the original model performance by setting --compilation-config {"cudagraph_mode":"PIECEWISE"}:
modified launch script
vllm serve meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 \
--tensor-parallel-size 8 \
--max-model-len 512K \
--max-num-batched-tokens 16K \
--max-num-seqs 16 \
--enable-auto-tool-choice \
--tool-call-parser pythonic \
--mm-encoder-tp-mode data \
--limit-mm-per-prompt {"image":5} \
--compilation-config {"cudagraph_mode":"PIECEWISE"}result
...
}
]
uuid_q='24c088f3-722d-422a-92e3-6ff3b755b9ff', uuid_a='99d79a2f-dec9-4aa3-ac1e-da1fb28c7006'
To find the value associated with the key "24c088f3-722d-422a-92e3-6ff3b755b9ff" in the given JSON data, we need to look at the JSON object provided.
The JSON data is:
{
"24c088f3-722d-422a-92e3-6ff3b755b9ff": "99d79a2f-dec9-4aa3-ac1e-da1fb28c7006",
"c524b637-e8cb-453b-a655-4e875ed5c305": "5963aaf6-5b61-468b-b601-b7d5f9b6954b",
...
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