[Bug]: vLLM CPU mode broken Unable to get JIT kernel for brgemm

Your current environment

The output of `python collect_env.py`
Collecting environment information...
PyTorch version: 2.5.1+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0
Clang version: Could not collect
CMake version: version 3.31.0
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov  6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.8.0-1018-gcp-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               8
On-line CPU(s) list:                  0-7
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 7B12
CPU family:                           23
Model:                                49
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             4499.99
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr arat npt nrip_save umip rdpid
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            128 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             2 MiB (4 instances)
L3 cache:                             16 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-7
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow:   Mitigation; Safe RET
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; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.5.0
[pip3] numpy==1.26.4
[pip3] pyzmq==26.2.0
[pip3] torch==2.5.1+cpu
[pip3] torchvision==0.20.1+cpu
[pip3] transformers==4.46.3
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.4.post2.dev22+g47826cac
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

VLLM_WORKER_MULTIPROC_METHOD=spawn
VLLM_LOGGING_LEVEL=DEBUG
LD_LIBRARY_PATH=/usr/local/lib/python3.10/dist-packages/cv2/../../lib64:

Model Input Dumps
No response
🐛 Describe the bug
Seems oneDNN is missing in latest Dockerfile or at least some part of it seems missing.
This used to work in v0.6.3 fine though. I was suspecting this PR since it touched how oneDNN is included: https://github.com/vllm-project/vllm/pull/9344
Steps to reproduce:

Git clone vllm repo latest main branch or v0.6.4.post1
Build the cpu docker image: docker build -t test-cpu -f Dockerfile.cpu .
Run the openai server:

docker run -d --name vllm -p 8000:8000 -e VLLM_LOGGING_LEVEL=DEBUG -e ONEDNN_VERBOSE=all -e VLLM_WORKER_MULTIPROC_METHOD=spawn test-cpu --model facebook/opt-125m --disable-frontend-multiprocessing

Wait for server to be ready and then send a simple prompt:

curl -v --fail-with-body --show-error http://localhost:8000/v1/completions   -H "Content-Type: application/json"   -d '{
  "model": "facebook/opt-125m",
  "prompt": "San Francisco is a",
  "max_tokens": 7,
  "temperature": 0
}'

Full logs:
INFO 11-20 06:32:30 importing.py:15] Triton not installed or not compatible; certain GPU-related functions will not be available.
INFO 11-20 06:32:31 api_server.py:592] vLLM API server version 0.6.4.post2.dev22+g47826cac
INFO 11-20 06:32:31 api_server.py:593] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=True, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='facebook/opt-125m', task='auto', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, download_dir=None, load_format='auto', config_format=<ConfigFormat.AUTO: 'auto'>, dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', override_neuron_config=None, override_pooler_config=None, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False)
INFO 11-20 06:32:31 __init__.py:31] No plugins found.
WARNING 11-20 06:32:37 _logger.py:72] [DEPRECATED] Block manager v1 has been removed, and setting --use-v2-block-manager to True or False has no effect on vLLM behavior. Please remove --use-v2-block-manager in your engine argument. If your use case is not supported by SelfAttnBlockSpaceManager (i.e. block manager v2), please file an issue with detailed information.
WARNING 11-20 06:32:37 _logger.py:72] Async output processing is only supported for CUDA, TPU, XPU and HPU.Disabling it for other platforms.
WARNING 11-20 06:32:37 _logger.py:72] CUDA graph is not supported on CPU, fallback to the eager mode.
WARNING 11-20 06:32:37 _logger.py:72] Environment variable VLLM_CPU_KVCACHE_SPACE (GB) for CPU backend is not set, using 4 by default.
INFO 11-20 06:32:37 llm_engine.py:249] Initializing an LLM engine (v0.6.4.post2.dev22+g47826cac) with config: model='facebook/opt-125m', speculative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cpu, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=facebook/opt-125m, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None, pooler_config=None)
INFO 11-20 06:32:42 importing.py:15] Triton not installed or not compatible; certain GPU-related functions will not be available.
(VllmWorkerProcess pid=29) INFO 11-20 06:32:43 __init__.py:31] No plugins found.
(VllmWorkerProcess pid=29) INFO 11-20 06:32:43 selector.py:221] Cannot use _Backend.FLASH_ATTN backend on CPU.
(VllmWorkerProcess pid=29) INFO 11-20 06:32:43 selector.py:156] Using Torch SDPA backend.
(VllmWorkerProcess pid=29) INFO 11-20 06:32:43 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
(VllmWorkerProcess pid=29) DEBUG 11-20 06:32:43 parallel_state.py:983] world_size=1 rank=0 local_rank=-1 distributed_init_method=tcp://<IP_ADDRESS>:35451 backend=gloo
(VllmWorkerProcess pid=29) DEBUG 11-20 06:32:43 decorators.py:84] Inferred dynamic dimensions for forward method of <class 'vllm.model_executor.models.opt.OPTModel'>: ['input_ids', 'positions', 'intermediate_tensors', 'inputs_embeds']
(VllmWorkerProcess pid=29) INFO 11-20 06:32:43 weight_utils.py:243] Using model weights format ['*.bin']
Loading pt checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
(VllmWorkerProcess pid=29) /usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/weight_utils.py:425: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
(VllmWorkerProcess pid=29)   state = torch.load(bin_file, map_location="cpu")
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  4.62it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  4.61it/s]
(VllmWorkerProcess pid=29)
INFO 11-20 06:32:45 cpu_executor.py:195] # CPU blocks: 7281
DEBUG 11-20 06:32:45 decorators.py:84] Inferred dynamic dimensions for forward method of <class 'vllm.model_executor.models.opt.OPTModel'>: ['input_ids', 'positions', 'intermediate_tensors', 'inputs_embeds']
INFO 11-20 06:32:45 api_server.py:534] Using supplied chat template:
INFO 11-20 06:32:45 api_server.py:534] None
INFO 11-20 06:32:45 launcher.py:19] Available routes are:
INFO 11-20 06:32:45 launcher.py:27] Route: /openapi.json, Methods: HEAD, GET
INFO 11-20 06:32:45 launcher.py:27] Route: /docs, Methods: HEAD, GET
INFO 11-20 06:32:45 launcher.py:27] Route: /docs/oauth2-redirect, Methods: HEAD, GET
INFO 11-20 06:32:45 launcher.py:27] Route: /redoc, Methods: HEAD, GET
INFO 11-20 06:32:45 launcher.py:27] Route: /health, Methods: GET
INFO 11-20 06:32:45 launcher.py:27] Route: /tokenize, Methods: POST
INFO 11-20 06:32:45 launcher.py:27] Route: /detokenize, Methods: POST
INFO 11-20 06:32:45 launcher.py:27] Route: /v1/models, Methods: GET
INFO 11-20 06:32:45 launcher.py:27] Route: /version, Methods: GET
INFO 11-20 06:32:45 launcher.py:27] Route: /v1/chat/completions, Methods: POST
INFO 11-20 06:32:45 launcher.py:27] Route: /v1/completions, Methods: POST
INFO 11-20 06:32:45 launcher.py:27] Route: /v1/embeddings, Methods: POST
INFO:     Started server process [1]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://<IP_ADDRESS>:8000 (Press CTRL+C to quit)
INFO 11-20 06:32:55 metrics.py:449] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 11-20 06:33:05 metrics.py:449] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 11-20 06:33:15 metrics.py:449] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 11-20 06:33:18 logger.py:37] Received request cmpl-3706abf5f25b4d21a79004dd76c22c0f-0: prompt: 'San Francisco is a', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=7, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: [2, 16033, 2659, 16, 10], lora_request: None, prompt_adapter_request: None.
INFO 11-20 06:33:18 async_llm_engine.py:208] Added request cmpl-3706abf5f25b4d21a79004dd76c22c0f-0.
DEBUG 11-20 06:33:18 async_llm_engine.py:836] Waiting for new requests...
DEBUG 11-20 06:33:18 async_llm_engine.py:855] Got new requests!
ERROR 11-20 06:33:19 multiproc_worker_utils.py:116] Worker VllmWorkerProcess pid 29 died, exit code: 1
INFO 11-20 06:33:19 multiproc_worker_utils.py:120] Killing local vLLM worker processes
ERROR 11-20 06:33:19 async_llm_engine.py:65] Engine background task failed
ERROR 11-20 06:33:19 async_llm_engine.py:65] Traceback (most recent call last):
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion
ERROR 11-20 06:33:19 async_llm_engine.py:65]     return_value = task.result()
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 872, in run_engine_loop
ERROR 11-20 06:33:19 async_llm_engine.py:65]     result = task.result()
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 795, in engine_step
ERROR 11-20 06:33:19 async_llm_engine.py:65]     request_outputs = await self.engine.step_async(virtual_engine)
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 347, in step_async
ERROR 11-20 06:33:19 async_llm_engine.py:65]     outputs = await self.model_executor.execute_model_async(
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 298, in execute_model_async
ERROR 11-20 06:33:19 async_llm_engine.py:65]     output = await make_async(self.execute_model
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
ERROR 11-20 06:33:19 async_llm_engine.py:65]     result = self.fn(*self.args, **self.kwargs)
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 210, in execute_model
ERROR 11-20 06:33:19 async_llm_engine.py:65]     output = self.driver_method_invoker(self.driver_worker,
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 311, in _async_driver_method_invoker
ERROR 11-20 06:33:19 async_llm_engine.py:65]     return driver.execute_method(method, *args, **kwargs).get()
ERROR 11-20 06:33:19 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 54, in get
ERROR 11-20 06:33:19 async_llm_engine.py:65]     raise self.result.exception
ERROR 11-20 06:33:19 async_llm_engine.py:65] ChildProcessError: worker died
Unable to get JIT kernel for brgemm. Params: M=5, N=5, K=64, str_a=1, str_b=1, brgemm_type=1, beta=0, a_trans=0, unroll_hint=1, lda=2304, ldb=5, ldc=5, config=0, b_vnni=02024-11-20 06:33:19,225 - __init__.py - asyncio - ERROR - Exception in callback functools.partial(<function _log_task_completion at 0x7646b03997e0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7646ae8d3310>>)
handle: <Handle functools.partial(<function _log_task_completion at 0x7646b03997e0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7646ae8d3310>>)>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion
    return_value = task.result()
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 872, in run_engine_loop
    result = task.result()
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 795, in engine_step
    request_outputs = await self.engine.step_async(virtual_engine)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 347, in step_async
    outputs = await self.model_executor.execute_model_async(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 298, in execute_model_async
    output = await make_async(self.execute_model
  File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 210, in execute_model
    output = self.driver_method_invoker(self.driver_worker,
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 311, in _async_driver_method_invoker
    return driver.execute_method(method, *args, **kwargs).get()
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 54, in get
    raise self.result.exception
ChildProcessError: worker died

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

Traceback (most recent call last):
  File "uvloop/cbhandles.pyx", line 63, in uvloop.loop.Handle._run
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 67, in _log_task_completion
    raise AsyncEngineDeadError(
vllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for the actual cause.
INFO:     <IP_ADDRESS>:37946 - "POST /v1/completions HTTP/1.1" 500 Internal Server Error
ERROR:    Exception in ASGI application
  + Exception Group Traceback (most recent call last):
  |   File "/usr/local/lib/python3.10/dist-packages/starlette/_utils.py", line 76, in collapse_excgroups
  |     yield
  |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 186, in __call__
  |     async with anyio.create_task_group() as task_group:
  |   File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 763, in __aexit__
  |     raise BaseExceptionGroup(
  | exceptiongroup.ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)
  +-+---------------- 1 ----------------
    | Traceback (most recent call last):
    |   File "/usr/local/lib/python3.10/dist-packages/uvicorn/protocols/http/httptools_impl.py", line 401, in run_asgi
    |     result = await app(  # type: ignore[func-returns-value]
    |   File "/usr/local/lib/python3.10/dist-packages/uvicorn/middleware/proxy_headers.py", line 60, in __call__
    |     return await self.app(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/fastapi/applications.py", line 1054, in __call__
    |     await super().__call__(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/applications.py", line 113, in __call__
    |     await self.middleware_stack(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/errors.py", line 187, in __call__
    |     raise exc
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/errors.py", line 165, in __call__
    |     await self.app(scope, receive, _send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 185, in __call__
    |     with collapse_excgroups():
    |   File "/usr/lib/python3.10/contextlib.py", line 153, in __exit__
    |     self.gen.throw(typ, value, traceback)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/_utils.py", line 82, in collapse_excgroups
    |     raise exc
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 187, in __call__
    |     response = await self.dispatch_func(request, call_next)
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 491, in add_request_id
    |     response = await call_next(request)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 163, in call_next
    |     raise app_exc
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 149, in coro
    |     await self.app(scope, receive_or_disconnect, send_no_error)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/cors.py", line 85, in __call__
    |     await self.app(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/exceptions.py", line 62, in __call__
    |     await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    |     raise exc
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    |     await app(scope, receive, sender)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 715, in __call__
    |     await self.middleware_stack(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 735, in app
    |     await route.handle(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 288, in handle
    |     await self.app(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 76, in app
    |     await wrap_app_handling_exceptions(app, request)(scope, receive, send)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    |     raise exc
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    |     await app(scope, receive, sender)
    |   File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 73, in app
    |     response = await f(request)
    |   File "/usr/local/lib/python3.10/dist-packages/fastapi/routing.py", line 301, in app
    |     raw_response = await run_endpoint_function(
    |   File "/usr/local/lib/python3.10/dist-packages/fastapi/routing.py", line 212, in run_endpoint_function
    |     return await dependant.call(**values)
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 367, in create_completion
    |     generator = await handler.create_completion(request, raw_request)
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/serving_completion.py", line 189, in create_completion
    |     async for i, res in result_generator:
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/utils.py", line 444, in merge_async_iterators
    |     item = await d
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 1051, in generate
    |     async for output in await self.add_request(
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 113, in generator
    |     raise result
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion
    |     return_value = task.result()
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 872, in run_engine_loop
    |     result = task.result()
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 795, in engine_step
    |     request_outputs = await self.engine.step_async(virtual_engine)
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 347, in step_async
    |     outputs = await self.model_executor.execute_model_async(
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 298, in execute_model_async
    |     output = await make_async(self.execute_model
    |   File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
    |     result = self.fn(*self.args, **self.kwargs)
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 210, in execute_model
    |     output = self.driver_method_invoker(self.driver_worker,
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 311, in _async_driver_method_invoker
    |     return driver.execute_method(method, *args, **kwargs).get()
    |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 54, in get
    |     raise self.result.exception
    | ChildProcessError: worker died
    +------------------------------------

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/uvicorn/protocols/http/httptools_impl.py", line 401, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
  File "/usr/local/lib/python3.10/dist-packages/uvicorn/middleware/proxy_headers.py", line 60, in __call__
    return await self.app(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/applications.py", line 113, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/errors.py", line 187, in __call__
    raise exc
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/errors.py", line 165, in __call__
    await self.app(scope, receive, _send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 185, in __call__
    with collapse_excgroups():
  File "/usr/lib/python3.10/contextlib.py", line 153, in __exit__
    self.gen.throw(typ, value, traceback)
  File "/usr/local/lib/python3.10/dist-packages/starlette/_utils.py", line 82, in collapse_excgroups
    raise exc
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 187, in __call__
    response = await self.dispatch_func(request, call_next)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 491, in add_request_id
    response = await call_next(request)
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 163, in call_next
    raise app_exc
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 149, in coro
    await self.app(scope, receive_or_disconnect, send_no_error)
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/cors.py", line 85, in __call__
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/exceptions.py", line 62, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    raise exc
  File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    await app(scope, receive, sender)
  File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 715, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 735, in app
    await route.handle(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 288, in handle
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 76, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    raise exc
  File "/usr/local/lib/python3.10/dist-packages/starlette/_exception_handler.py", line 42, in wrapped_app
    await app(scope, receive, sender)
  File "/usr/local/lib/python3.10/dist-packages/starlette/routing.py", line 73, in app
    response = await f(request)
  File "/usr/local/lib/python3.10/dist-packages/fastapi/routing.py", line 301, in app
    raw_response = await run_endpoint_function(
  File "/usr/local/lib/python3.10/dist-packages/fastapi/routing.py", line 212, in run_endpoint_function
    return await dependant.call(**values)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 367, in create_completion
    generator = await handler.create_completion(request, raw_request)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/serving_completion.py", line 189, in create_completion
    async for i, res in result_generator:
  File "/usr/local/lib/python3.10/dist-packages/vllm/utils.py", line 444, in merge_async_iterators
    item = await d
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 1051, in generate
    async for output in await self.add_request(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 113, in generator
    raise result
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion
    return_value = task.result()
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 872, in run_engine_loop
    result = task.result()
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 795, in engine_step
    request_outputs = await self.engine.step_async(virtual_engine)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 347, in step_async
    outputs = await self.model_executor.execute_model_async(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 298, in execute_model_async
    output = await make_async(self.execute_model
  File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 210, in execute_model
    output = self.driver_method_invoker(self.driver_worker,
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/cpu_executor.py", line 311, in _async_driver_method_invoker
    return driver.execute_method(method, *args, **kwargs).get()
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 54, in get
    raise self.result.exception
ChildProcessError: worker died
INFO 11-20 06:33:25 metrics.py:449] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 11-20 06:33:35 metrics.py:449] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.

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.

cc @bigPYJ1151

Hmm, please run lscpu and let me check instruction sets on your platform. I exactly followed the steps and try to reproduce the bug on my platform, but both AVX512 and AVX2 versions worked well.

Architecture:             x86_64
  CPU op-mode(s):         32-bit, 64-bit
  Address sizes:          48 bits physical, 48 bits virtual
  Byte Order:             Little Endian
CPU(s):                   8
  On-line CPU(s) list:    0-7
Vendor ID:                AuthenticAMD
  Model name:             AMD EPYC 7B12
    CPU family:           23
    Model:                49
    Thread(s) per core:   2
    Core(s) per socket:   4
    Socket(s):            1
    Stepping:             0
    BogoMIPS:             4499.99
    Flags:                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fx
                          sr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl
                           nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4
                          _2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_leg
                          acy abm sse4a misalignsse 3dnowprefetch osvw topoext ssbd ibrs ibpb stibp vmmcall fsgs
                          base tsc_adjust bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xs
                          avec xgetbv1 clzero xsaveerptr arat npt nrip_save umip rdpid
Virtualization features:
  Hypervisor vendor:      KVM
  Virtualization type:    full
Caches (sum of all):
  L1d:                    128 KiB (4 instances)
  L1i:                    128 KiB (4 instances)
  L2:                     2 MiB (4 instances)
  L3:                     16 MiB (1 instance)
NUMA:
  NUMA node(s):           1
  NUMA node0 CPU(s):      0-7
Vulnerabilities:
  Gather data sampling:   Not affected
  Itlb multihit:          Not affected
  L1tf:                   Not affected
  Mds:                    Not affected
  Meltdown:               Not affected
  Mmio stale data:        Not affected
  Reg file data sampling: Not affected
  Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
  Spec rstack overflow:   Mitigation; Safe RET
  Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
  Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Spectre v2:             Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS No
                          t affected; BHI Not affected
  Srbds:                  Not affected
  Tsx async abort:        Not affected

Same here with docker and Interl CPU. Trying a Qwen2.5 infrence with AWQ format.
"""
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        39 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               8
On-line CPU(s) list:                  0-7
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU E3-1270 v6 @ 3.80GHz
CPU family:                           6
Model:                                158
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
Stepping:                             9
CPU(s) scaling MHz:                   95%
CPU max MHz:                          4200.0000
CPU min MHz:                          800.0000
BogoMIPS:                             7599.80
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm
constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1
sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb pti ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsba
se tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi m
d_clear flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            128 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             1 MiB (4 instances)
L3 cache:                             8 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-7
Vulnerability Gather data sampling:   Mitigation; Microcode
Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
Vulnerability L1tf:                   Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; 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; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Mitigation; Microcode
Vulnerability Tsx async abort:        Mitigation; TSX disabled
"""

@imkebe Can you help to run the offline_inference.py with your model configurations and gather oneDNN logs on your platform? Because I can't reproduce this problem on new CPU types. Steps to run:

Modify examples/offline_inference.py as your configuration.
ONEDNN_VERBOSE=all python3 examples/offline_inference.py > output.log

In output.log, you would see something likes:
Processed prompts:   0%|          | 0/4 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]onednn_verbose,info,oneDNN v3.5.3 (commit 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
onednn_verbose,info,cpu,runtime:OpenMP,nthr:96
onednn_verbose,info,cpu,isa:Intel AVX-512 with float16, Intel DL Boost and bfloat16 support and Intel AMX with bfloat16 and 8-bit integer support
onednn_verbose,info,gpu,runtime:none
onednn_verbose,info,graph,backend,0:dnnl_backend
onednn_verbose,primitive,info,template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time
onednn_verbose,graph,info,template:operation,engine,partition_id,partition_kind,op_names,data_formats,logical_tensors,fpmath_mode,backend,exec_time
onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg_matmul:avx10_1_512_amx_fp16,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x2304,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,primitive,create:dispatch,brgemm_matmul,datatype configuration not supported on this isa,src/cpu/x64/matmul/brgemm_matmul_utils.cpp:1178
onednn_verbose,primitive,create:cache_miss,cpu,matmul,brg_matmul:avx10_1_512,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x2304,1.38989
onednn_verbose,primitive,exec,cpu,matmul,brg_matmul:avx10_1_512,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x2304,1.58203
onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg_matmul:avx10_1_512_amx_fp16,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x768,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,primitive,create:dispatch,brgemm_matmul,datatype configuration not supported on this isa,src/cpu/x64/matmul/brgemm_matmul_utils.cpp:1178
onednn_verbose,primitive,create:cache_miss,cpu,matmul,brg_matmul:avx10_1_512,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x768,1.19507
onednn_verbose,primitive,exec,cpu,matmul,brg_matmul:avx10_1_512,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x768,0.446045
onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg_matmul:avx10_1_512_amx_fp16,undef,src_f16::blocked:ab::f0 wei_f16::blocked:ba::f0 dst_f16::blocked:ab::f0,attr-scratchpad:user attr-post-ops:sum,,26x768:768x3072,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,primitive,create:dispatch,brgemm_matmul,datatype configuration not supported on this isa,src/cpu/x64/matmul/brgemm_matmul_utils.cpp:1178
...

The log will explain the cause of the error.

@bigPYJ1151 this is the output, same as before:
ONEDNN_VERBOSE=all python3 examples/offline_inference.py
INFO 11-21 02:36:07 importing.py:15] Triton not installed or not compatible; certain GPU-related functions will not be available.
INFO 11-21 02:36:08 __init__.py:31] No plugins found.
INFO 11-21 02:36:08 __init__.py:31] No plugins found.
WARNING 11-21 02:36:15 _logger.py:72] [DEPRECATED] Block manager v1 has been removed, and setting --use-v2-block-manager to True or False has no effect on vLLM behavior. Please remove --use-v2-block-manager in your engine argument. If your use case is not supported by SelfAttnBlockSpaceManager (i.e. block manager v2), please file an issue with detailed information.
WARNING 11-21 02:36:15 _logger.py:72] Async output processing is only supported for CUDA, TPU, XPU and HPU.Disabling it for other platforms.
WARNING 11-21 02:36:15 _logger.py:72] CUDA graph is not supported on CPU, fallback to the eager mode.
WARNING 11-21 02:36:15 _logger.py:72] Environment variable VLLM_CPU_KVCACHE_SPACE (GB) for CPU backend is not set, using 4 by default.
INFO 11-21 02:36:15 llm_engine.py:249] Initializing an LLM engine (v0.6.4.post2.dev22+g47826cac) with config: model='facebook/opt-125m', speculative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cpu, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=facebook/opt-125m, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None, pooler_config=None)
INFO 11-21 02:36:15 __init__.py:31] No plugins found.
INFO 11-21 02:36:15 selector.py:221] Cannot use _Backend.FLASH_ATTN backend on CPU.
INFO 11-21 02:36:15 selector.py:156] Using Torch SDPA backend.
DEBUG 11-21 02:36:15 parallel_state.py:983] world_size=1 rank=0 local_rank=-1 distributed_init_method=tcp://<IP_ADDRESS>:45909 backend=gloo
DEBUG 11-21 02:36:15 decorators.py:84] Inferred dynamic dimensions for forward method of <class 'vllm.model_executor.models.opt.OPTModel'>: ['input_ids', 'positions', 'intermediate_tensors', 'inputs_embeds']
INFO 11-21 02:36:15 weight_utils.py:243] Using model weights format ['*.bin']
Loading pt checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/weight_utils.py:425: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state = torch.load(bin_file, map_location="cpu")
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  3.40it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  3.40it/s]

INFO 11-21 02:36:16 cpu_executor.py:195] # CPU blocks: 7281
Processed prompts:   0%|              | 0/4 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Unable to get JIT kernel for brgemm. Params: M=6, N=6, K=64, str_a=1, str_b=1, brgemm_type=1, beta=0, a_trans=0, unroll_hint=1, lda=2304, ldb=6, ldc=6, config=0, b_vnni=0

@samos123 Hmm... strange, no oneDNN log. Looks like you are using Half dtype, please try to specify dtype=bfloat16 explicitly and try again. I suspect oneDNN may have some bugs of Half GEMM support on legacy CPU types.

Yeah it worked when using dtype=bfloat16
ONEDNN_VERBOSE=all python3 examples/offline_inference.py --dtype bfloat16
INFO 11-21 03:26:50 importing.py:15] Triton not installed or not compatible; certain GPU-related functions will not be available.
INFO 11-21 03:26:51 __init__.py:31] No plugins found.
INFO 11-21 03:26:51 __init__.py:31] No plugins found.
WARNING 11-21 03:26:52 _logger.py:72] Casting torch.float16 to torch.bfloat16.
WARNING 11-21 03:26:57 _logger.py:72] [DEPRECATED] Block manager v1 has been removed, and setting --use-v2-block-manager to True or False has no effect on vLLM behavior. Please remove --use-v2-block-manager in your engine argument. If your use case is not supported by SelfAttnBlockSpaceManager (i.e. block manager v2), please file an issue with detailed information.
WARNING 11-21 03:26:57 _logger.py:72] Async output processing is only supported for CUDA, TPU, XPU and HPU.Disabling it for other platforms.
WARNING 11-21 03:26:57 _logger.py:72] CUDA graph is not supported on CPU, fallback to the eager mode.
WARNING 11-21 03:26:57 _logger.py:72] Environment variable VLLM_CPU_KVCACHE_SPACE (GB) for CPU backend is not set, using 4 by default.
INFO 11-21 03:26:57 llm_engine.py:249] Initializing an LLM engine (v0.6.4.post2.dev22+g47826cac) with config: model='facebook/opt-125m', speculative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cpu, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=facebook/opt-125m, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None, pooler_config=None)
INFO 11-21 03:26:58 __init__.py:31] No plugins found.
INFO 11-21 03:26:58 selector.py:221] Cannot use _Backend.FLASH_ATTN backend on CPU.
INFO 11-21 03:26:58 selector.py:156] Using Torch SDPA backend.
DEBUG 11-21 03:26:58 parallel_state.py:983] world_size=1 rank=0 local_rank=-1 distributed_init_method=tcp://<IP_ADDRESS>:39413 backend=gloo
DEBUG 11-21 03:26:58 decorators.py:84] Inferred dynamic dimensions for forward method of <class 'vllm.model_executor.models.opt.OPTModel'>: ['input_ids', 'positions', 'intermediate_tensors', 'inputs_embeds']
INFO 11-21 03:26:58 weight_utils.py:243] Using model weights format ['*.bin']
Loading pt checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/weight_utils.py:425: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state = torch.load(bin_file, map_location="cpu")
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  2.34it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  2.34it/s]

INFO 11-21 03:26:58 cpu_executor.py:195] # CPU blocks: 7281
Processed prompts:   0%|              | 0/4 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]INFO 11-21 03:27:05 metrics.py:449] Avg prompt throughput: 5.1 tokens/s, Avg generation throughput: 8.6 tokens/s, Running: 4 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%.
DEBUG 11-21 03:27:07 llm_engine.py:1523] Stopping remote worker execution loop.
Processed prompts: 100%|██████| 4/4 [00:06<00:00,  1.72s/it, est. speed input: 3.78 toks/s, output: 9.31 toks/s]
Prompt: 'Hello, my name is', Generated text: ' Richard and I live in Essex, UK.    *"The'

I believe this all worked in v0.6.3 afaik, so unsure what changed.

@imkebe Can you help to run the offline_inference.py with your model configurations and gather oneDNN logs on your platform? Because I can't reproduce this problem on new CPU types.

If you want I can give you direct access for two days to this machine - please e-mail me.

I've noticed that the same issue persists when using dtype=torch.float16. Upon reviewing the recent changes, I believe the issue may be related to the modifications introduced in Pull Request 9911). Specifically, the change in the _verify_and_get_model_config() function) might be the culprit.
For CPU execution, when the data type is set to float16, it gets converted to bfloat16. However, with this pull request, that conversion code was removed.


Convert cool format to dense contact matrix

Hi,
I was wondering if there is any way to convert .cool format to dense contact matrix for following analysis such as TopDom? Thanks a lot!
Best,
Kun

You can use the matrix selector to retrieve dense numpy arrays in Python. From there, you can output whatever dense format TopDom needs. e.g. np.savetxt, or np.save.
Cooltools also has a command line tool dump-cworld to output the dense text format used by the Dekker lab's C-world tools, which might be what you want (tab-separated).

Understand~Thanks a lot!


Firehose metrics add subsystem, create override_default_tags

Description

Added subsystem value to common attributes of firehose metrics
Added override_default_tags to allow users to override the default tags we set

How Has This Been Tested?
Local testing with our AWS account
Checklist:

[ ] I have updated the relevant example in the examples directory for the module.
[ ] I have updated the relevant test file for the module.
[ ] This change does not affect module (e.g. it's readme file change)

Hey @ryantanjunming just an idea, perhaps var.override_default_tags should be a boolean. var.user_supplied_tags remains the variable for user input. The former just changes the behavior of the merge. If true, the user-supplied tags come later in the merge, if false the other way around (which is the default).
Having two maps for user-supplied tags will be confusing.

@macmiranda i'll set it as var.use_default_tags boolean default to true. If false, it will remove the default tags and leave just the var.user_supplied_tags.
So it's possible to have it empty also.


Validator for scientific notation

It would be very useful to have a validator that accepts all number representations, including scientific notation, eg 1.24e-42

I guess you could make a custom validator http://vee-validate.logaretm.com/rules.html#custom-rules which validates this scientific format and use it together with the number validator

Try /[+-]?(?:0|[1-9]\d*)(?:.\d*)?(?:[eE][+-]?\d+)?/

Closing, you can just create a custom validator that does that.


Remove bpf2c.exe from the redist-package project.

The redist-package project should just contain the CLI bpftool.exe utility.

@gtrevi is bpf2c.exe not needed by the customer for converting .o file to .sys file?

@gtrevi is bpf2c.exe not needed by the customer for converting .o file to .sys file?

It's needed in the MSI, but not in the redist, which my understanding is for things that are needed on the runtime machine.


python312Packages.openai: 1.44.0 -> 1.45.1

Description of changes
Update to latest version. No changes that effect build.
Diff: https://github.com/openai/openai-python/compare/refs/tags/v1.44.0...v1.45.0
Changelog: https://github.com/openai/openai-python/releases/tag/v1.45.0
Things done

Built on platform(s)

[ ] x86_64-linux
[x] aarch64-linux
[x] x86_64-darwin
[x] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[x] Tested basic functionality of all binary files (usually in ./result/bin/)
24.11 Release Notes (or backporting 23.11 and 24.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[x] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.


How do I force Restler to try out all different values provided in a custom payload?

... or what would be the best practice to achieve something similar, if custom payloads are not intended for this purpose?
I am testing Restler on a very simple CRUD Api for TodoItems (id: Integer, name: String, isComplete: Boolean) and have provided the following custom payload in the dictionary:
"restler_custom_payload": {
  "name": [
    "fuzzstring", "ultrasuperduperannoyinglylongstringwithwhichtofuzz"
  ]
},

and introduced a very simple bug which I was hoping to "catch" with Restler. Trying to POST or PUT any TodoItem with a name longer than 20 characters returns 501.
After 5 minutes in fuzz mode, Restler only uses the shorter string for any and all requests and thus does not discover the bug. The one exception and only reason I know the custom payload was correctly compiled is that Restler used the second, longer string once, in a sequence where it POSTed an item with the short name, deleted it and then tried to PUT an item with the long name at the same id. Resulting in a 404 because the item has been deleted.
If I reverse the order in this list, it will try to POST with the long name, get the 501, create a bug bucket, try the short string and (sensibly) use the short name in all further tests.
I would like to know how I can test each value at least once, without needing to manually order them such that the correct ones come last and whether a custom payload is even the right way to do that.
Thanks,
Joris

I just realized the --test_all_combinations flag does what I want, though it only works in Test and Fuzz-Lean modes.
I am keeping the issue open for now because running help or trying to use the flag in Fuzz mode says it only works in Test mode, even though it works perfectly fine in Fuzz-Lean, so that's a very minor documentation error.

@Joris-Kuehl Hi Joris,
In fuzz mode, RESTler is supposed to try all possible value combinations up to max_combinations per request (by default 20) - see https://github.com/microsoft/restler-fuzzer/blob/main/docs/user-guide/SettingsFile.md#max_combinations-int-default-20 -- are you sure that limit is not exceeded?
Yes, test-all-combinations is a new option to force this behavior, that is, trying all value combinations, in test mode (and fuzz-lean mode, which is just test mode with checkers). But it should not be needed in fuzz mode...

@PatGod Hi Patrice,
thank you for the reply. I am fairly sure the limit was not exceeded. I have since made some changes. Namely, the error is now triggered by special characters in the name and is a 500 instead of 501. The most recent setup in which i have verified this uses the following dictionary:
{
  "restler_fuzzable_string": [
    "fuzzstring"
  ],
  "restler_fuzzable_string_unquoted": [],
  "restler_fuzzable_datetime": [
    "2019-06-26T20:20:39+00:00"
  ],
  "restler_fuzzable_datetime_unquoted": [],
  "restler_fuzzable_uuid4": [
    "566048da-ed19-4cd3-8e0a-b7e0e1ec4d72"
  ],
  "restler_fuzzable_uuid4_unquoted": [],
  "restler_fuzzable_int": [
    "1"
  ],
  "restler_fuzzable_number": [
    "1.23"
  ],
  "restler_fuzzable_bool": [
    "true"
  ],
  "restler_fuzzable_object": [
    "{ \"fuzz\": false }"
  ],
  "restler_custom_payload": {
    "name": [
      "namestring", "??????????"
    ],
    "isComplete": [
      "true", "false"
    ]
  },
  "restler_custom_payload_unquoted": {},
  "restler_custom_payload_uuid4_suffix": {}
}

That comes out to a total of 4 combinations for a TodoItem. I have left the settings file at its default max 20 combinations. If I do not add the --test_all_combinations flag, only the combination (1, "namestring", true) is used, even in fuzz mode.
Again, the baffling exception in 15 minutes of fuzzing are the two seperate times where Restler posts, deletes and then tries to put a resource and uses the "??????????" name for the put, getting a 404. Outside of this very specific case, it does not touch the other combinations.
Once I do add the flag, all 4 combinations are properly tried out and the bug is found in fuzz-lean mode. Going through the network testing file, Restler also confirms that there are only 4 combinations - the file says combination 1/4, 2/4, etc.
I have only run fuzz mode for between 1 and 15 minutes though, since it's a very simple Api and I was not expecting to find anything anyway.
Is it possible that Restler does try all combinations, but that is way down on the list of priorities and only happens an hour or two into fuzzing? If it should happen near the beginning of the run though, I could send you the full result folders if you like?

@Joris-Kuehl Hi Joris,
Thank you for your detailed report and additional information.
In fuzz mode, I expected RESTler to try all these combinations as soon as it reaches that POST request the first time (that is, early in the search). But perhaps RESTler treats lists of values with 'rester_custom_payload' differently from lists of values with 'restler_fuzzable_*' -- for the latter, it should definitely try all the listed values in fuzz mode (if this is easy for you, you could try extending your dictionary with multiple values for, say, restler_fuzzable_number, and you should see RESTler trying all these values and combinations in fuzz mode).
Maybe @marina-p might know the explanation...

I have now tried this dictionary, moving the list of names up to restler_fuzzable_string:
{
  "restler_fuzzable_string": [
    "namestring", "??????????"
  ],
  "restler_fuzzable_string_unquoted": [],
  "restler_fuzzable_datetime": [
    "2019-06-26T20:20:39+00:00"
  ],
  "restler_fuzzable_datetime_unquoted": [],
  "restler_fuzzable_uuid4": [
    "566048da-ed19-4cd3-8e0a-b7e0e1ec4d72"
  ],
  "restler_fuzzable_uuid4_unquoted": [],
  "restler_fuzzable_int": [
    "1"
  ],
  "restler_fuzzable_number": [
    "1.23"
  ],
  "restler_fuzzable_bool": [
    "true"
  ],
  "restler_fuzzable_object": [
    "{ \"fuzz\": false }"
  ],
  "restler_custom_payload": {
    "isComplete": [
      "true"
    ]
  },
  "restler_custom_payload_unquoted": {},
  "restler_custom_payload_uuid4_suffix": {}
}

The only reason I was using custom payloads instead of restler_fuzzable_XXXX was, because they overwrite the automatically inferred producer consumer dependency between POST and PUT, specifically of the isComplete field (lazy workaround for #271).
The situation remains the same. Restler now detects two combinations (as it should). In test and fuzz-lean, the bug is only found with the --test_all_combinations flag on. Otherwise Restler stops at the first successful combination.
That remains true for fuzz mode, which did not try the second combination at all in 6 minutes of fuzzing (not even the odd POST,DELETE,PUT sequence I mentioned before). Reading the network.testing file, POST was definitely encountered and used successfully dozens of times.

@Joris-Kuehl Hi Joris,
Thanks for confirming. I actually can confirm this behavior with my own tests.
@marina-p To summarize, fuzz mode now behaves like the test (and fuzz-lean) mode: alternate values (specified in the fuzzing dictionary) are now only considered when a request is failing. Isn't this a regression?
(This would also mean we don't have a unit-test that covers this behavior of the fuzz mode, right?)
Bottom-line: this looks like a new RESTler bug (a regression) to me...

@Joris-Kuehl @PatGod Thank you for the detailed description and repro steps!  This sounds like a bug.  The code is supposed to do the same thing in fuzz mode as in testing all combinations:
# line 265 in driver.py
    # bfs needs to be exhaustive to provide full grammar coverage
    elif Settings().fuzzing_mode in ['bfs', 'bfs-fast', 'test-all-combinations']:

        # This loop will iterate over possible remaining renderings of the
        # current sequence.
...

(and one of the engine unit tests is supposed to test for this behavior).
I will investigate.
Thanks,
Marina

@marina-p This behavior is actually expected because the 'fuzz' mode currently uses the 'bfs-cheap' search strategy - see line 378 of src/driver/Program.fs (https://github.com/microsoft/restler-fuzzer/blob/d8f73524187330c3d8ca12b749996f40d9a64393/src/driver/Program.fs#L378
    let fuzzingParameters =
        (getCommonParameters parameters (Some maxDurationHours))
        @
        [
            "--fuzzing_mode bfs-cheap"
        ]

    return! runRestlerEngine workingDirectory fuzzingParameters pythonFilePath
}

Bfs-cheap is defined in https://patricegodefroid.github.io/public_psfiles/icst2020.pdf and was set as the default in order to explore all the request sequences deeper and faster when using checkers.
To allow users (like @Joris-Kuehl) to force RESTler to explore all renderings in every state (bfs) or to explore all renderings only when visiting a new state for the first time (bfs-fast), the main driver restler.exe, defined in Program.fs, should be extended to let the user overwrite the default search strategy (bfs-cheap) by either bfs or bfs-fast in fuzz mode only. OK?

@marina-p I have just assigned this item to myself and will take care of it.
I suggest the following changes:

in fuzz mode, let's bfs-fast become the default-search strategy.
let's add a new 'search-strategy' option to the main driver restler.exe where the user can instead select bfs or bfs-cheap.

(Note: historically, bfs-cheap become the default search strategy in fuzz mode after the checkers were available but before the new fuzz-lean mode was available: fuzz with bfs-cheap is indeed closest to fuzz-lean, compared to bfs or bfs-fast.)

@PatGod Sounds good.  Thank you!
(I believe it's implied by the above but just in case: from the driver, the new search strategy should work just for Fuzz mode, not Fuzz-Lean)

@Joris-Kuehl PR #288 will set the new default search-strategy to bfs-fast in fuzz mode and will enumerate all the values you specified in your directory as you expected.
This PR will be merged shortly and will close this issue.
Let us know if you have any other comments. Thanks (and sorry for the delay...)!


Try to rebuild for changing timestamp_clock_id?

https://perfetto.dev/docs/concepts/clock-sync#operation
I noticed that this page mentioned multi-clock domain.
However,  I can not find how to rebuild for this "timestamp_clock_id ".
Would you please help to provide more information? or can I change default time by setting??

timestamp_clock_id is a field of the TracePacket proto. Information on it can be found at https://perfetto.dev/docs/reference/trace-packet-proto


Azure SDK for .NET 4.7.2 with CSPack

Library name and version
Azure SDK 2.9
Query/Question
I have a client that is using the Azure SDK 2.9 to create cspkg to deploy applications to classic Cloud Services.
Their current version of the SDK only supports OS Family 4 and they would like to upgrade to enable support for 6.
They are constrained to stay on .NET 4.7.2 for now, so they may not be able to use newer libraries.
Can you please let me know where they can download the latest version of the Azure SDK compatible with .NET 4.7.2 that contains the CSPack tool? (According to the docs, the latest "legacy" SDK is 3.0 and it is compatible with 4.6 and can deploy to OS Family 6, but I'm having difficulties finding it; if a different, newer SDK is available, they are happy to migrate).
Thank you.
Environment
.NET Framework 4.7.2

Label prediction was below confidence level 0.6 for Model:ServiceLabels: 'KeyVault:0.26717564,Tables:0.11703366,Azure.Core:0.046653327'

Hello @UnoSD What was the documentation which you have referred?

https://docs.microsoft.com/en-us/previous-versions/azure/reference/dn479282(v=azure.100)
and
https://docs.microsoft.com/en-us/azure/cloud-services/cloud-services-guestos-update-matrix#sdk-support

Hi @SatishBoddu-MSFT ,
unfortunately the NuGet packages will not help here. They do not contain the CSPack tool and there is no package that is specific to cloud services.
However, I was able to find the response somewhere else: the CSPack tool is included in the Visual Studio installation installing the Azure Workload. Legacy Azure SDK installers are integrated into VS and no longer distributed independently.


Include a more comprehensive template in the opened file

It would be useful to provide more commented template lines in the editor.

Specifically, what more information do you want in there?

A list of possible fields might be nice.

vorbis comments are free form, so you can include any field.
available id3 tags can easily be looked up.
That said, I'd be willing to look at any PRs.


Page rendering gives errors

cannot load pages: ex:

https://github.com/go-echarts/examples/blob/master/examples/page_center_layout.go
https://github.com/go-echarts/examples/blob/master/examples/page_center_layout.go
https://github.com/go-echarts/examples/blob/master/examples/page_flex_layout.go

gives errors where the first chart renders, but the others do not

error messages:
Loaded claygl, version 1.2.1
echarts-gl.min.js:1 Loaded echarts, version 4.1.0
page_none_layout.html:30 Uncaught SyntaxError: Identifier 'goecharts_MZqBadxvuKyl' has already been declared
page_none_layout.html:41 Uncaught SyntaxError: Identifier 'goecharts_MZqBadxvuKyl' has already been declared
page_none_layout.html:52 Uncaught SyntaxError: Identifier 'goecharts_MZqBadxvuKyl' has already been declared
page_none_layout.html:74 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:85 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:96 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:107 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:118 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:129 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:140 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:151 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:162 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:173 Uncaught SyntaxError: Identifier 'goecharts_mhjEmTCPRtms' has already been declared
page_none_layout.html:184

i also noticed this issue trying to recreate the issue on my project

problem noticed in both chrome and firefox

playing around with the generated html - it looks like the generated id for each chart in the page is not unique
manually altering that id for each chart seems to fix the issue.
unsure if this issue should be moved to the main echarts repo

playing around some more - sometimes the unique ids are different
i think potentially the random id generation needs to be a little more random (potentially taking time too much into account?)

moved to https://github.com/go-echarts/go-echarts/issues/139


fix: gcloud client tracing

fix https://github.com/odpf/guardian/issues/345
If we use WithHTTPClient(), then the BigQuery client will be created without applying the WithToken() option. This means that Oauth won't be set up properly.

duplicates with https://github.com/odpf/guardian/pull/346


IpVersionsClientTypeDeferredProcessing/RateLimitQuotaIntegrationTest.MultiRequestWithTokenBucketThrottling/IPv4_GoogleGrpc_WithDeferredProcessing is flakey

IpVersionsClientTypeDeferredProcessing/RateLimitQuotaIntegrationTest.MultiRequestWithTokenBucketThrottling/IPv4_GoogleGrpc_WithDeferredProcessing is flakey and occasionally times out.
[ RUN      ] IpVersionsClientTypeDeferredProcessing/RateLimitQuotaIntegrationTest.MultiRequestWithTokenBucketThrottling/IPv4_GoogleGrpc_WithDeferredProcessing
-- Test timed out at 2024-05-01 14:49:53 UTC --
================================================================================
INFO: Elapsed time: 1157.590s, Critical Path: 1010.71s
INFO: 36757 processes: 20244 remote cache hit, 13646 internal, 1 local, 2866 processwrapper-sandbox.
//test/extensions/filters/http/rate_limit_quota:integration_test        TIMEOUT in 1 out of 2 in 915.0s

Full error
cc @tyxia who is the codeowner of RLQS

Thanks for notification Adi.
The flakiness is expected due to pending stats implementation. I actually have added comment https://github.com/envoyproxy/envoy/blob/6db20c3e6d224a55ec90494428d062bb43a03fdb/test/extensions/filters/http/rate_limit_quota/integration_test.cc#L642-L644.
https://github.com/envoyproxy/envoy/pull/33921 will address the issue here.


HiddenInput fields are visible

Issue Summary
Wagtail's admin CSS doesn't properly style hidden inputs. See the screenshot (the second field, "Entity key").

In markup, it's <input type="hidden"> which is fine, but the label and the whole div still show up.
Steps to Reproduce
class MyForm(forms.Form):
    tip_content = forms.CharField()
    entity_key = forms.CharField(required=False, widget=forms.HiddenInput)

I have confirmed that this issue can be reproduced as described on a fresh Wagtail project: no

Further info
I've worked around this with the following CSS:
.field.hidden_input {
  display: none;
}

Hi @alexgleason,
Is this form rendered with your own template? If so, you should loop over visible_fields and hidden_fields separately (see Django docs), and only use the Wagtail styled includes for the visible ones. As far as I'm aware Wagtail's own forms always do this (see for example https://github.com/wagtail/wagtail/blob/master/wagtail/admin/templates/wagtailadmin/generic/create.html) - if that's not the case, then that's a bug we should fix.

Thanks for the info. I'm experiencing some separate issues now but I'll close this.
After switching to this:

  <form class="draftailmodal" action="/admin/draftailmodal/chooser/" method="post" novalidate>
    {% csrf_token %}
    {% for field in form.hidden_fields %}{{ field }}{% endfor %}

      {% for field in form.visible_fields %}
        {% include "wagtailadmin/shared/field_as_li.html" %}
      {% endfor %}

<input type="submit" value="{% trans 'Save' %}" class="button">

  </form>

Draftail breaks out of the ul tag  and the labels disappear.

Sticking with my hacked CSS for now. I may open a separate issue for the Draftail thing if I can figure out what's actually happening.
Thank you!

Ah, it's entirely possible that the visible_fields / hidden_fields mechanism is treating the Draftail widget as a hidden field, because that's what the underlying <input> field is. Our chooser widget classes define is_hidden = False to override this, and the Draftail widget may well need this added too...
https://github.com/wagtail/wagtail/blob/a9f33aa99eae37824eb604b6146f7faaa523bbfa/wagtail/admin/widgets.py#L116-L118

Fixed in #5583


Add support for Universal Actions

With Universal Actions, we can create QR codes directly from selected text or clipboard history entries. I don’t typically submit the compiled Workflow file for PRs, but the code isn’t in the repo.
No code was touched.

Edited the Workflow to add the Universal Actions.
Added a line about them to the About.
Added a line about them to the REAME.
Bumped the version to 1.0.3.

@TorreyBetts Small ping to check if you’re interested in the change. This is a good workflow to include in the upcoming Alfred Gallery (blog; forum).
I’d rather have submitted this as an update to the info.plist, but you don’t include it in the repo. If it helps, I’m a member of the Alfred team (see the official org on GitHub for confirmation).

@vitorgalvao Yikes, sorry I hadn't realize this had been sitting here. I'll merge the request. I'll also update this repo to include the source, should you want to go back and edit the plist.


Unicode error during 'kegbot upgrade'

Does anyone know how I can get past this error?
(kb)kegbot@kegbot-server:~/.kegbot$ kegbot upgrade
Upgrading from 1.0.2 to 1.1.1
--- Running command: syncdb --noinput -v 0
--- Running command: migrate -v 0
--- Running command: regen_stats
Traceback (most recent call last):+++++++++++++++++++++++++++++          ] 710/924
File "/home/kegbot/kb/bin/kegbot", line 39, in
management.execute_from_command_line()
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/core/management/init.py", line 399, in execute_from_command_line
utility.execute()
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/core/management/init.py", line 392, in execute
self.fetch_command(subcommand).run_from_argv(self.argv)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/core/management/base.py", line 242, in run_from_argv
self.execute(*args, **options.dict)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/core/management/base.py", line 285, in execute
output = self.handle(*args, **options)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/management/commands/upgrade.py", line 95, in handle
run(regen_stats.Command())
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/management/commands/upgrade.py", line 46, in run
cmd.run_from_argv([sys.argv[0], cmdname] + args)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/core/management/base.py", line 242, in run_from_argv
self.execute(*args, **options.dict)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/core/management/base.py", line 285, in execute
output = self.handle(*args, **options)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/django/db/transaction.py", line 371, in inner
return func(*args, **kwargs)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/management/commands/regen_stats.py", line 42, in handle
stats.rebuild_from_id(0, cb=cb)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/stats.py", line 223, in rebuild_from_id
_build_all_views(drink, results_cache=results_cache)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/stats.py", line 292, in _build_all_views
stats = _build_single_view(drink, view, prior_stats=prior_stats)
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/stats.py", line 238, in _build_single_view
logger.debug('>>> Building stats for {}'.format(view))
File "/home/kegbot/kb/local/lib/python2.7/site-packages/pykeg/core/stats.py", line 44, in str
ret += 'user={} '.format(self.user.username)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xf1' in position 2: ordinal not in range(128)

Fix in v1.2.2


Big codestyle reformatting

TL;DR:

Ran Black and isort
Configured pre-commit to run them
Started writing CONTRIBUTING.md

flake8 incoming, but I need to manually fix the issues first
EDIT: now reformatted for flake8

shipit


[$1000] Web - Group Chat - Email address not displayed when hover over the user who has long Display name

If you haven’t already, check out our contributing guidelines for onboarding and email<EMAIL_ADDRESS>to request to join our Slack channel!

Action Performed:

Navigate to staging.new.expensify.com
Create a group with the user with very long Display name & other users
Go to the group chat
Go to details
Hover over the user who has long Display name

Expected Result:
When hover over the user who has long name also email address should be showing
Actual Result:
Email address not displayed when hover over the user who has very long Display name
Workaround:
Unknown
Platforms:

Which of our officially supported platforms is this issue occurring on?

[ ] Android / native
[ ] Android / Chrome
[ ] iOS / native
[ ] iOS / Safari
[x] Windows / Chrome
[ ] MacOS / Desktop

Version Number: <IP_ADDRESS>
Reproducible in staging?: Yes
Reproducible in production?: Yes
If this was caught during regression testing, add the test name, ID and link from TestRail:
Email or phone of affected tester (no customers):
Logs: https://stackoverflow.com/c/expensify/questions/4856
Notes/Photos/Videos: Any additional supporting documentation
https://github.com/Expensify/App/assets/78819774/e645e169-1eae-41fe-a5c4-679ab37d2f51
Expensify/Expensify Issue URL:
Issue reported by: Applause - Internal Team
Slack conversation:
Upwork Automation - Do Not Edit

Upwork Job URL: https://www.upwork.com/jobs/~013107871e59432037
Upwork Job ID:<PHONE_NUMBER>393382401
Last Price Increase: 2023-07-20

Proposal
Name field not being passed from BE until the user with lo0ng name send a message

k

Contributor details
Your Expensify account email<EMAIL_ADDRESS>Upwork Profile Link: https://www.upwork.com/fl/rehanghafoor

This is a BE issue since the login is not passed from the backend for a user whom the current user has not chatted before.
Try these steps:
1.Create a new user with a very long name.
2.Go to some other user and create a group with the long name user and some other users using their emails.
3.We will get the problem as mentioned in this issue.
4.Go to new chat and create a new chat with the long name user using his email.
5.Chat opens and still the same error.
If the current user is creating a group or a simple chat with a user using his email, the email should be shown because the current user already knows his email.
https://github.com/Expensify/App/assets/36226639/7956f425-1f1b-4af2-b78d-d90312c7b01c

@ArekChr mind reviewing this proposal today? 🙇♂

Hey @dylanexpensify, I'm not able today, tomorrow I'm OOO, I will be back on Monday

@ArekChr sounds good! Looking forward to the recap today!

@dylanexpensify When trying to reproduce this error, I see no issue on staging. All users with long display names have displayed email as well.
https://github.com/Expensify/App/assets/24796318/24f7fec2-a0eb-4d65-b090-f9babe2e11e3

@ArekChr I'm still able to reproduce in staging. Pre-condition: the user that you hovered hasn't sent any message before and isn't a member of any workspace that you are a member.
Screencast.from.24-07-2<IP_ADDRESS>.webm

@dukenv0307 Could you test the profiles with long display names?

I have created some fresh accounts with no messages sent, still can't reproduce that error.
Are there any specific reproduction steps different from the issue description?
https://github.com/Expensify/App/assets/24796318/71809270-9b2a-457a-84a3-383e3ff29e8c

I see now for the same organization, address email is always displayed. Tested with another email and the bug is reproducible

Proposal
Please re-state the problem that we are trying to solve in this issue.
Web - Group Chat - Email address not displayed when hover over the user who doesn't chat with the current user before.
What is the root cause of that problem?
It's expected not to send login from backend as this is just new chat and user hasn't sent any message yet. That makes email is hidden when hovering the avatar of this user
https://github.com/Expensify/App/blob/e87f2eb32587d29d8b6423fcadc8fe862c73be2d/src/components/UserDetailsTooltip/index.web.js#L38
What changes do you think we should make in order to solve the problem?
We can set subtitle as Hidden if login field isn't already in personal detail of this user. That is similar subtitle when we click on avatar to see participants of the group chat
const subtitle = (userLogin || '').trim() && !_.isEqual(LocalePhoneNumber.formatPhoneNumber(userLogin || ''), userDisplayName) ? Str.removeSMSDomain(userLogin) : 'Hidden';

https://github.com/Expensify/App/blob/e87f2eb32587d29d8b6423fcadc8fe862c73be2d/src/components/UserDetailsTooltip/index.web.js#L38

What alternative solutions did you explore? (Optional)
NA
Result

@dukenv0307 Different label is needed based on localization

Different label is needed based on localization

@ArekChr Sorry, I don't understand

I mean this sentence:

We can set subtitle as Hidden if login field isn't already in personal detail of this user. That is similar subtitle when we click > on avatar to see participants of the group chat

The hidden label can not be set because when the app language is Espanol needs to be different. Localize is needed

@ArekChr When we change the language and open participant page, subtitle is still Hidden. So I think we shouldn't translate this label to consistent with other page.

@dukenv0307 I think this is another bug then. @dylanexpensify could you confirm?

reviewing shortly!

agree - that is another bug!

@dukenv0307 Looks good to me 👌
:ribbon: :eyes: :ribbon: C+ reviewed

I confirmed with @puneetlath but the lack of subtitle text in this case is by design, so going to close this out.


empty commit

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Project coverage is 96.18%. Comparing base (2bf4edd) to head (7eb31c7).
Report is 1 commits behind head on master.

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Additional details and impacted files
@@            Coverage Diff             @@
##           master      #10      +/-   ##
==========================================
- Coverage   96.18%   96.18%   -0.01%
==========================================
  Files          10       10
  Lines         865      864       -1
==========================================
- Hits          832      831       -1
  Misses         33       33

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