vllm - 💡(How to fix) Fix [Bug]: IMA in _causal_conv1d_fwd_kernel for long sequence input [1 participants]

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vllm-project/vllm#40905Fetched 2026-04-27 05:29:24
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When running Mamba-based models (e.g., Mamba or Jamba) with long input sequences, vLLM crashes with a CUDA illegal memory access (IMA) inside the Triton kernel _causal_conv1d_fwd_kernel.

Error Message

Traceback (most recent call last): File "/workspace/./run_vllm.py", line 29, in <module> out = llm.generate([single_prompt]*batch_size, sampling_params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 500, in generate return self._run_completion( ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 1859, in _run_completion (EngineCore pid=2761) Process EngineCore: return self._run_engine(use_tqdm=use_tqdm, output_type=output_type) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 2011, in _run_engine step_outputs = self.llm_engine.step() ^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/llm_engine.py", line 302, in step outputs = self.engine_core.get_output() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core_client.py", line 780, in get_output raise self._format_exception(outputs) from None vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause. (EngineCore pid=2761) Traceback (most recent call last): (EngineCore pid=2761) File "/usr/local/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap (EngineCore pid=2761) self.run() (EngineCore pid=2761) File "/usr/local/lib/python3.11/multiprocessing/process.py", line 108, in run (EngineCore pid=2761) self._target(*self._args, **self._kwargs) (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1112, in run_engine_core (EngineCore pid=2761) raise e (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1101, in run_engine_core (EngineCore pid=2761) engine_core.run_busy_loop() (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1142, in run_busy_loop (EngineCore pid=2761) self._process_engine_step() (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1181, in _process_engine_step (EngineCore pid=2761) outputs, model_executed = self.step_fn() (EngineCore pid=2761) ^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 451, in step_with_batch_queue (EngineCore pid=2761) exec_future = self.model_executor.execute_model( (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 114, in execute_model (EngineCore pid=2761) output.result() (EngineCore pid=2761) File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 449, in result (EngineCore pid=2761) return self.__get_result() (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result (EngineCore pid=2761) raise self._exception (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 84, in collective_rpc (EngineCore pid=2761) result = run_method(self.driver_worker, method, args, kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/serial_utils.py", line 510, in run_method (EngineCore pid=2761) return func(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/worker_base.py", line 332, in execute_model (EngineCore pid=2761) return self.worker.execute_model(scheduler_output) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=2761) return func(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_worker.py", line 803, in execute_model (EngineCore pid=2761) output = self.model_runner.execute_model( (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=2761) return func(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 4034, in execute_model (EngineCore pid=2761) model_output = self._model_forward( (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 3515, in _model_forward (EngineCore pid=2761) return self.model( (EngineCore pid=2761) ^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl (EngineCore pid=2761) return self._call_impl(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl (EngineCore pid=2761) return forward_call(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 234, in forward (EngineCore pid=2761) hidden_states = self.backbone( (EngineCore pid=2761) ^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/compilation/decorators.py", line 467, in call (EngineCore pid=2761) return self.forward(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 162, in forward (EngineCore pid=2761) hidden_states, residual = layer( (EngineCore pid=2761) ^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl (EngineCore pid=2761) return self._call_impl(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl (EngineCore pid=2761) return forward_call(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 97, in forward (EngineCore pid=2761) self.mixer(hidden_states, output) (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl (EngineCore pid=2761) return self._call_impl(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl (EngineCore pid=2761) return forward_call(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 227, in forward (EngineCore pid=2761) torch.ops.vllm.mamba_mixer( (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/torch/_ops.py", line 1209, in call (EngineCore pid=2761) return self._op(*args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 516, in mamba_mixer (EngineCore pid=2761) self.forward_impl(hidden_states=hidden_states, output=output) (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 341, in forward_impl (EngineCore pid=2761) conv_out_p = causal_conv1d_fn( (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/ops/causal_conv1d.py", line 699, in causal_conv1d_fn (EngineCore pid=2761) _causal_conv1d_fwd_kernel[grid]( (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda> (EngineCore pid=2761) return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) (EngineCore pid=2761) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 744, in run (EngineCore pid=2761) kernel.run(grid_0, grid_1, grid_2, stream, kernel.function, kernel.packed_metadata, launch_metadata, (EngineCore pid=2761) File "/usr/local/lib/python3.11/site-packages/triton/backends/nvidia/driver.py", line 713, in call (EngineCore pid=2761) self.launch(gridX, gridY, gridZ, stream, function, self.launch_cooperative_grid, self.launch_pdl, (EngineCore pid=2761) RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered

Root Cause

output:

Traceback (most recent call last):
  File "/workspace/./run_vllm.py", line 29, in <module>
    out = llm.generate([single_prompt]*batch_size, sampling_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 500, in generate
    return self._run_completion(
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 1859, in _run_completion
(EngineCore pid=2761) Process EngineCore:
    return self._run_engine(use_tqdm=use_tqdm, output_type=output_type)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 2011, in _run_engine
    step_outputs = self.llm_engine.step()
                   ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/llm_engine.py", line 302, in step
    outputs = self.engine_core.get_output()
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core_client.py", line 780, in get_output
    raise self._format_exception(outputs) from None
vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
(EngineCore pid=2761) Traceback (most recent call last):
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=2761)     self.run()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/multiprocessing/process.py", line 108, in run
(EngineCore pid=2761)     self._target(*self._args, **self._kwargs)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1112, in run_engine_core
(EngineCore pid=2761)     raise e
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1101, in run_engine_core
(EngineCore pid=2761)     engine_core.run_busy_loop()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1142, in run_busy_loop
(EngineCore pid=2761)     self._process_engine_step()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1181, in _process_engine_step
(EngineCore pid=2761)     outputs, model_executed = self.step_fn()
(EngineCore pid=2761)                               ^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 451, in step_with_batch_queue
(EngineCore pid=2761)     exec_future = self.model_executor.execute_model(
(EngineCore pid=2761)                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 114, in execute_model
(EngineCore pid=2761)     output.result()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 449, in result
(EngineCore pid=2761)     return self.__get_result()
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=2761)     raise self._exception
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 84, in collective_rpc
(EngineCore pid=2761)     result = run_method(self.driver_worker, method, args, kwargs)
(EngineCore pid=2761)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/serial_utils.py", line 510, in run_method
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/worker_base.py", line 332, in execute_model
(EngineCore pid=2761)     return self.worker.execute_model(scheduler_output)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_worker.py", line 803, in execute_model
(EngineCore pid=2761)     output = self.model_runner.execute_model(
(EngineCore pid=2761)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 4034, in execute_model
(EngineCore pid=2761)     model_output = self._model_forward(
(EngineCore pid=2761)                    ^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 3515, in _model_forward
(EngineCore pid=2761)     return self.model(
(EngineCore pid=2761)            ^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 234, in forward
(EngineCore pid=2761)     hidden_states = self.backbone(
(EngineCore pid=2761)                     ^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/compilation/decorators.py", line 467, in __call__
(EngineCore pid=2761)     return self.forward(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 162, in forward
(EngineCore pid=2761)     hidden_states, residual = layer(
(EngineCore pid=2761)                               ^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 97, in forward
(EngineCore pid=2761)     self.mixer(hidden_states, output)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 227, in forward
(EngineCore pid=2761)     torch.ops.vllm.mamba_mixer(
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/_ops.py", line 1209, in __call__
(EngineCore pid=2761)     return self._op(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 516, in mamba_mixer
(EngineCore pid=2761)     self.forward_impl(hidden_states=hidden_states, output=output)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 341, in forward_impl
(EngineCore pid=2761)     conv_out_p = causal_conv1d_fn(
(EngineCore pid=2761)                  ^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/ops/causal_conv1d.py", line 699, in causal_conv1d_fn
(EngineCore pid=2761)     _causal_conv1d_fwd_kernel[grid](
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
(EngineCore pid=2761)     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
(EngineCore pid=2761)                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 744, in run
(EngineCore pid=2761)     kernel.run(grid_0, grid_1, grid_2, stream, kernel.function, kernel.packed_metadata, launch_metadata,
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/backends/nvidia/driver.py", line 713, in __call__
(EngineCore pid=2761)     self.launch(gridX, gridY, gridZ, stream, function, self.launch_cooperative_grid, self.launch_pdl,
(EngineCore pid=2761) RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered

Case 2: Jamba model on B300 Result: Same CUDA illegal memory access

modelId = "ai21labs/AI21-Jamba-Mini-1.5"
batch_size = 5
seq_len = 52436

Fix Action

Fix / Workaround

============================== 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): 240 On-line CPU(s) list: 0-239 Vendor ID: AuthenticAMD Model name: AMD EPYC 9575F 64-Core Processor CPU family: 26 Model: 2 Thread(s) per core: 1 Core(s) per socket: 1 Socket(s): 240 Stepping: 1 BogoMIPS: 6599.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean flushbyasid pausefilter pfthreshold v_vmsave_vmload vgif vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid movdiri movdir64b fsrm avx512_vp2intersect flush_l1d arch_capabilities Virtualization: AMD-V Hypervisor vendor: KVM Virtualization type: full L1d cache: 15 MiB (240 instances) L1i cache: 15 MiB (240 instances) L2 cache: 120 MiB (240 instances) L3 cache: 3.8 GiB (240 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-239 Vulnerability Gather data sampling: Not affected Vulnerability Indirect target selection: 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; STIBP disabled; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected

Code Example

==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.11.14 (main, Mar 12 2026, 12:45:12) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-6.8.0-100-generic-x86_64-with-glibc2.35
    
==============================
       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        : 580.126.09
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0
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):                                  240
On-line CPU(s) list:                     0-239
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 9575F 64-Core Processor
CPU family:                              26
Model:                                   2
Thread(s) per core:                      1
Core(s) per socket:                      1
Socket(s):                               240
Stepping:                                1
BogoMIPS:                                6599.99
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean flushbyasid pausefilter pfthreshold v_vmsave_vmload vgif vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid movdiri movdir64b fsrm avx512_vp2intersect flush_l1d arch_capabilities
Virtualization:                          AMD-V
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               15 MiB (240 instances)
L1i cache:                               15 MiB (240 instances)
L2 cache:                                120 MiB (240 instances)
L3 cache:                                3.8 GiB (240 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-239
Vulnerability Gather data sampling:      Not affected
Vulnerability Indirect target selection: 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; STIBP disabled; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.5.0.dev0
[pip3] nvidia-cutlass-dsl-libs-base==4.5.0.dev0
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] optree==0.19.0
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.56.0
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.6.0
[conda] numpy                     2.4.2                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.19.0.56                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.29.3                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] torch                     2.12.0.dev20260311+cu128          pypi_0    pypi
[conda] torchaudio                2.11.0.dev20260310+cu128          pypi_0    pypi
[conda] torchvision               0.26.0.dev20260311+cu128          pypi_0    pypi
[conda] triton                    3.6.0+git9844da95          pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.19.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    0-239   0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    0-239   0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    0-239   0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    0-239   0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    0-239   0               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    0-239   0               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    0-239   0               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      0-239   0               N/A

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

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
CUDA_VERSION=12.8.1
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/x86_64-linux-gnu
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

---

modelId = "state-spaces/mamba-130m-hf"
batch_size = 1
seq_len = 1399013
single_prompt = "word "*seq_len
single_prompt = single_prompt.strip() 
llm = LLM(model=modelId, trust_remote_code=True, hf_token=HF_TOKEN, 
          attention_config={"backend":"FLASH_ATTN"},
          max_model_len = seq_len
          enforce_eager = True)
sampling_params = SamplingParams(temperature=0)
out = llm.generate([single_prompt]*batch_size, sampling_params)

---

Traceback (most recent call last):
  File "/workspace/./run_vllm.py", line 29, in <module>
    out = llm.generate([single_prompt]*batch_size, sampling_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 500, in generate
    return self._run_completion(
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 1859, in _run_completion
(EngineCore pid=2761) Process EngineCore:
    return self._run_engine(use_tqdm=use_tqdm, output_type=output_type)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 2011, in _run_engine
    step_outputs = self.llm_engine.step()
                   ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/llm_engine.py", line 302, in step
    outputs = self.engine_core.get_output()
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core_client.py", line 780, in get_output
    raise self._format_exception(outputs) from None
vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
(EngineCore pid=2761) Traceback (most recent call last):
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=2761)     self.run()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/multiprocessing/process.py", line 108, in run
(EngineCore pid=2761)     self._target(*self._args, **self._kwargs)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1112, in run_engine_core
(EngineCore pid=2761)     raise e
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1101, in run_engine_core
(EngineCore pid=2761)     engine_core.run_busy_loop()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1142, in run_busy_loop
(EngineCore pid=2761)     self._process_engine_step()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1181, in _process_engine_step
(EngineCore pid=2761)     outputs, model_executed = self.step_fn()
(EngineCore pid=2761)                               ^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 451, in step_with_batch_queue
(EngineCore pid=2761)     exec_future = self.model_executor.execute_model(
(EngineCore pid=2761)                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 114, in execute_model
(EngineCore pid=2761)     output.result()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 449, in result
(EngineCore pid=2761)     return self.__get_result()
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=2761)     raise self._exception
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 84, in collective_rpc
(EngineCore pid=2761)     result = run_method(self.driver_worker, method, args, kwargs)
(EngineCore pid=2761)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/serial_utils.py", line 510, in run_method
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/worker_base.py", line 332, in execute_model
(EngineCore pid=2761)     return self.worker.execute_model(scheduler_output)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_worker.py", line 803, in execute_model
(EngineCore pid=2761)     output = self.model_runner.execute_model(
(EngineCore pid=2761)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 4034, in execute_model
(EngineCore pid=2761)     model_output = self._model_forward(
(EngineCore pid=2761)                    ^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 3515, in _model_forward
(EngineCore pid=2761)     return self.model(
(EngineCore pid=2761)            ^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 234, in forward
(EngineCore pid=2761)     hidden_states = self.backbone(
(EngineCore pid=2761)                     ^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/compilation/decorators.py", line 467, in __call__
(EngineCore pid=2761)     return self.forward(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 162, in forward
(EngineCore pid=2761)     hidden_states, residual = layer(
(EngineCore pid=2761)                               ^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 97, in forward
(EngineCore pid=2761)     self.mixer(hidden_states, output)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 227, in forward
(EngineCore pid=2761)     torch.ops.vllm.mamba_mixer(
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/_ops.py", line 1209, in __call__
(EngineCore pid=2761)     return self._op(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 516, in mamba_mixer
(EngineCore pid=2761)     self.forward_impl(hidden_states=hidden_states, output=output)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 341, in forward_impl
(EngineCore pid=2761)     conv_out_p = causal_conv1d_fn(
(EngineCore pid=2761)                  ^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/ops/causal_conv1d.py", line 699, in causal_conv1d_fn
(EngineCore pid=2761)     _causal_conv1d_fwd_kernel[grid](
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
(EngineCore pid=2761)     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
(EngineCore pid=2761)                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 744, in run
(EngineCore pid=2761)     kernel.run(grid_0, grid_1, grid_2, stream, kernel.function, kernel.packed_metadata, launch_metadata,
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/backends/nvidia/driver.py", line 713, in __call__
(EngineCore pid=2761)     self.launch(gridX, gridY, gridZ, stream, function, self.launch_cooperative_grid, self.launch_pdl,
(EngineCore pid=2761) RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered

---

modelId = "ai21labs/AI21-Jamba-Mini-1.5"
batch_size = 5
seq_len = 52436

single_prompt = "word "*seq_len
single_prompt = single_prompt.strip() 
llm = LLM(model=modelId, trust_remote_code=True, hf_token=HF_TOKEN, 
          attention_config={"backend":"FLASH_ATTN"},
          max_num_batched_tokens=batch_size*seq_len+1,
          enforce_eager = True)

sampling_params = SamplingParams(temperature=0)
out = llm.generate([single_prompt]*batch_size, sampling_params)
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.11.14 (main, Mar 12 2026, 12:45:12) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-6.8.0-100-generic-x86_64-with-glibc2.35
    
==============================
       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        : 580.126.09
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0
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):                                  240
On-line CPU(s) list:                     0-239
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 9575F 64-Core Processor
CPU family:                              26
Model:                                   2
Thread(s) per core:                      1
Core(s) per socket:                      1
Socket(s):                               240
Stepping:                                1
BogoMIPS:                                6599.99
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean flushbyasid pausefilter pfthreshold v_vmsave_vmload vgif vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid movdiri movdir64b fsrm avx512_vp2intersect flush_l1d arch_capabilities
Virtualization:                          AMD-V
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               15 MiB (240 instances)
L1i cache:                               15 MiB (240 instances)
L2 cache:                                120 MiB (240 instances)
L3 cache:                                3.8 GiB (240 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-239
Vulnerability Gather data sampling:      Not affected
Vulnerability Indirect target selection: 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; STIBP disabled; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.5.0.dev0
[pip3] nvidia-cutlass-dsl-libs-base==4.5.0.dev0
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] optree==0.19.0
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.56.0
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.6.0
[conda] numpy                     2.4.2                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.19.0.56                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.29.3                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] torch                     2.12.0.dev20260311+cu128          pypi_0    pypi
[conda] torchaudio                2.11.0.dev20260310+cu128          pypi_0    pypi
[conda] torchvision               0.26.0.dev20260311+cu128          pypi_0    pypi
[conda] triton                    3.6.0+git9844da95          pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.19.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    0-239   0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    0-239   0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    0-239   0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    0-239   0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    0-239   0               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    0-239   0               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    0-239   0               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      0-239   0               N/A

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

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
CUDA_VERSION=12.8.1
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/x86_64-linux-gnu
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
</details>

🐛 Describe the bug

Description

When running Mamba-based models (e.g., Mamba or Jamba) with long input sequences, vLLM crashes with a CUDA illegal memory access (IMA) inside the Triton kernel _causal_conv1d_fwd_kernel.

Minimal reproduction:

Case 1: Mamba model on B200

modelId = "state-spaces/mamba-130m-hf"
batch_size = 1
seq_len = 1399013
single_prompt = "word "*seq_len
single_prompt = single_prompt.strip() 
llm = LLM(model=modelId, trust_remote_code=True, hf_token=HF_TOKEN, 
          attention_config={"backend":"FLASH_ATTN"},
          max_model_len = seq_len
          enforce_eager = True)
sampling_params = SamplingParams(temperature=0)
out = llm.generate([single_prompt]*batch_size, sampling_params)

output:

Traceback (most recent call last):
  File "/workspace/./run_vllm.py", line 29, in <module>
    out = llm.generate([single_prompt]*batch_size, sampling_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 500, in generate
    return self._run_completion(
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 1859, in _run_completion
(EngineCore pid=2761) Process EngineCore:
    return self._run_engine(use_tqdm=use_tqdm, output_type=output_type)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 2011, in _run_engine
    step_outputs = self.llm_engine.step()
                   ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/llm_engine.py", line 302, in step
    outputs = self.engine_core.get_output()
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core_client.py", line 780, in get_output
    raise self._format_exception(outputs) from None
vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
(EngineCore pid=2761) Traceback (most recent call last):
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=2761)     self.run()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/multiprocessing/process.py", line 108, in run
(EngineCore pid=2761)     self._target(*self._args, **self._kwargs)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1112, in run_engine_core
(EngineCore pid=2761)     raise e
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1101, in run_engine_core
(EngineCore pid=2761)     engine_core.run_busy_loop()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1142, in run_busy_loop
(EngineCore pid=2761)     self._process_engine_step()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 1181, in _process_engine_step
(EngineCore pid=2761)     outputs, model_executed = self.step_fn()
(EngineCore pid=2761)                               ^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 451, in step_with_batch_queue
(EngineCore pid=2761)     exec_future = self.model_executor.execute_model(
(EngineCore pid=2761)                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 114, in execute_model
(EngineCore pid=2761)     output.result()
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 449, in result
(EngineCore pid=2761)     return self.__get_result()
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=2761)     raise self._exception
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/executor/uniproc_executor.py", line 84, in collective_rpc
(EngineCore pid=2761)     result = run_method(self.driver_worker, method, args, kwargs)
(EngineCore pid=2761)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/serial_utils.py", line 510, in run_method
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/worker_base.py", line 332, in execute_model
(EngineCore pid=2761)     return self.worker.execute_model(scheduler_output)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_worker.py", line 803, in execute_model
(EngineCore pid=2761)     output = self.model_runner.execute_model(
(EngineCore pid=2761)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(EngineCore pid=2761)     return func(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 4034, in execute_model
(EngineCore pid=2761)     model_output = self._model_forward(
(EngineCore pid=2761)                    ^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py", line 3515, in _model_forward
(EngineCore pid=2761)     return self.model(
(EngineCore pid=2761)            ^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 234, in forward
(EngineCore pid=2761)     hidden_states = self.backbone(
(EngineCore pid=2761)                     ^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/compilation/decorators.py", line 467, in __call__
(EngineCore pid=2761)     return self.forward(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 162, in forward
(EngineCore pid=2761)     hidden_states, residual = layer(
(EngineCore pid=2761)                               ^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/models/mamba.py", line 97, in forward
(EngineCore pid=2761)     self.mixer(hidden_states, output)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
(EngineCore pid=2761)     return self._call_impl(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl
(EngineCore pid=2761)     return forward_call(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 227, in forward
(EngineCore pid=2761)     torch.ops.vllm.mamba_mixer(
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/torch/_ops.py", line 1209, in __call__
(EngineCore pid=2761)     return self._op(*args, **kwargs)
(EngineCore pid=2761)            ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 516, in mamba_mixer
(EngineCore pid=2761)     self.forward_impl(hidden_states=hidden_states, output=output)
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/mamba_mixer.py", line 341, in forward_impl
(EngineCore pid=2761)     conv_out_p = causal_conv1d_fn(
(EngineCore pid=2761)                  ^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/vllm/model_executor/layers/mamba/ops/causal_conv1d.py", line 699, in causal_conv1d_fn
(EngineCore pid=2761)     _causal_conv1d_fwd_kernel[grid](
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
(EngineCore pid=2761)     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
(EngineCore pid=2761)                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/runtime/jit.py", line 744, in run
(EngineCore pid=2761)     kernel.run(grid_0, grid_1, grid_2, stream, kernel.function, kernel.packed_metadata, launch_metadata,
(EngineCore pid=2761)   File "/usr/local/lib/python3.11/site-packages/triton/backends/nvidia/driver.py", line 713, in __call__
(EngineCore pid=2761)     self.launch(gridX, gridY, gridZ, stream, function, self.launch_cooperative_grid, self.launch_pdl,
(EngineCore pid=2761) RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered

Case 2: Jamba model on B300 Result: Same CUDA illegal memory access

modelId = "ai21labs/AI21-Jamba-Mini-1.5"
batch_size = 5
seq_len = 52436

single_prompt = "word "*seq_len
single_prompt = single_prompt.strip() 
llm = LLM(model=modelId, trust_remote_code=True, hf_token=HF_TOKEN, 
          attention_config={"backend":"FLASH_ATTN"},
          max_num_batched_tokens=batch_size*seq_len+1,
          enforce_eager = True)

sampling_params = SamplingParams(temperature=0)
out = llm.generate([single_prompt]*batch_size, sampling_params)

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extent analysis

TL;DR

The issue is likely caused by a CUDA illegal memory access in the Triton kernel _causal_conv1d_fwd_kernel when running Mamba-based models with long input sequences, and can be mitigated by adjusting the sequence length or model configuration.

Guidance

  • Verify that the issue is indeed caused by the CUDA illegal memory access by checking the stacktrace and error message.
  • Try reducing the sequence length (seq_len) to see if the issue persists, as this may help identify if the problem is related to memory allocation or usage.
  • Check the model configuration, particularly the max_model_len and max_num_batched_tokens parameters, to ensure they are set correctly for the given sequence length.
  • Consider updating the Triton library to the latest version, as this may include fixes for similar issues.

Example

No code example is provided, as the issue is related to a specific library and model configuration.

Notes

The issue may be specific to the Mamba-based models and the Triton library, and further investigation may be required to determine the root cause. Additionally, the NVIDIA_VISIBLE_DEVICES environment variable is set to void, which may affect the behavior of the CUDA kernel.

Recommendation

Apply a workaround by reducing the sequence length or adjusting the model configuration to mitigate the CUDA illegal memory access issue. If the issue persists, consider updating the Triton library to the latest version.

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