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[Bug]: ValueError: could not broadcast input array from shape (513,) into shape (512,) #8432

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ndao600 opened this issue Sep 12, 2024 · 7 comments
Open
1 task done

[Bug]: ValueError: could not broadcast input array from shape (513,) into shape (512,) #8432

ndao600 opened this issue Sep 12, 2024 · 7 comments
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bug Something isn't working

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@ndao600
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ndao600 commented Sep 12, 2024

Your current environment

Collecting environment information...
/home/miniconda3/envs/vllm/lib/python3.12/site-packages/torch/cuda/init.py:128: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 2: out of memory (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
return torch._C._cuda_getDeviceCount() > 0
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration:
GPU 0: NVIDIA RTX 4000 Ada Generation
GPU 1: NVIDIA RTX 4000 Ada Generation
GPU 2: NVIDIA RTX 4000 Ada Generation
GPU 3: NVIDIA RTX 4000 Ada Generation
GPU 4: NVIDIA RTX 4000 Ada Generation
GPU 5: NVIDIA RTX 4000 Ada Generation
GPU 6: NVIDIA RTX 4000 Ada Generation
GPU 7: NVIDIA RTX 4000 Ada Generation

Nvidia driver version: 555.99
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Vendor ID: AuthenticAMD
Model name: AMD Ryzen Threadripper 7960X 24-Cores
CPU family: 25
Model: 24
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 1
BogoMIPS: 8387.54
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Virtualization: AMD-V
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 768 KiB (24 instances)
L1i cache: 768 KiB (24 instances)
L2 cache: 24 MiB (24 instances)
L3 cache: 32 MiB (1 instance)
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 Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.68
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.44.2
[pip3] triton==3.0.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi
[conda] nvidia-ml-py 12.560.30 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.68 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
[conda] pyzmq 26.2.0 pypi_0 pypi
[conda] torch 2.4.0 pypi_0 pypi
[conda] torchvision 0.19.0 pypi_0 pypi
[conda] transformers 4.44.2 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS SYS SYS SYS SYS SYS SYS N/A
GPU1 SYS X SYS SYS SYS SYS SYS SYS N/A
GPU2 SYS SYS X SYS SYS SYS SYS SYS N/A
GPU3 SYS SYS SYS X SYS SYS SYS SYS N/A
GPU4 SYS SYS SYS SYS X SYS SYS SYS N/A
GPU5 SYS SYS SYS SYS SYS X SYS SYS N/A
GPU6 SYS SYS SYS SYS SYS SYS X SYS N/A
GPU7 SYS SYS SYS SYS SYS SYS SYS X 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

Model Input Dumps

INFO: 127.0.0.1:47618 - "POST /v1/completions HTTP/1.1" 200 OK
ERROR 09-12 19:15:10 async_llm_engine.py:63] Engine background task failed
ERROR 09-12 19:15:10 async_llm_engine.py:63] Traceback (most recent call last):
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 53, in _log_task_completion
ERROR 09-12 19:15:10 async_llm_engine.py:63] return_value = task.result()
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 939, in run_engine_loop
ERROR 09-12 19:15:10 async_llm_engine.py:63] result = task.result()
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 868, in engine_step
ERROR 09-12 19:15:10 async_llm_engine.py:63] request_outputs = await self.engine.step_async(virtual_engine)
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 345, in step_async
ERROR 09-12 19:15:10 async_llm_engine.py:63] output = await self.model_executor.execute_model_async(
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/executor/distributed_gpu_executor.py", line 177, in execute_model_async
ERROR 09-12 19:15:10 async_llm_engine.py:63] return await self._driver_execute_model_async(execute_model_req)
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/executor/multiproc_gpu_executor.py", line 231, in _driver_execute_model_async
ERROR 09-12 19:15:10 async_llm_engine.py:63] return await self.driver_exec_model(execute_model_req)
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/concurrent/futures/thread.py", line 58, in run
ERROR 09-12 19:15:10 async_llm_engine.py:63] result = self.fn(*self.args, **self.kwargs)
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/worker_base.py", line 303, in execute_model
ERROR 09-12 19:15:10 async_llm_engine.py:63] inputs = self.prepare_input(execute_model_req)
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/multi_step_worker.py", line 164, in prepare_input
ERROR 09-12 19:15:10 async_llm_engine.py:63] kwargs) = self._get_driver_input_and_broadcast(execute_model_req)
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/multi_step_worker.py", line 62, in _get_driver_input_and_broadcast
ERROR 09-12 19:15:10 async_llm_engine.py:63] self.model_runner.prepare_model_input(
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/multi_step_model_runner.py", line 254, in prepare_model_input
ERROR 09-12 19:15:10 async_llm_engine.py:63] frozen_model_input = self._base_model_runner.prepare_model_input(
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 1380, in prepare_model_input
ERROR 09-12 19:15:10 async_llm_engine.py:63] model_input = self._prepare_model_input_tensors(
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 1042, in _prepare_model_input_tensors
ERROR 09-12 19:15:10 async_llm_engine.py:63] return builder.build() # type: ignore
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 741, in build
ERROR 09-12 19:15:10 async_llm_engine.py:63] attn_metadata = self.attn_metadata_builder.build(
ERROR 09-12 19:15:10 async_llm_engine.py:63] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/attention/backends/flash_attn.py", line 467, in build
ERROR 09-12 19:15:10 async_llm_engine.py:63] input_block_tables[i, :len(block_table)] = block_table
ERROR 09-12 19:15:10 async_llm_engine.py:63] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
ERROR 09-12 19:15:10 async_llm_engine.py:63] ValueError: could not broadcast input array from shape (513,) into shape (512,)
Exception in callback functools.partial(<function _log_task_completion at 0x7f81020a6ca0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f80fe6315e0>>)
handle: <Handle functools.partial(<function _log_task_completion at 0x7f81020a6ca0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f80fe6315e0>>)>
Traceback (most recent call last):
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 53, in _log_task_completion
return_value = task.result()
^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 939, in run_engine_loop
result = task.result()
^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 868, in engine_step
request_outputs = await self.engine.step_async(virtual_engine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 345, in step_async
output = await self.model_executor.execute_model_async(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/executor/distributed_gpu_executor.py", line 177, in execute_model_async
return await self._driver_execute_model_async(execute_model_req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/executor/multiproc_gpu_executor.py", line 231, in _driver_execute_model_async
return await self.driver_exec_model(execute_model_req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/worker_base.py", line 303, in execute_model
inputs = self.prepare_input(execute_model_req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/multi_step_worker.py", line 164, in prepare_input
kwargs) = self._get_driver_input_and_broadcast(execute_model_req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/multi_step_worker.py", line 62, in _get_driver_input_and_broadcast
self.model_runner.prepare_model_input(
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/multi_step_model_runner.py", line 254, in prepare_model_input
frozen_model_input = self._base_model_runner.prepare_model_input(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 1380, in prepare_model_input
model_input = self._prepare_model_input_tensors(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 1042, in _prepare_model_input_tensors
return builder.build() # type: ignore
^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 741, in build
attn_metadata = self.attn_metadata_builder.build(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/attention/backends/flash_attn.py", line 467, in build
input_block_tables[i, :len(block_table)] = block_table
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
ValueError: could not broadcast input array from shape (513,) into shape (512,)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "uvloop/cbhandles.pyx", line 63, in uvloop.loop.Handle._run
File "/home/0/miniconda3/envs/vllm/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py", line 65, in _log_task_completion
raise AsyncEngineDeadError(
vllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for the actual cause.
ERROR 09-12 19:15:10 client.py:266] Got Unhealthy response from RPC Server
ERROR 09-12 19:15:10 client.py:412] AsyncEngineDeadError('Background loop is stopped.')

🐛 Describe the bug

The error: "ValueError: could not broadcast input array from shape (513,) into shape (512,)"
It looks like when I use --num-scheduler-step of any values I experience the above message mid-processing. When I remove it, I no longer have the same error.
I have tried to change context length, max tokens, batch size, reinstall vllm, etc.. nothing helps.
Thank you for the help!

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@ndao600 ndao600 added the bug Something isn't working label Sep 12, 2024
@SolitaryThinker
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Fixed in #8340

@ashgold
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ashgold commented Sep 13, 2024 •
edited
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This is the same issue as #8068.
Please refer to the above issue for more information.

I believe this issue was fixed in v0.6.1 (#8340)
What version did you use? It would be nice if you could specify the version when reporting a bug.

@JieChen91
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Hi! I encounter the same error with the latest vllm v0.6.4.

@tail-recursion
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I am also getting the same error with the latest vllm.

@zifeitong
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Can you share the models you're using when you see the error?

Is there a reliable way to reproduce it?

@tail-recursion
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Setting max_seq_len_to_capture=max_model_len fixed it for me.

@SefaZeng
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Using phi-4 meet this error.

@zhuangqh zhuangqh mentioned this issue Feb 20, 2025
Merged
Fei-Guo pushed a commit to kaito-project/kaito that referenced this issue Feb 21, 2025
@zhuangqh
chore: bump vllm to 0.7.2 (#890)
6553330 
**Reason for Change**:

fix this [issue](vllm-project/vllm#8432)

---------

Signed-off-by: jerryzhuang <[email protected]>
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@ashgold @tail-recursion @SolitaryThinker @SefaZeng @JieChen91 @zifeitong @ndao600