search

vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
30.71k stars 4.66k forks source link

[Bug]: RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling cublasLtMatmul with transpose_mat1 t transpose_mat2 n m 9216 n 3398 k 7168 mat1_ld 7168 mat2_ld 7168 result_ld 9216 computeType 68 scaleType 0 #5731

Open medwang1 opened 5 months ago

medwang1 commented 5 months ago

Your current environment

Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.2.0-19ubuntu1) 11.2.0
Clang version: Could not collect
CMake version: version 3.29.5
Libc version: glibc-2.35

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA L40
GPU 1: NVIDIA L40
GPU 2: NVIDIA L40
GPU 3: NVIDIA L40
GPU 4: NVIDIA L40
GPU 5: NVIDIA L40
GPU 6: NVIDIA L40
GPU 7: NVIDIA L40

Nvidia driver version: 535.161.07
cuDNN version: Probably one of the following:
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
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:                      52 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             384
On-line CPU(s) list:                0-383
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 9K84 96-Core Processor
CPU family:                         25
Model:                              17
Thread(s) per core:                 2
Core(s) per socket:                 96
Socket(s):                          2
Stepping:                           0
BogoMIPS:                           5200.03
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 nonstop_tsc cpuid extd_apicid amd_dcm tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ibpb 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 avx512_bf16 clzero xsaveerptr wbnoinvd arat avx512vbmi umip avx512_vbmi2 vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          6 MiB (192 instances)
L1i cache:                          6 MiB (192 instances)
L2 cache:                           192 MiB (192 instances)
L3 cache:                           768 MiB (24 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-191
NUMA node1 CPU(s):                  192-383
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, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, STIBP disabled, 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-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] transformers==4.41.2
[pip3] triton==2.3.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] torch                     2.3.0                    pypi_0    pypi
[conda] transformers              4.41.2                   pypi_0    pypi
[conda] triton                    2.3.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.0.post1
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      NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-191   0               N/A
GPU1    NODE     X      PIX     NODE    SYS     SYS     SYS     SYS     0-191   0               N/A
GPU2    NODE    PIX      X      NODE    SYS     SYS     SYS     SYS     0-191   0               N/A
GPU3    NODE    NODE    NODE     X      SYS     SYS     SYS     SYS     0-191   0               N/A
GPU4    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    192-383 1               N/A
GPU5    SYS     SYS     SYS     SYS     NODE     X      PIX     NODE    192-383 1               N/A
GPU6    SYS     SYS     SYS     SYS     NODE    PIX      X      NODE    192-383 1               N/A
GPU7    SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      192-383 1               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

🐛 Describe the bug

how to deploy model

CUDA_VISIBLE_DEVICES=0 python -m vllm.entrypoints.openai.api_server --model /data/home/model/Yi-1.5-34B-Chat-FP8 -tp=1 --trust-remote-code --gpu-memory-utilization 0.8 --max-model-len 4096 --port 8081 --enable-prefix-caching --quantization fp8 --enforce-eager
from datasets import load_dataset
from transformers import AutoTokenizer

from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig

pretrained_model_dir = "/data/home/model/yi/34b/origin/Yi-1.5-34B-Chat"
quantized_model_dir = "/data/home/model/yi/34b/origin/Yi-1.5-34B-Chat-FP8"

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(512))
examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds]
examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda")

quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static")

model = AutoFP8ForCausalLM.from_pretrained(
    pretrained_model_dir, quantize_config=quantize_config
)
model.quantize(examples)
model.save_quantized(quantized_model_dir)

I use the fp8 model Yi-1.5-34B-Chat-FP8 generated by the above python script. Then I have a pressure test with concurrency 128. Then have a error log as the below:

ERROR 06-21 14:10:32 async_llm_engine.py:52] Engine background task failed
ERROR 06-21 14:10:32 async_llm_engine.py:52] Traceback (most recent call last):
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 42, in _log_task_completion
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return_value = task.result()
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 532, in run_engine_loop
ERROR 06-21 14:10:32 async_llm_engine.py:52]     has_requests_in_progress = await asyncio.wait_for(
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/asyncio/tasks.py", line 445, in wait_for
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return fut.result()
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 506, in engine_step
ERROR 06-21 14:10:32 async_llm_engine.py:52]     request_outputs = await self.engine.step_async()
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 235, in step_async
ERROR 06-21 14:10:32 async_llm_engine.py:52]     output = await self.model_executor.execute_model_async(
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 117, in execute_model_async
ERROR 06-21 14:10:32 async_llm_engine.py:52]     output = await make_async(self.driver_worker.execute_model
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/concurrent/futures/thread.py", line 58, in run
ERROR 06-21 14:10:32 async_llm_engine.py:52]     result = self.fn(*self.args, **self.kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return func(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/worker/worker.py", line 280, in execute_model
ERROR 06-21 14:10:32 async_llm_engine.py:52]     output = self.model_runner.execute_model(seq_group_metadata_list,
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return func(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 749, in execute_model
ERROR 06-21 14:10:32 async_llm_engine.py:52]     hidden_states = model_executable(
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return self._call_impl(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return forward_call(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 371, in forward
ERROR 06-21 14:10:32 async_llm_engine.py:52]     hidden_states = self.model(input_ids, positions, kv_caches,
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return self._call_impl(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return forward_call(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 288, in forward
ERROR 06-21 14:10:32 async_llm_engine.py:52]     hidden_states, residual = layer(
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return self._call_impl(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return forward_call(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 227, in forward
ERROR 06-21 14:10:32 async_llm_engine.py:52]     hidden_states = self.self_attn(
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return self._call_impl(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return forward_call(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 158, in forward
ERROR 06-21 14:10:32 async_llm_engine.py:52]     qkv, _ = self.qkv_proj(hidden_states)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return self._call_impl(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
ERROR 06-21 14:10:32 async_llm_engine.py:52]     return forward_call(*args, **kwargs)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/layers/linear.py", line 298, in forward
ERROR 06-21 14:10:32 async_llm_engine.py:52]     output_parallel = self.quant_method.apply(self, input_, bias)
ERROR 06-21 14:10:32 async_llm_engine.py:52]   File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/fp8.py", line 283, in apply
ERROR 06-21 14:10:32 async_llm_engine.py:52]     output, _ = torch._scaled_mm(
ERROR 06-21 14:10:32 async_llm_engine.py:52] RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling cublasLtMatmul with transpose_mat1 t transpose_mat2 n m 9216 n 3398 k 7168 mat1_ld 7168 mat2_ld 7168 result_ld 9216 computeType 68 scaleType 0
Exception in callback functools.partial(<function _log_task_completion at 0x7f5157c375b0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f513fea2170>>)
handle: <Handle functools.partial(<function _log_task_completion at 0x7f5157c375b0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f513fea2170>>)>
Traceback (most recent call last):
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 42, in _log_task_completion
    return_value = task.result()
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 532, in run_engine_loop
    has_requests_in_progress = await asyncio.wait_for(
  File "/data/home/.conda/envs/vllm/lib/python3.10/asyncio/tasks.py", line 445, in wait_for
    return fut.result()
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 506, in engine_step
    request_outputs = await self.engine.step_async()
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 235, in step_async
    output = await self.model_executor.execute_model_async(
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 117, in execute_model_async
    output = await make_async(self.driver_worker.execute_model
  File "/data/home/.conda/envs/vllm/lib/python3.10/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/worker/worker.py", line 280, in execute_model
    output = self.model_runner.execute_model(seq_group_metadata_list,
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 749, in execute_model
    hidden_states = model_executable(
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 371, in forward
    hidden_states = self.model(input_ids, positions, kv_caches,
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 288, in forward
    hidden_states, residual = layer(
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 227, in forward
    hidden_states = self.self_attn(
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 158, in forward
    qkv, _ = self.qkv_proj(hidden_states)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/layers/linear.py", line 298, in forward
    output_parallel = self.quant_method.apply(self, input_, bias)
  File "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/fp8.py", line 283, in apply
    output, _ = torch._scaled_mm(
RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling cublasLtMatmul with transpose_mat1 t transpose_mat2 n m 9216 n 3398 k 7168 mat1_ld 7168 mat2_ld 7168 result_ld 9216 computeType 68 scaleType 0

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 "/data/home/.conda/envs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 54, 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 theactual cause.
github-actions[bot] commented 1 month ago

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!