[Bug]: Increased VRAM usage since v0.6.4.post1 (vs v0.6.3.post1) [OOM][KV cache]

Your current environment

The output of `python collect_env.py`
Your output of `python collect_env.py` here

Model Input Dumps
No response
🐛 Describe the bug
Hello,
I have a hard time understanding why the newer release make the hardware almost useless with such abysmal context size, if it wasn't for the older version i'd consider giving up.
So just in case it's a bug and not a feature, i tried to show the most informations i can in this comparaison.
If needed i can test anything else.
Thanks
Hardware

GPU : NVIDIA A40-48Q
VRAM : 48GB
CUDA Version : 12.2
Driver Version : 535.161.08
OS :NAME="Red Hat Enterprise Linux"
VERSION="8.10 (Ootpa)"
ID="rhel"
ID_LIKE="fedora"
VERSION_ID="8.10"
PLATFORM_ID="platform:el8"
PRETTY_NAME="Red Hat Enterprise Linux 8.10 (Ootpa)"
ANSI_COLOR="0;31"
CPE_NAME="cpe:/o:redhat:enterprise_linux:8::baseos"
HOME_URL="https://www.redhat.com/"
DOCUMENTATION_URL="https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/8"
BUG_REPORT_URL="https://issues.redhat.com/"

REDHAT_BUGZILLA_PRODUCT="Red Hat Enterprise Linux 8"
REDHAT_BUGZILLA_PRODUCT_VERSION=8.10
REDHAT_SUPPORT_PRODUCT="Red Hat Enterprise Linux"
REDHAT_SUPPORT_PRODUCT_VERSION="8.10"

Test 1
Test 1.1 : Version v0.6.3.post1

model : Qwen-Qwen2.5-72B-Instruct-GPTQ-Int4
max-model-len : 8192
gpu-memory-utilization : 0.95
enforce-eager : true
vram usage : 43787MiB / 49152MiB

Test 1.2 : Version v0.6.3.post1

model : Qwen-Qwen2.5-72B-Instruct-GPTQ-Int4
max-model-len : 8192
gpu-memory-utilization : 0.95
enforce-eager : false <-------------------------
vram usage : 44362MiB / 49152MiB (nvtop 47.206Gi/48.000Gi)

Test 1 : collect_env.py (on test 1.1 configuration)

## Test 1 : collect_env.py (on test 1.1 configuration)
Collecting environment information...
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: Could not collect
Libc version: glibc-2.35

Python version: 3.12.7 (main, Oct  1 2024, 08:52:12) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-4.18.0-553.16.1.el8_10.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A40-48Q
Nvidia driver version: 535.161.08
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:                        45 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               4
On-line CPU(s) list:                  0-3
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Gold 6354 CPU @ 3.00GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   1
Core(s) per socket:                   1
Socket(s):                            4
Stepping:                             7
BogoMIPS:                             5985.93
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Hypervisor vendor:                    VMware
Virtualization type:                  full
L1d cache:                            192 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             5 MiB (4 instances)
L3 cache:                             156 MiB (4 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-3
Vulnerability Gather data sampling:   Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] flashinfer==0.1.6+cu121torch2.4
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==<IP_ADDRESS>
[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==<IP_ADDRESS>
[pip3] nvidia-cufft-cu12==<IP_ADDRESS>
[pip3] nvidia-curand-cu12==<IP_ADDRESS>
[pip3] nvidia-cusolver-cu12==<IP_ADDRESS>
[pip3] nvidia-cusparse-cu12==<IP_ADDRESS>
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] triton==3.0.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.3.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-3     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

NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.4.1
NVIDIA_DISABLE_REQUIRE=1
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
CUDA_MODULE_LOADING=LAZY

Test 2
Test 2.1 : Version v0.6.4.post1

model : Qwen-Qwen2.5-72B-Instruct-GPTQ-Int4

max-model-len : 8192

gpu-memory-utilization : 0.95

enforce-eager : true

vram usage : OOM
INFO 12-16 02:38:24 model_runner.py:1077] Loading model weights took 38.5260 GB
INFO 12-16 02:38:40 worker.py:232] Memory profiling results: total_gpu_memory=47.74GiB initial_memory_usage=42.62GiB peak_torch_memory=40.87GiB memory_usage_post_profile=42.63GiB non_torch_memory=4.09GiB kv_cache_size=0.39GiB gpu_memory_utilization=0.95
INFO 12-16 02:38:41 gpu_executor.py:113] # GPU blocks: 80, # CPU blocks: 819
INFO 12-16 02:38:41 gpu_executor.py:117] Maximum concurrency for 8192 tokens per request: 0.16x
ERROR 12-16 02:38:41 engine.py:366] The model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (1280). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
ERROR 12-16 02:38:41 engine.py:366] Traceback (most recent call last):
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 357, in run_mp_engine
ERROR 12-16 02:38:41 engine.py:366]     engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
ERROR 12-16 02:38:41 engine.py:366]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 119, in from_engine_args
ERROR 12-16 02:38:41 engine.py:366]     return cls(ipc_path=ipc_path,
ERROR 12-16 02:38:41 engine.py:366]            ^^^^^^^^^^^^^^^^^^^^^^
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 71, in __init__
ERROR 12-16 02:38:41 engine.py:366]     self.engine = LLMEngine(*args, **kwargs)
ERROR 12-16 02:38:41 engine.py:366]                   ^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 350, in __init__
ERROR 12-16 02:38:41 engine.py:366]     self._initialize_kv_caches()
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 500, in _initialize_kv_caches
ERROR 12-16 02:38:41 engine.py:366]     self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/executor/gpu_executor.py", line 120, in initialize_cache
ERROR 12-16 02:38:41 engine.py:366]     self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 268, in initialize_cache
ERROR 12-16 02:38:41 engine.py:366]     raise_if_cache_size_invalid(num_gpu_blocks,
ERROR 12-16 02:38:41 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 498, in raise_if_cache_size_invalid
ERROR 12-16 02:38:41 engine.py:366]     raise ValueError(
ERROR 12-16 02:38:41 engine.py:366] ValueError: The model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (1280). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
Process SpawnProcess-1:
Traceback (most recent call last):
  File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 368, in run_mp_engine
    raise e
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 357, in run_mp_engine
    engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 119, in from_engine_args
    return cls(ipc_path=ipc_path,
          ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 71, in __init__
    self.engine = LLMEngine(*args, **kwargs)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 350, in __init__
    self._initialize_kv_caches()
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 500, in _initialize_kv_caches
    self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
  File "/usr/local/lib/python3.12/dist-packages/vllm/executor/gpu_executor.py", line 120, in initialize_cache
    self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
  File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 268, in initialize_cache
    raise_if_cache_size_invalid(num_gpu_blocks,
  File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 498, in raise_if_cache_size_invalid
    raise ValueError(
ValueError: The model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (1280). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
Task exception was never retrieved
future: <Task finished name='Task-2' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-3' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-4' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-5' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-6' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 643, in <module>
    uvloop.run(run_server(args))
  File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 109, in run
    return __asyncio.run(
          ^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/asyncio/runners.py", line 194, in run
    return runner.run(main)
          ^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
  File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 61, in wrapper
    return await main
          ^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 609, in run_server
    async with build_async_engine_client(args) as engine_client:
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__
    return await anext(self.gen)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 113, in build_async_engine_client
    async with build_async_engine_client_from_engine_args(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__
    return await anext(self.gen)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 210, in build_async_engine_client_from_engine_args
    raise RuntimeError(
RuntimeError: Engine process failed to start. See stack trace for the root cause.

Test 2.2 : Version v0.6.4.post1

model : Qwen-Qwen2.5-72B-Instruct-GPTQ-Int4

max-model-len : 8192

gpu-memory-utilization : 0.95

enforce-eager : false <-------------------------

vram usage : OOM
INFO 12-16 02:41:46 model_runner.py:1077] Loading model weights took 38.5260 GB
INFO 12-16 02:42:02 worker.py:232] Memory profiling results: total_gpu_memory=47.74GiB initial_memory_usage=42.62GiB peak_torch_memory=40.87GiB memory_usage_post_profile=42.63GiB non_torch_memory=4.09GiB kv_cache_size=0.39GiB gpu_memory_utilization=0.95
INFO 12-16 02:42:02 gpu_executor.py:113] # GPU blocks: 80, # CPU blocks: 819
INFO 12-16 02:42:02 gpu_executor.py:117] Maximum concurrency for 8192 tokens per request: 0.16x
ERROR 12-16 02:42:02 engine.py:366] The model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (1280). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
ERROR 12-16 02:42:02 engine.py:366] Traceback (most recent call last):
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 357, in run_mp_engine
ERROR 12-16 02:42:02 engine.py:366]     engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
ERROR 12-16 02:42:02 engine.py:366]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 119, in from_engine_args
ERROR 12-16 02:42:02 engine.py:366]     return cls(ipc_path=ipc_path,
ERROR 12-16 02:42:02 engine.py:366]            ^^^^^^^^^^^^^^^^^^^^^^
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 71, in __init__
ERROR 12-16 02:42:02 engine.py:366]     self.engine = LLMEngine(*args, **kwargs)
ERROR 12-16 02:42:02 engine.py:366]                   ^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 350, in __init__
ERROR 12-16 02:42:02 engine.py:366]     self._initialize_kv_caches()
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 500, in _initialize_kv_caches
ERROR 12-16 02:42:02 engine.py:366]     self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/executor/gpu_executor.py", line 120, in initialize_cache
ERROR 12-16 02:42:02 engine.py:366]     self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 268, in initialize_cache
ERROR 12-16 02:42:02 engine.py:366]     raise_if_cache_size_invalid(num_gpu_blocks,
ERROR 12-16 02:42:02 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 498, in raise_if_cache_size_invalid
ERROR 12-16 02:42:02 engine.py:366]     raise ValueError(
ERROR 12-16 02:42:02 engine.py:366] ValueError: The model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (1280). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
Process SpawnProcess-1:
Traceback (most recent call last):
  File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 368, in run_mp_engine
    raise e
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 357, in run_mp_engine
    engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 119, in from_engine_args
    return cls(ipc_path=ipc_path,
          ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 71, in __init__
    self.engine = LLMEngine(*args, **kwargs)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 350, in __init__
    self._initialize_kv_caches()
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 500, in _initialize_kv_caches
    self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
  File "/usr/local/lib/python3.12/dist-packages/vllm/executor/gpu_executor.py", line 120, in initialize_cache
    self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
  File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 268, in initialize_cache
    raise_if_cache_size_invalid(num_gpu_blocks,
  File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 498, in raise_if_cache_size_invalid
    raise ValueError(
ValueError: The model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (1280). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
[rank0]:[W1216 02:42:02.843787431 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())
Task exception was never retrieved
future: <Task finished name='Task-2' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-3' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-4' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-5' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Task exception was never retrieved
future: <Task finished name='Task-6' coro=<MQLLMEngineClient.run_output_handler_loop() done, defined at /usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py:178> exception=ZMQError('Operation not supported')>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/client.py", line 184, in run_output_handler_loop
    while await self.output_socket.poll(timeout=VLLM_RPC_TIMEOUT
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/zmq/_future.py", line 400, in poll
    raise _zmq.ZMQError(_zmq.ENOTSUP)
zmq.error.ZMQError: Operation not supported
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 643, in <module>
    uvloop.run(run_server(args))
  File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 109, in run
    return __asyncio.run(
          ^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/asyncio/runners.py", line 194, in run
    return runner.run(main)
          ^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
  File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 61, in wrapper
    return await main
          ^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 609, in run_server
    async with build_async_engine_client(args) as engine_client:
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__
    return await anext(self.gen)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 113, in build_async_engine_client
    async with build_async_engine_client_from_engine_args(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__
    return await anext(self.gen)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 210, in build_async_engine_client_from_engine_args
    raise RuntimeError(
RuntimeError: Engine process failed to start. See stack trace for the root cause.

Test 2 : collect_env.py (on test 2.1 configuration with reduced max-model-len)

## Test 2 : collect_env.py (on test 2.1 configuration with reduced max-model-len)
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
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: Could not collect
Libc version: glibc-2.35

Python version: 3.12.7 (main, Oct  1 2024, 08:52:12) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-4.18.0-553.16.1.el8_10.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A40-48Q
Nvidia driver version: 535.161.08
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:                        45 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               4
On-line CPU(s) list:                  0-3
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Gold 6354 CPU @ 3.00GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   1
Core(s) per socket:                   1
Socket(s):                            4
Stepping:                             7
BogoMIPS:                             5985.93
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Hypervisor vendor:                    VMware
Virtualization type:                  full
L1d cache:                            192 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             5 MiB (4 instances)
L3 cache:                             156 MiB (4 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-3
Vulnerability Gather data sampling:   Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] flashinfer==0.1.6+cu121torch2.4
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==<IP_ADDRESS>
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==<IP_ADDRESS>
[pip3] nvidia-cufft-cu12==<IP_ADDRESS>
[pip3] nvidia-curand-cu12==<IP_ADDRESS>
[pip3] nvidia-cusolver-cu12==<IP_ADDRESS>
[pip3] nvidia-cusparse-cu12==<IP_ADDRESS>
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.0
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.46.2
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.4.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-3     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

NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.4.1
NVIDIA_DISABLE_REQUIRE=1
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
CUDA_MODULE_LOADING=LAZY

Before submitting a new issue...

[X] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

Can you try again using the latest code on main branch? #10511 might fix this.

You can read more about chunked prefill here

@githebs #10511 changed the --gpu-memory-utilization flag to be per-model instead of a global limit, so if you set --gpu-memory-utilization=0.95 but more than 5% of your vram is already consumed by something else, it'll OOM when trying to write the KV cache, which you can see is happening here from your stack trace:
ERROR 12-17 00:48:42 engine.py:366]   File "/usr/local/lib/python3.12/dist-packages/vllm/worker/cache_engine.py", line 62, in __init__
ERROR 12-17 00:48:42 engine.py:366]     self.gpu_cache = self._allocate_kv_cache(
ERROR 12-17 00:48:42 engine.py:366]                      ^^^^^^^^^^^^^^^^^^^^^^^^

Can you try again with a lower --gpu-memory-utilization value?
Maybe we can add an up-front check to make sure that there's enough free vram before loading so we can raise a nicer error than just an OOM


French problems

I'm getting several problems with words with accents. For example the word "Allô", I get a low score threshold if I write just "allo" without accent.
How can I improve situations like this one?

@rubentic Please help with the link of document on which above document feedback request is created.
In case your query in not related to document content, open a new forum thread in MSDN forum or Stack Overflow as it could be better place to get help on your scenario. These forum community members could provide their expert guidance on your scenario based on their experience. Thanks.

@rubentlc  Since we have not heard back from you we will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly reopen the issue and continue the discussion.


Lab - Implement and manage storage for AVD (AD DS)

Exercise 1, Task 3, Step 9
Description of issue
The UI no longer shows Active Directory and Configured.

It now shows as per below

Hi Steve,
resolved
rgds
Marcin


kue-dashboard

Is there a possibility to view and manage the jobs with kue-dashboard?

Hi, yes it should work the same as with just Kue.
Once, you start adonis-kue with the ace command, you can use the same process to connect to the user interface.
https://github.com/Automattic/kue#user-interface
Let me know if that helps!

Thanks @nrempel! I have added an npm script to launch this for me (with dotenv-cli to get env vars)
{
  "kue": "./ace kue:listen",
  "kue:inspect": "node_modules/kue/bin/kue-dashboard -p $(dotenv -p KUE_INSPECT_PORT) -r redis://$(dotenv -p REDIS_HOSTNAME):$(dotenv -p REDIS_PORT) -q prefix"
}

Thanks, @nrempel. I tried this a few weeks ago, and kue-dashboard hasn't displayed any data. I will try it next week again.

@dspangenberg It could be because adonis-kue removes jobs after they've completed, so they're just going straight through and you never see them in the interface.
Try queueing up some jobs before turning on the kue listener, then you should be able to see them in the kue queue interface, then when you turn the listener on it should process them and they'll be removed from kue

This is true, I have it set to remove jobs after completion because I ran into issues with running out of memory.
I'm open to suggestions on how to handle this in a way that keeps the UI available.

You should also now be able to set removeOnComplete to false so that they remain in the UI.
Just be aware that you can use up your memory if you are queueing a lot of messages.
https://github.com/nrempel/adonis-kue#dispatching-jobs


[FEATURE] Android support

Thanks for taking the time to write a feature request! Use the following prompts to help you describe what you're looking for.  The more info you provide, the easier it'll be for me to understand what I can do to make it happen for you without having to come back and ask you questions.
Is your feature request related to a problem? Please describe.
It'll be great to be able to use mobile device as a NAM host. Amp rack is a great alternative, which can run the lv2 version for linux, but I have issues with IR loader there (it bitcrushes the sound for some reason).
Describe the solution you'd like
NAM as a standalone app for android devices
Describe alternatives you've considered
Amp rack
Additional context
Add any other context or screenshots about the feature request here.

[Transferred from the trainer repo, which is separate from the plugin]
Unfortunately, "Android" is a really broad thing to build for. There are a lot of Android devices that just don't do real-time audio well enough for this to be useful.
The other challenge here is that iPlug2 (the framework this plugin is built using) doesn't have anything for building for Android (to my knowledge), so this would be an entirely different project to do that.
Based on what I know right now, I'm not going to be taking this on in this repo.


Bin-Cheng-THU/DEMBod is no longer available

README.md:110 References github.com/Bin-Cheng-THU/DEMBod which is no longer available.
[Discrete Element Model Body (DEMBody)](https://github.com/Bin-Cheng-THU/DEMBody): software for Granular Dynamics Simulation for Planetary Science and Geophysics, by Bin Cheng. It incorporates classical granular dynamics simulations and N-body self-gravity calculation.

remoevd


LinkaLatte should be a submodule

@jsmecham is working on it now, but as soon as he's done we need to convert it.

I'll coordinate this with @jsmecham.

Fixed in 210fae3158c45bcfe4b75e9051190fe67b08b5f2


we need a dropdown interface to extend some element!

we need a dropdown interface to extend some element!
such like components of combobox,picker,menu,select

i found a  could instead of iron-dropdown. but it seems not professor design for the dropdown

Hello, the element that you are looking for is indeed mwc-menu-surface, but it is not a component that we want to support for external usage (hence why it is not documented). Feel free to use it, but beware that it might break.
Also, we do not want to support it as it is not a component that is explicitly defined by the material spec.


Version mismatch between library and pretrained model?

Describe the bug
I installed the most recent nussl version (1.1.3) using pip today and tried to run the Deep Clustering example, which first downloads the model dpcl-wsj2mix-model.pth.
This is the error message:
Traceback (most recent call last):
  File "explore_nussl_embeddings.py", line 25, in <module>
    separator = nussl.separation.deep.DeepClustering(
  File "/home/verena/miniconda3/lib/python3.8/site-packages/nussl/separation/deep/deep_clustering.py", line 36, in __init__
    self.load_model(model_path, device=device)
  File "/home/verena/miniconda3/lib/python3.8/site-packages/nussl/separation/base/deep_mixin.py", line 35, in load_model
    model_dict = safe_loader.load(model_path, 'cpu')
  File "/home/verena/miniconda3/lib/python3.8/site-packages/nussl/core/migration.py", line 83, in load
    metadata = self._get_moved(metadata, model_dict, model_path)
  File "/home/verena/miniconda3/lib/python3.8/site-packages/nussl/core/migration.py", line 101, in _get_moved
    raise SeparationException(f"Failed loading model. Expected to find "
nussl.separation.base.separation_base.SeparationException: Failed loading model. Expected to find 'nussl_version' in /home/verena/.nussl/models/dpcl-wsj2mix-model.pth.

Steps To Reproduce
Running this example
Expected behavior
I expected the example above to work with the most current version of nussl.
What did happen
The example produced the above mentioned error when using nussl 1.1.3, but when using an older version (e.g. 1.0) it works.
Software versions
>>> import platform; print(platform.platform())
Linux-5.4.0-52-generic-x86_64-with-glibc2.10
>>> import sys; print("Python", sys.version)
Python 3.8.3 (default, May 19 2020, 18:47:26)
[GCC 7.3.0]
>>> import numpy; print("NumPy", numpy.__version__)
NumPy 1.19.1
>>> import scipy; print("SciPy", scipy.__version__)
SciPy 1.5.2
>>> import nussl; print("nussl", nussl.__version__)
nussl 1.1.3

Whoops! Sorry Verena. Thanks for catching that. We're in the process of adding more metadata to the models so that we can future-proof them. All of the code should be ready to go, but the models haven't been updated yet. So we added some strict checks to make sure that we're not loading models we don't recognize, which is what you hit. The models currently available for download are kind of old.
Here's a work around for the time being:

Download the model outside of nussl (just direct download from the EFZ website).
Load it in torch (https://pytorch.org/tutorials/beginner/saving_loading_models.html)
Add a nussl_version key to the top-level dict of the loaded model. The value shouldn't matter but you can add 1.0.0.
Save it in torch.
Load the modified model in nussl.

Going forward, you should not need to do this. All models trained from 1.1.3 onward should be fully-future proof, with migration happening automatically on load in newer versions of nussl.

Hi! @ethman  I have reproduced your indicated steps as follows:
model_path = nussl.efz_utils.download_trained_model('mask-inference-wsj2mix-model-v1.pth')

# workaround for known issue in the library
# https://github.com/nussl/nussl/issues/192#issuecomment-720689506
model = torch.load("/root/.nussl/models/mask-inference-wsj2mix-model-v1.pth", map_location=torch.device('cpu'))
model["nussl_version"] = "1.1.9"
torch.save(model, "/root/.nussl/models/mask-inference-wsj2mix-model-v1.pth")
##

audio_signal = nussl_custom_bach10[0]["mix"]
separator = nussl.separation.deep.DeepMaskEstimation(audio_signal, mask_type='soft', model_path=model_path)
estimates = separator()

and now I run in to this exception:

SeparationException                       Traceback (most recent call last)
 in ()
24
25 audio_signal = nussl_custom_bach10[0]["mix"]
---> 26 separator = nussl.separation.deep.DeepMaskEstimation(audio_signal, mask_type='soft', model_path=model_path)
27 estimates = separator()
28
5 frames
/usr/local/lib/python3.7/dist-packages/nussl/separation/deep/deep_mask_estimation.py in init(self, input_audio_signal, model_path, device, **kwargs)
25         super().init(input_audio_signal, **kwargs)
26         if model_path is not None:
---> 27             self.load_model(model_path, device=device)
28         self.model_output = None
29         # audio channel dimension in a mask estimation model
/usr/local/lib/python3.7/dist-packages/nussl/separation/base/deep_mixin.py in load_model(self, model_path, device)
35
36         self.device = device
---> 37         model, metadata = SeparationModel.load(model_path)
38         model = model.to(device).eval()
39         self.model = model
/usr/local/lib/python3.7/dist-packages/nussl/ml/networks/separation_model.py in load(location)
234
235         safe_loader = SafeModelLoader()
--> 236         model_dict = safe_loader.load(location, 'cpu')
237         metadata = model_dict['metadata']
238
/usr/local/lib/python3.7/dist-packages/nussl/core/migration.py in load(self, model_path, device, expected_eval)
83         metadata = self._get_moved(metadata, model_dict, model_path)
84         self.eval = expected_eval
---> 85         metadata = self._validate_and_populate(metadata)
86         metadata['config'] = json.dumps(metadata['config'])
87         model_dict['metadata'] = metadata
/usr/local/lib/python3.7/dist-packages/nussl/core/migration.py in validate_and_populate(self, received)
165                 for sub_key, type in expected_val.items():
166                     sub_val = next_dict.get(sub_key, None)
--> 167                     sub_result[sub_key] = self.load_types(type, sub_key, sub_val)
168                 result[key] = sub_result
169
/usr/local/lib/python3.7/dist-packages/nussl/core/migration.py in _load_types(expected_type, key, val)
142         if val is not None:
143             if type(val) != expected_type:
--> 144                 raise SeparationException(f'Expected type {expected_type} '
145                                           f'for key {key} but got {type(val)}!')
146             return val
SeparationException: Expected type None for key seed but got <class 'int'>!

Hi! @ethman I have reproduced your indicated steps as follows:
model_path = nussl.efz_utils.download_trained_model('mask-inference-wsj2mix-model-v1.pth')

# workaround for known issue in the library
# https://github.com/nussl/nussl/issues/192#issuecomment-720689506
model = torch.load("/root/.nussl/models/mask-inference-wsj2mix-model-v1.pth", map_location=torch.device('cpu'))
model["nussl_version"] = "1.1.9"
torch.save(model, "/root/.nussl/models/mask-inference-wsj2mix-model-v1.pth")
##

audio_signal = nussl_custom_bach10[0]["mix"]
separator = nussl.separation.deep.DeepMaskEstimation(audio_signal, mask_type='soft', model_path=model_path)
estimates = separator()

and now I run in to this exception:

SeparationException                       Traceback (most recent call last)
in ()
24
25 audio_signal = nussl_custom_bach10[0]["mix"]
---> 26 separator = nussl.separation.deep.DeepMaskEstimation(audio_signal, mask_type='soft', model_path=model_path)
27 estimates = separator()
28
5 frames
/usr/local/lib/python3.7/dist-packages/nussl/separation/deep/deep_mask_estimation.py in init(self, input_audio_signal, model_path, device, **kwargs)
25         super().init(input_audio_signal, **kwargs)
26         if model_path is not None:
---> 27             self.load_model(model_path, device=device)
28         self.model_output = None
29         # audio channel dimension in a mask estimation model
/usr/local/lib/python3.7/dist-packages/nussl/separation/base/deep_mixin.py in load_model(self, model_path, device)
35
36         self.device = device
---> 37         model, metadata = SeparationModel.load(model_path)
38         model = model.to(device).eval()
39         self.model = model
/usr/local/lib/python3.7/dist-packages/nussl/ml/networks/separation_model.py in load(location)
234
235         safe_loader = SafeModelLoader()
--> 236         model_dict = safe_loader.load(location, 'cpu')
237         metadata = model_dict['metadata']
238
/usr/local/lib/python3.7/dist-packages/nussl/core/migration.py in load(self, model_path, device, expected_eval)
83         metadata = self._get_moved(metadata, model_dict, model_path)
84         self.eval = expected_eval
---> 85         metadata = self._validate_and_populate(metadata)
86         metadata['config'] = json.dumps(metadata['config'])
87         model_dict['metadata'] = metadata
/usr/local/lib/python3.7/dist-packages/nussl/core/migration.py in validate_and_populate(self, received)
165                 for sub_key, type in expected_val.items():
166                     sub_val = next_dict.get(sub_key, None)
--> 167                     sub_result[sub_key] = self.load_types(type, sub_key, sub_val)
168                 result[key] = sub_result
169
/usr/local/lib/python3.7/dist-packages/nussl/core/migration.py in _load_types(expected_type, key, val)
142         if val is not None:
143             if type(val) != expected_type:
--> 144                 raise SeparationException(f'Expected type {expected_type} '
145                                           f'for key {key} but got {type(val)}!')
146             return val
SeparationException: Expected type None for key seed but got <class 'int'>!

Hi,
I have run into the same issue with you, and I go to  /usr/local/lib/python3.7/dist-packages/nussl/core/migration.py, change the type of key "seed" in line 48 to int, and it works.
Hope it helps you too!


Address issues that require us to pin, "@patternfly/react-core" to 3.16.4

We are currently pinned to 3.16.4 for "@patternfly/react-core"
One of the issues I hit in past was related to our modal and passing in an argument to onClose().
https://github.com/fusor/mig-ui/blob/master/src/app/common/components/ConfirmModal.tsx#L7
Below issue is related and has been closed on patternfly side.
https://github.com/patternfly/patternfly-react/issues/2233

According to this comment, the isConfirmed field was never actually used for anything, so it should be safe to just delete it from our usage and we should still compile. I'm not sure what patternfly version we'd like to move up to, but we should upgrade to that and determine there haven't been any regressions before considering this closed.

Your isConfirmed: any was never being used, so just remove it or open an issue for Modal if you want the type to be (event: MouseEvent) => void.

Resolved by #285, although it revealed some dependency conflicts between our @emotion styles and theirs that aren't likely to be resolved unless PF4 updates their @emotion dep. Ian and I have decided to avoid this altogether with #287 as a solution.


WFLY-3854 / WFLY-6777 fix compat testsuite to not hang on jdk9

upgrade to latest micro of openjpa & eclipse link (only used in testsuite)
upgrade ecj compiler to latest avalible build in maven.
cleanup compat testsuite pom.xml

Result when running on JDK9 https://ci.wildfly.org/viewLog.html?buildId=19779&tab=buildResultsDiv&buildTypeId=WF_MasterLinuxJdk9ea (it doesn't hang anymore)


Investigate how to integrate hooks in the API

Hooks examples in the v3 code reveal article
also talk to john regarding aggregators

Spec how API can support hooks. Ask John

Summary:

hooks are stored on a pool at pool registration - information is emitted in PoolRegistered event
each hook has a list of flags suggesting what it can do, eg: enableHookAdjustedAmounts, setShouldCallComputeDynamicSwapFee
we don't entirely know how a hook changes the amounts based on the onchain data - some offchain registry with categories, use-cases might be needed, eg: changing amounts based on user state, or random events

https://www.notion.so/balancer/aaa809e2180f415b88bd4b410de2c746?v=b03f2d1730534a0f8be50709ade7ffef


#211 Fix

Made getFittest and getAverage async, and added appropriate awaits as referenced in #211

Looks great, thanks for the contributions 👍


Set FontAwesome as first priority to avoid clashes

In some cases FontAwesome icons were replaced by letters and symbols from other fonts. Add the font as the first priority in the style sheet to avoid this issue.

Unfortunately this doesn't seem to be a full fix. The ":" in the clock is now offset vertically because apparently it's coming from FontAwesome instead of Roboto. I fixed this manually in my stylesheet by adjusting #clock but that's probably not a real solution.

It's actually worse than just the : being wrong. Font Awesome apparently includes letters, numbers and a few but not all punctuation. So -: are included but %() are not. This wrecks havoc in the theme. I don't see how to fix this manually as I can't add a  around the icons themselves as CSS class is not allowed.

The only way I found to solve this was to sprinkle the config file with things like ". This works but then breaks the tooltip that shows the html as text.

That's not a correct fix i think, we can add a style to force font on format-icon.
I'll look into it, and push in this pr

Thanks. The workaround is working for me now because I have tooltips disabled anyway.  This is definitely broken, I didn't close it in case I found a different solution to push into this branch/pr.

If you move "FontAwesome 5 Free Solid" at the end, does it still fix the issue for you?
font-family: Roboto, Helvetica, Arial, sans-serif, "FontAwesome 5 Free Solid";

I tried that as well. But then the conflicts return as the characters in Roboto are the ones that are conflicting with the ones in FontAwesome.

Which characters are you talking about? IIRC this kind of issue is often related to font fallback in freetype and setting a list of font-families in Gtk CSS never helps because Gtk doesn't do font fallback itself (or only after freetype does its own font fallback, which makes the whole thing pointless).

Here's an example from my config:

That's how gedit renders those icons. Without the span forcing the font that's how Waybar displays things as well. Setting FontAwesome as priority fixes that but then there are other characters that are used from FontAwesome instead of Roboto, like ":" in the clock which then becomes vertically offset.

Any update on this? I'm facing the issue as well.

@jtheoof Does setting a font list for sans-serif help (like in my dotfiles) as a workaround for you? (run fc-cache and restart waybar after creating or updating that file)

Yes @jplatte it works thank you.

So I ended up doing things a little differently but heavily inspired. I created a custom font replacement with a Waybar font name.
So that I can have in style.css:
* {
     border: none;
     border-radius: 0;
     font-family: Waybar;
     font-weight: 800;
     font-size: 12px;
     min-height: 0;
 }

This way I have the Font Awesome override but only for the Waybar font which is used only in this file.

Still got some missing char with your work arounds

and it is weirder if I install a new ttf package as dejavu

BTW, is someone configured his bar without any font awesome symbols and having a clean look ?

@Eluminae fc-match should be able to tell you which font is being used to render U+F796. E.g. for me it's Font Awesome:
% fc-match -s monospace:charset=f796 | head -n3
Font Awesome 5 Free-Solid-900.otf: "Font Awesome 5 Free" "Solid"
DejaVuSansMono.ttf: "DejaVu Sans Mono" "Book"
DejaVuSansMono-Bold.ttf: "DejaVu Sans Mono" "Bold"

You can read more about how to fix issues like this in the Arch wiki page 'Font configuration'. The section Replace or set default fonts might be especially interesting in your situation, or you might want to blacklist or uninstall a font that is claiming to support character ranges it doesn't actually supply useful characters for.

Okay I got it.. In void packages we got font-awesome and font-awesome5. And guess what, font-awesome5 works very well.

Okay now the jtheoof's way with the Waybar font alias rocks !

@pedrocr I think this PR can now be closed since the proposed changes don't work the way they were intended to, right? I've just created an issue (see link above) about improving the documentation regarding font fallback which would probably be better for further discussion than a PR.

Maybe providing another default template who do not use font-awesome could be minimalist alternative ?

Thanks for this hint.

Here's what worked for me:
As well as adding a font-config file, I also had to change it to: font-family: Roboto, Helvetica; (dropping Arial and sans-serif). With those two removed, appending Font Awesome to the list also worked. I guess somehow they're matching characters like (0xf026) even when they shouldn't be. Given that most users have a better understanding of how to install a font than fontconfig overrides, a simple solution here might be to just drop support for the fallback fonts and use:
font-family: Roboto, Helvetica, "Font Awesome 5 Free Solid";

@rdnetto Do you actually have Roboto or Helvetica installed on your system? Does Waybar look any different if you set just font-family: "Font Awesome 5 Free Solid";? I suspect neither is the case, and my first comment still applies:

IIRC […] setting a list of font-families in Gtk CSS never helps because Gtk doesn't do font fallback itself (or only after freetype does its own font fallback, which makes the whole thing pointless)

@rdnetto Do you actually have Roboto or Helvetica installed on your system?

I can confirm I have Roboto installed. (I thought I had Helvetica installed, but I double checked and it turns out it's just "Adobe Helvetica").

Does Waybar look any different if you set just font-family: "Font Awesome 5 Free Solid";? I suspect neither is the case, and my first comment still applies:
Yes - the font changes, and th in eth0 fails to render.

For comparison, my current working config is font-family: Roboto, Helvetica; with the following font config:
    <alias>
        <family>Roboto</family>
        <prefer>
            <family>Font Awesome 5 Free</family>
        </prefer>
    </alias>

with the following font config

Right, so it's still just matching Roboto on the Gtk side and the actual per-glyph fallback happens inside fontconfig.

Should probably note that Font Awesome, on Gentoo at least, is now "Font Awesome 6 Free", and not version 5. Found out thanks to the fc-match -s monospace:charset=f796 command provided above.
font-family: "Noto Sans", "Font Awesome 6 Free";


Create "Google Summer of Code | Coding Period | Week 5" blog post

Description
Create Google Summer of Code week 5 blog post with markdown files.


Support transparent images

I'm submitting a ...

[ ] bug report
[x] feature request
[ ] question about the decisions made in the repository
[ ] question about how to use this project

Summary
Implementing support for transparent images seem to be possible (like waifu2x), but unfortunately my TF skills are a bit lacking, so when I tried tinkering with it, I just ended up breaking it. Is this something you would consider implementing? 😸

Hey, thank you for your interest in my project.
For this requirement you mentioned, I think it can be achieved without changing the internal codes of waifu2x-tfjs.
In fact, waifu2x does not process the transparency channel when performing image super-resolution.
You only need to pass the RGB channels of the image into waifu2x-tfjs to get a new super-resolution RGB image, and then combine it with the original transparency channel.
Of course, if the image is enlarged, then you need to enlarge the transparency channel to the same size when you combine the new image and the transparency channel.
This should work. You could have a try.

Hey, thank you for your interest in my project.
For this requirement you mentioned, I think it can be achieved without changing the internal codes of waifu2x-tfjs.
In fact, waifu2x does not process the transparency channel when performing image super-resolution.
You only need to pass the RGB channels of the image into waifu2x-tfjs to get a new super-resolution RGB image, and then combine it with the original transparency channel.
Of course, if the image is enlarged, then you need to enlarge the transparency channel to the same size when you combine the new image and the transparency channel.
This should work. You could have a try.

或许可以增加一个支持透明度的选项，开启后检测到有透明的图片先提取透明通道到灰度图像，然后把灰度图像也分别送入放大，主图和灰度图放大完成后把灰度图还原为透明通道合并回主图，会消耗两倍的时间但应该可以实现保证质量的透明放大

Hey, thank you for your interest in my project.
For this requirement you mentioned, I think it can be achieved without changing the internal codes of waifu2x-tfjs.
In fact, waifu2x does not process the transparency channel when performing image super-resolution.
You only need to pass the RGB channels of the image into waifu2x-tfjs to get a new super-resolution RGB image, and then combine it with the original transparency channel.
Of course, if the image is enlarged, then you need to enlarge the transparency channel to the same size when you combine the new image and the transparency channel.
This should work. You could have a try.

或许可以增加一个支持透明度的选项，开启后检测到有透明的图片先提取透明通道到灰度图像，然后把灰度图像也分别送入放大，主图和灰度图放大完成后把灰度图还原为透明通道合并回主图，会消耗两倍的时间但应该可以实现保证质量的透明放大

想法可行，不过我暂时没有时间做此类拓展。
本仓库基本上就是实现waifu2x模型原本能实现的功能，关于透明通道放大之类的，相当于是模型之外的额外操作，我的建议是可以自己把透明通道转换为灰度图再转换为imagebitmap输入模型当中，再对输出转换回透明通道。


PDF fonts Misrendered

Attach (recommended) or Link to PDF file here:
Configuration:

Web browser and its version: Firefox Nightly, 65.0a1 (2018-12-10)
Operating system and its version: openSUSE Leap 15
PDF.js version: Firefox Nightly version
Is a browser extension:

Steps to reproduce the problem:

Open https://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/scsi-accessed-enclosures.pdf
See the misrendered fonts

What is the expected behavior? (add screenshot)
Evince:
What went wrong? (add screenshot)
Link to a viewer (if hosted on a site other than mozilla.github.io/pdf.js or as Firefox/Chrome extension):
https://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/scsi-accessed-enclosures.pdf

Looks fine to me, can you attach screenshots?

Sorry, I forgot to add the print screen. Mine Firefox Nightly comes from Flatpak: https://firefox-flatpak.mojefedora.cz/
I don't know if this has something different from the "normal" Nightly distribution.
Is the image bellow enough to show the problem? I can't see errors in console.

Yes, this does make the issue more clear. However, I can't reproduce it. Could you try the regular (stable) Firefox or an official nightly? It might be a bug in Nightly.

I haven't this problem too, using Firefox Quantum 63.0.3 (64-bit) on OpenSUSE Tumbleweed, default font Roboto (16pt), zoom at 300%, see screenshot bellow:

Should I open a bug in another place regarding this issue with flatpak?

I think that would be good. Maybe they're changing some prefs? Also, do you see anything in the web console?

@brendandahl anything on console, so I opened a bug in flatpak repo. Thanks!

I'll close this issue since it does not look like a problem on the PDF.js side. If there is something that needs fixing here, feel free to let us know and we can reopen. Thank you.


Minor improvements

Added some friendlier end user labels, fixed css values, removed a few stray tabs.
I've tested these changes, but since I don't use git I had to cut and paste into the web editor here (which has a few bugs of its own).  In short there may be a typo lurking.

Looks good to me, merging it in!


RequestPane body form input text color visibility fix in dark mode

In dark mode body form input text color visibility issue fix. Implemented color style of input in both dark and light theme files.

Merged!
Thanks for this @Gvinod1991 !


How to customize (or fix) some elements

Hi,
I love the theme colors and contrast!  Thank you!
I know beauty is in the eye of the beholder...
I find the window borders an separators a bit wide and taking up too much real estate, especially vertical windows,  and Nvim-tree and toggle-term.  For Telescope the border lines and title seem to lack just a bit of contrast.  I was trying to get a 'flat' no border look for telescope as described here:
https://www.reddit.com/r/neovim/comments/se377t/telescopenvim_looks_neat/
see also: https://github.com/nvim-telescope/telescope.nvim/blob/39b12d84e86f5054e2ed98829b367598ae53ab41/plugin/telescope.lua#L27-L30
I tried for hours putting the snippets in the telescope.lua config, as well as trying to integrate directly into nightfly.
(I don't know that it would be benefical to include all the code here;  Maybe this has become a feature request...!)
It does seem to me that there are a few Telescope items missing in the nightfly/init.lua config, notably:
  | highlight(0, "TelescopeSelectionCaret", { fg = p.red, bg = bg_visual })
  | highlight(0, "TelescopePromptTitle", { fg = p.orange, bg = "NONE" })
  | highlight(0, "TelescopeResultsTitle", { fg = p.orange, bg = "NONE" })
  | highlight(0, "TelescopePreviewTitle", { fg = p.orange, bg = "NONE" })
  | highlight(0, "TelescopePromptCounter", { fg = p.red, bg = "NONE" })
  | highlight(0, "TelescopePreviewHyphen", { fg = p.red, bg = "NONE" })

I was also fiddling around with:
highlight(0, "WinSeparator", { link = "VertSplit" })
highlight(0, "VertSplit", { fg = border, bg = "NONE" })

I have added a couple of screen shots to show the difference between nightfly and 'another' popular theme.  Note its not the colors I am wanting to change but the border.
example flat no border for Telegram:

nightfly needs some more contrast with the titles and perhaps borders?

another theme showing Nvim-tree, buffer, and help window.

Note in nightfly the large separation between esp the vertical windows

Sorrry for not sending in a merge request.   I'll probably eventually find the right way to do this, but in the mean time, it might also be (subject to personal opinion) an improvement to nightfly.

The existing nightflyWinSeparator option is likely to solve your issue, set it to 2 as explained here:
https://github.com/bluz71/vim-nightfly-colors#nightflywinseparator

Answered.
Note also, it is simple to override certain default theme hightlight groups as noted here, for example:
local highlight = vim.api.nvim_set_hl
local custom_highlight = vim.api.nvim_create_augroup("CustomHighlight", {})
vim.api.nvim_create_autocmd("ColorScheme", {
  pattern = "nightfly",
  callback = function()
     highlight(0, "TelescopeSelectionCaret", { fg = p.red, bg = bg_visual })
     highlight(0, "TelescopePromptTitle", { fg = p.orange, bg = "NONE" })
     highlight(0, "TelescopeResultsTitle", { fg = p.orange, bg = "NONE" })
     highlight(0, "TelescopePreviewTitle", { fg = p.orange, bg = "NONE" })
     highlight(0, "TelescopePromptCounter", { fg = p.red, bg = "NONE" })
     highlight(0, "TelescopePreviewHyphen", { fg = p.red, bg = "NONE" })
  end,
  group = custom_highlight,
})

Awesome.  Thank you.   Sorry for the noise!

Awesome. Thank you. Sorry for the noise!

Not noise; I am more than happy to help.
Cheers.


Add tests

Pull Request Test Coverage Report for Build 38

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.8%) to 80.156%

Totals

Change from base Build 36:
0.8%

Covered Lines:
824

Relevant Lines:
1028

💛  - Coveralls


[RN 0.54.2] OWASP Vulnerabilities due to React Native dependencies

Hello,
It looks like 5 node modules used by React Native fail OWASP. Each of the 5 failing modules is a dependency-of-a-dependency of React Native (most coming from connect).
Solution: React Native need to update their dependencies.
Environment
Environment:
  OS: macOS High Sierra 10.13.3
  Node: 7.10.0
  Yarn: 0.24.6
  npm: 4.2.0
  Watchman: 4.9.0
  Xcode: Xcode 9.2 Build version 9C40b
  Android Studio: 3.0 AI-171.4443003

Packages: (wanted => installed)
  react: 16.2.0 => 16.2.0
  react-native: 0.54.2 => 0.54.2

Expected Behavior
Clean OWASP scan.
Actual Behavior
Import parts in green.

You can see that negotiator is a nested dependency of RN.
You can see from the npm ls commands that each of the offending npm modules is a dependency of react-native, e.g.
npm ls negotiator
<EMAIL_ADDRESS>/Users/Paul/git/MyProject/Project
└─┬<EMAIL_ADDRESS>  └─┬<EMAIL_ADDRESS>    ├─┬<EMAIL_ADDRESS>    │ └─┬<EMAIL_ADDRESS>    │   └──<EMAIL_ADDRESS>    └─┬<EMAIL_ADDRESS>      └─┬<EMAIL_ADDRESS>        └──<EMAIL_ADDRESS>
We can see that negotiator is a derived dependency of the connect package that react-native relies on:.
<EMAIL_ADDRESS>relies on:
<EMAIL_ADDRESS>which relies on<EMAIL_ADDRESS>which relies on<EMAIL_ADDRESS>and
<EMAIL_ADDRESS>which relies on<EMAIL_ADDRESS>which relies on<EMAIL_ADDRESS>Only<EMAIL_ADDRESS>is vulnerable.

If negotiator were a direct dependency of Project, the ls tree would look like this:
npm ls enzyme
<EMAIL_ADDRESS>/Users/Paul/git/MyProject/Project
└──<EMAIL_ADDRESS>Steps to Reproduce
Use https://jeremylong.github.io/DependencyCheck/dependency-check-cli/ on a React Native project,  source here: https://github.com/jeremylong/DependencyCheck

Thanks for the report. For anyone interested in contributing, I suggest upgrading the dependencies incrementally in separate PRs. For example, to address the negotiator line item, you might open a PR that bumps the connect dependency from 2.30.2 to 3.6.6. I can take over ensuring the upgraded dependencies do not introduce other issues to our own React Native consumers at Facebook.

I would add that when bumping a major version with breaking changes to the public API of the package, you need to look at the existing call sites and either update them or make sure they are still working and explain this in the commit description.

Shell-quote is listed as a high security risk node-module. Refer to the report (https://nvd.nist.gov/vuln/detail/CVE-2016-10541) containing the detail about it in National Vulnerabilities Database.
Please help me in fixing this issue.

Hey @ranahammad, did you see my comment above?

Hello @hramos, yes saw that. Have forked the repository and figuring out how to fix it. Once it's fixed, will create a PR.

👋 here - this issue wanted to provide some generic info on some packs with security issues, but it's been a long time since it's been opened so I'll close it.
We are striving to keep most packs more updated as possible, so please follow Hector's instructions above if you want to help out.
Also, now GitHub also provides an interface for keeping up to date with latest security issues so this issue can be closed 👍

what is the current status of OWASP vulnerabilities on RN versions 0.59.* and above?


README.md Cannot listen for callback

@percentageUpdate="updatePoints"
Change to @percentage-update="updatePoints" That's it
But the value calculated by the callback seems to be inaccurate. The value of the first callback is about 30%.

It has something to do with the picture. I changed a jpg picture and the calculation is accurate。。。

 const { context } = this.$scopedSlots.default()[0];
this.slotDomElement = context.$children.find((vnode) => vnode._uid === this._uid).$el;
Why not use this.$el directly to get the outermost div

@percentageUpdate="updatePoints"
Change to @percentage-update="updatePoints" That's it
But the value calculated by the callback seems to be inaccurate. The value of the first callback is about 30%.

Can you provide me the image in question so I can reproduce this issue?

 const { context } = this.$scopedSlots.default()[0];
this.slotDomElement = context.$children.find((vnode) => vnode._uid === this._uid).$el;
Why not use this.$el directly to get the outermost div

I will give you a detailed explanation later. Too tired right now but it is good to do it that way 😄

@percentageUpdate="updatePoints"
Change to @percentage-update="updatePoints" That's it
But the value calculated by the callback seems to be inaccurate. The value of the first callback is about 30%.

Can you provide me the image in question so I can reproduce this issue?
EDIT: Or better can you provide the vue file you are using in your example

I found the cause of the problem because my picture is a png type picture, and the transparent area may not be classified as a mask layer.

@percentageUpdate="updatePoints"
Change to @percentage-update="updatePoints" That's it
But the value calculated by the callback seems to be inaccurate. The value of the first callback is about 30%.

Can you provide me the image in question so I can reproduce this issue?
EDIT: Or better can you provide the vue file you are using in your example

I found the cause of the problem because my picture is a png type picture, and the transparent area may not be classified as a mask layer. When the picture is in jpg format, the calculation is fine

Yes that makes sense the calculation uses the alpha value of a certain pixel to determine whether it is scratched free or not. If the cover includes transparency the component doesn't require the user to scratch that certain area free since it is already scratched free in the sense that you can view what lies below.
Therefore the percentage value is that high. 30% of the area are already "free" (as in transparent).

 const { context } = this.$scopedSlots.default()[0];
this.slotDomElement = context.$children.find((vnode) => vnode._uid === this._uid).$el;
Why not use this.$el directly to get the outermost div

I will give you a detailed explanation later. Too tired right now but it is good to do it that way 😄

So: I actually can't give you a detailed explanation because there is none. I replaced it with this.$el in the version I just published. Also while looking into my code I found out that the MutationObserver was never working - I also fixed that in the latest version.


是不是不支持macOS 12 版本以上的系统啊

Warning: You are using macOS 12.
We do not provide support for this pre-release version.
You will encounter build failures with some formulae.
Please create pull requests instead of asking for help on Homebrew's GitHub,
Twitter or any other official channels. You are responsible for resolving
any issues you experience while you are running this
pre-release version.

同样遇到这个问题,系统版本 12.5.1 (21G83) M1芯片

12.6实测没问题

目前是预览版，不保证对 macOS 12 的支持


Windows Service Monitor returns an error

What version of Butler are you using?
9.2.1
What version of Node.js are you using? Not applicable if you use the standalone version of Butler.
I use tandalone version of Butler.
What command did you use to start Butler?
butler.exe -c test.yaml -l debug
What operating system are you using?
Windows 2019
What CPU architecture are you using?
Intel Xeon
What Qlik Sense versions are you using?
May 2023
Describe the Bug
Butler does not find the Qlik services installed on the Sense Server. Please find a screenshot attached.

Expected Behavior
No response
To Reproduce
No response

Ohh... interesting. Could it be that Qlik changed the name of the engine service in QS 2023-May?
Got to admit that I have not tested Butler with that QS version - will certainly have to do so!

Hi Göran,
I checked the names of the services. They have remained the same. I have tried it with any service of the Windows operating system and it works. Is it a problem that Butler is running on a separate Windows computer and not directly on the Sense server?
BR,
Rumen

I had a look at this and it's most likely a permissions problem.
It can be tested by running this command on the server where Butler is running:
sc.exe \\<IP_ADDRESS> query state= all
If the response looks like this you got a permissions problem, with the remote/Sense server blocking access to the services on the that server.
C:\temp>sc.exe \\<IP_ADDRESS> query state= all
[SC] OpenSCManager FAILED 5:

Access is denied.

C:\temp>

If you get the same error as above this can be solved in a few different ways:

Modify Butler itself so it authenticates with the remote server before trying to get info about its services. This requires some work, but would be a nice and clean solution.
Disable the security mechanism preventing Butler's access to the remote server. This works, but lowering security may not be ideal/allowed. Some editing of the registry of the Sense server is needed, examples here.
If the user account running Butler is member of the domain Administrators group, things should work even with today's Butler version. The question then is whether it's ok for the Butler service account to be a domain admin.
Run Butler on the same server as Sense itself.

Hello Göran,
This issue is not urgent for me. I will wait for the new version.
Thank you very much!
Rumen

Hi Göran,
I have just installed the new version 9.3.1 and I still have the same issue. Please take a look at the screenshot.

BR,
Rumen


Circle ripple

Add attribute that would allow circular ripples

Use rounded corners with radius of half width instead ce4be503c27d6b824a1f3349e251a983c3e553df


Forward compatibility error in Avro schema

When I test the forward compatibility of Avro schema, it throws an exception Schema mismatch. This problem does not exist in other schemas like JSON or Protobuf.
I declared two type of message TestMessageV3 and TestMessageV4. TestMessageV4 is based on TestMessageV3 and delete an optional fields.
The test step like below:

Create a consumer that consumes TestMessageV3 message.
Create a producer that produces TestMessageV4 message.
When the consumer received the V4 message, a schema mismatch exception was thrown.

Here is my testing codes:
/// <summary>
        /// Base version message
        /// </summary>
        // [Aliases("TestMessageV4","TestMessageV3")]
        [Aliases("TestMessage")]
        public class TestMessageV3
        {
            public string Id { get; set; }

            public string Name { get; set; }

            public int Age { get; set; }

            public byte[] Data { get; set; } = Encoding.UTF8.GetBytes(new string('0', 1024));

            // by setting the new field to nullable, it is optional to the contract
            public int? NewField { get; set; } = -999;

            public override string ToString()
            {
                return
                    $"DataContractV3 Id:{Id}; Name: {Name}; Age: {Age}; Data.Length: {Data.Length}; NewField: {NewField}";
            }
        }

        /// <summary>
        /// remove optional field
        /// </summary>
        // [Aliases("TestMessageV4","TestMessageV3")]
        [Aliases("TestMessage")]
        public class TestMessageV4
        {
            public string Id { get; set; }

            public string Name { get; set; }

            public int Age { get; set; }

            public byte[] Data { get; set; } = Encoding.UTF8.GetBytes(new string('0', 1024));

            public override string ToString()
            {
                return $"DataContractV4 Id:{Id}; Name: {Name}; Age: {Age}; Data.Length: {Data.Length}";
            }
        }

        /// <summary>
        /// Produce a single randomly generated message, Id will be a guid for tracking into consumer
        /// </summary>
        /// <param name="producerName">Name of Producer</param>
        /// <param name="topic">Topic to publish to</param>
        /// <returns></returns>
        public async Task ProduceMessageV4(string producerName, string topic)
        {
            // Generate a unique id for the message to help track it to the consumers
            var uid = Regex.Replace(Convert.ToBase64String(Guid.NewGuid().ToByteArray()), "[/+=]", "");
            var msg = new TestMessageV4() {Id = uid, Name = "V4", Age = 11};
            var producerBuilder = Client.NewProducer(Schema.AVRO<TestMessageV4>())
                .Topic(topic)
                .ProducerName(producerName);

            var producer = await producerBuilder.CreateAsync();

            var messageId = await producer.SendAsync(msg);
            logger.WriteLine("Message Sent: " + messageId);
            logger.WriteLine(msg.ToString());
            await producer.DisposeAsync();
        }

        /// <summary>
        /// Subscribe to and consume Messages
        /// </summary>
        /// <param name="consumerName">Name of Consumer</param>
        /// <param name="topic">Topic to subscribe to</param>
        /// <param name="cancellationToken">Token used to stop subscription</param>
        /// <returns></returns>
        public async Task ConsumeMessagesV3(string consumerName, string topic, CancellationToken cancellationToken)
        {
            // Get subscription for the topic and schema
            var subscription = await Client.NewConsumer(Schema.AVRO<TestMessageV3>())
                .Topic(topic)
                .ConsumerName(consumerName)
                .SubscriptionName($"subscription_{consumerName}")
                .SubscriptionInitialPosition(SubscriptionInitialPosition.Latest)
                .SubscribeAsync();

            // consume until canceled
            await ReceiveMessagesAsync<TestMessageV3>(subscription, cancellationToken, "TestMessageV3");
        }

        private async Task ReceiveMessagesAsync<T>(IConsumer<T> consumer, CancellationToken cancellationToken,
            string version)
        {
            while (!cancellationToken.IsCancellationRequested)
            {
                logger.WriteLine("Listening to version " + version);
                var message = await consumer.ReceiveAsync(cancellationToken);

                if (message != null)
                {
                    try
                    {
                        var baseMsg = message.GetValue();
                        var messageDetails = baseMsg.ToString();
                        logger.WriteLine(messageDetails);
                        await consumer.AcknowledgeAsync(message.MessageId);
                    }
                    catch (Exception ex)
                    {
                        logger.WriteLine(ex.Message);
                        logger.WriteLine(" EXCEPTION!");
                    }
                }
            }
            await consumer.UnsubscribeAsync();
        }

        [Fact]
        public async Task ForwardCompatibility()
        {
            var topicName = "public/default/topic-" + Guid.NewGuid().ToString("N");
            logger.WriteLine(topicName);
            CancellationTokenSource cts = new CancellationTokenSource();
            var consumeTask = ConsumeMessagesV3("c1", topicName, cts.Token);
            var produceTask = ProduceMessageV4("p1", topicName);
            await Task.WhenAll(consumeTask, produceTask);
        }

and it outputs these result:
Schema mismatch. Reader: {"type":"record","name":"TestMessageV3","namespace":"UseCaseSchema.DataContract","aliases":["UseCaseSchema.DataContract.TestMessage"],"fields":[{"name":"Id","default":null,"type":["null","string"]},{"name":"Name","default":null,"type":["null","string"]},{"name":"Age","type":"int"},{"name":"Data","default":null,"type":["null","bytes"]},{"name":"NewField","default":null,"type":["null","int"]}]}, writer: {"type":"record","name":"TestMessageV4","namespace":"UseCaseSchema.DataContract","aliases":["UseCaseSchema.DataContract.TestMessage"],"fields":[{"name":"Id","default":null,"type":["null","string"]},{"name":"Name","default":null,"type":["null","string"]},{"name":"Age","type":"int"},{"name":"Data","default":null,"type":["null","bytes"]}]}

The issue with AVRO support for forwarding compatibility that we ran into comes from how Avro verifies class name match to aliases as described here https://avro.apache.org/docs/current/spec.html#Aliases. Their .Net implementation requires that the reader schema has aliases matching writer classes. In the Forward Compatibility scenario, it means that an older consumer schema somehow includes aliases for newer producer schemas. Theoretically, this is possible but seems impractical in reality.
So the temporary solution is to set all the class names that need to be compatible when setting alias. Like [Aliases("TestMessageV4","TestMessageV3")]. Can we find a better way to solve this problem? Can we do some fix on this client? Or is this an issue from Avro reader?
If there is something unclear about the test code, please let me know. Thank you.

Sorry, I'm not sure I understand clearly what you mean, should I fix something on Pulsar.Client library side? If yes, would you be able to add an integration test that reproduces the issue?

Sorry, I'm not sure I understand clearly what you mean, should I fix something on Pulsar.Client library side? If yes, would you be able to add an integration test that reproduces the issue?

I have edited the issue description and added some test codes and test output.

Thanks, I'll dig into this issue

@RobertIndie I've checked this issue, and indeed - verification on schema compatibility is based on reader aliases, ignoring writer aliases. It seems that it's not .net only issue, I've found similar suggestion https://issues.apache.org/jira/browse/AVRO-1347 which wasn't approved. Looks like we have 2 choices: not change records' names or to force update consumers with a new alias. You can also try pushing the support of this feature in Avro repository.


[FP]: CVE-2023-43810  Reported for detector-resources-support-0.32.0

Package URl
<EMAIL_ADDRESS>CPE
cpe:2.3:a:opentelemetry:opentelemetry:0.32.0:::::::*
CVE
CVE-2023-43810
ODC Integration
{"label"=>"Gradle Plugin"}
ODC Version
11.1.1
Description
No response

You don't need to create an item for every single CVE if the library and CPE are the same.
Please also include a description so people know why you think it is a false positive.

approved


Monterey development - summary view on 10/15/2016

Intention

I'm submitting a document request
This document should be the container of all existing Monterey issues.
While I am creating its initial version here as a service to all of us involved in planning for the next release, it should be @JeroenVinke who will continue managing it in any way he sees fit - including transforming it into a Trello document, or Zenhub integrated with Github.
Equally important, I am writing this to help me see the whole elephant - not just a couple of feet and a trunk which are currently implemented.

I really see this elephant like this

where I intentionally left some parts outside "red-bounded" areas visible to insinuate that I do have some ability to extrapolate 😏
I used this graphics analogy of my current vision to indicate that I really want to see more while extending the current set of features, so I can feel good that we left the needed space and structure for the whole elephant.

This document should be considered the structured (tree-list like) view of Monterey issues - which is at the moment several pages long with many internal links that makes it difficult to view and grasp its content.
I do not expect that this introduction will surprise anyone; nevertheless I wrote it as any good perfectionist would do.

Monterey definition
Def 1.
Monterey is an extensible application that provides a graphical user interface for a collection of tools to simplify the creation, configuration and maintenance of Aurelia applications.
Def 2.
Application configuration tasks are equally present in application creation process and in application maintenance process. Since at the moment we do not have all features needed for application creation and maintenance for all module loaders (indeed we have only RequireJS **partially available" via Aurelia-CLI), we need to address this situation in a non-symmetric way.
Starting classification
I am not sure what is the best taxonomy (classification into ordered categories) at the moment, so consider this the first iteration only - which is why this should not be done using some fancier tool (Google spreadsheet comes to mind). The first level categorization (which will likely be removed later, by unifying these two top level categories into into one) is this:

Monterey is an extensible application that provides a graphical user interface for a collection of tools to simplify the creation, configuration and maintenance of Aurelia applications.

1. Creating application
2. Managing applications

Starting classification
I am not sure what is the best taxonomy (classification into ordered categories) at the moment, so consider this the first iteration only - which is why this should not be done using some fancier tool (Google spreadsheet comes to mind). The first level categorization (which will likely be removed later, by unifying these two top level categories into into one) is this:

Monterey is an extensible application that provides a graphical user interface for a collection of tools to simplify the creation, configuration and maintenance of Aurelia applications.

1. Creating application
2. Managing applications

4. General (module independent)  features
This category includes all features that make Monterey more appealing to developers.
The list below is unfinished and uncategorized
4.1 Enabling Github access when Monterey is behind a proxy (#389)
4.2 GitKraken / Commander integration (#382)
4.3 ConEmu app launcher (#380)
4.4 Editor integration ( #356, ... many other related issues to be added here)
4.5 Connecting Monterey with Aurelia HUB (#378)
4.6 Sanity checks (#365)
4.7 Insight in size of application (363) (this title feels like "bad English")
4.8 Online API to get all versions of packages (#361)
many more to follow ...


Some Property Detail Pages show blank page

https://codefornebraska.github.io/landlord/low-condition/0125490002
Error:
Uncaught TypeError: Cannot read property 'tableData' of null     at data-manager.js:304     at Array.map (<anonymous>)     at e.value (data-manager.js:303)     at n.value (material-table.js:656)     at n.value (material-table.js:693)     at us (react-dom.production.min.js:212)     at xl (react-dom.production.min.js:255)     at t.unstable_runWithPriority (scheduler.production.min.js:19)     at Gi (react-dom.production.min.js:122)     at wl (react-dom.production.min.js:248)

It looks like the violations aren't populating correctly anymore causing the problem. I temporarily filtered out the null values so that the property details page still loads, but it won't match up with the Violations Count on the main table pages.

The URL changed from https://www.omahapermits.com/permitinfo/* to https://www.omahapermits.com/PermitInfo2020/* so all the scraping is bad.

Hey, @ronaldewatts thanks for your work on this. It looks like your changes resolved the issue. I'm going to close the issue.


ruff fixes

simulation\orders\objects.py:21:9: F841 Local variable t1 is assigned to but never used
simulation\orders\objects.py:24:9: F841 Local variable t2 is assigned to but never used
simulation\routers\bruno.py:7:1: E402 Module level import not at top of file
simulation\routers\bruno.py:40:52: E741 Ambiguous variable name: l
simulation\routers\bruno.py:43:53: E741 Ambiguous variable name: l
simulation\routers\data.py:109:9: F811 Redefinition of unused replace_nan_with_None from line 85
simulation\routers\data.py:238:36: E711 Comparison to None should be cond is not None
simulation\routers\dzmitry.py:10:1: E402 Module level import not at top of file
simulation\routers\dzmitry.py:164:9: E722 Do not use bare except
simulation\routers\dzmitry.py:184:9: E722 Do not use bare except
simulation\routers\test_bruno.py:48:5: F841 Local variable tol is assigned to but never used
simulation\routers\test_bruno.py:120:24: F841 Local variable l_max is assigned to but never used
simulation\routers\test_bruno.py:120:31: F841 Local variable p_max is assigned to but never used
simulation\routers\test_bruno.py:122:9: E722 Do not use bare except
simulation\routers\test_bruno.py:136:20: E741 Ambiguous variable name: l
simulation\routers\test_bruno.py:149:9: E722 Do not use bare except
simulation\routers\test_dzmitry.py:6:1: E402 Module level import not at top of file
simulation\routers\test_dzmitry.py:21:1: E402 Module level import not at top of file
simulation\routers\test_dzmitry.py:22:1: E402 Module level import not at top of file
simulation\routers\test_dzmitry.py:22:17: F811 Redefinition of unused np from line 2
simulation\routers\test_dzmitry.py:24:1: E402 Module level import not at top of file
simulation\routers\test_dzmitry.py:131:5: F821 Undefined name simulate
blackbox\joiner.py:19:5: F841 Local variable asset is assigned to but never used
blackbox\playground.py:10:1: E402 Module level import not at top of file

kind off ok, please PR from latest main HEAD, not from old one.

cd mantis
poetry lock --no-update
poetry install
poetry run pytest
these commands should success after your changes, as they are before on CI