[Bug] RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'

先决条件

[X] 我已经搜索过 问题 和 讨论 但未得到预期的帮助。
[X] 错误在 最新版本 中尚未被修复。

问题类型
我正在使用官方支持的任务/模型/数据集进行评估。
环境
/opt/miniconda/lib/python3.10/site-packages/torch/cuda/init.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
return torch._C._cuda_getDeviceCount() > 0
{'CUDA available': False,
'GCC': 'gcc (GCC) 10.2.0',
'MMEngine': '0.8.4',
'OpenCV': '4.8.0',
'PyTorch': '1.13.1+cu117',
'PyTorch compiling details': 'PyTorch built with:\n'
'  - GCC 9.3\n'
'  - C++ Version: 201402\n'
'  - Intel(R) Math Kernel Library Version '
'2020.0.0 Product Build 20191122 for Intel(R) 64 '
'architecture applications\n'
'  - Intel(R) MKL-DNN v2.6.0 (Git Hash '
'52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n'
'  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n'
'  - LAPACK is enabled (usually provided by '
'MKL)\n'
'  - NNPACK is enabled\n'
'  - CPU capability usage: AVX2\n'
'  - Build settings: BLAS_INFO=mkl, '
'BUILD_TYPE=Release, CUDA_VERSION=11.7, '
'CUDNN_VERSION=8.5.0, '
'CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, '
'CXX_FLAGS= -fabi-version=11 -Wno-deprecated '
'-fvisibility-inlines-hidden -DUSE_PTHREADPOOL '
'-fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM '
'-DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK '
'-DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE '
'-DEDGE_PROFILER_USE_KINETO -O2 -fPIC '
'-Wno-narrowing -Wall -Wextra '
'-Werror=return-type -Werror=non-virtual-dtor '
'-Wno-missing-field-initializers '
'-Wno-type-limits -Wno-array-bounds '
'-Wno-unknown-pragmas -Wunused-local-typedefs '
'-Wno-unused-parameter -Wno-unused-function '
'-Wno-unused-result -Wno-strict-overflow '
'-Wno-strict-aliasing '
'-Wno-error=deprecated-declarations '
'-Wno-stringop-overflow -Wno-psabi '
'-Wno-error=pedantic -Wno-error=redundant-decls '
'-Wno-error=old-style-cast '
'-fdiagnostics-color=always -faligned-new '
'-Wno-unused-but-set-variable '
'-Wno-maybe-uninitialized -fno-math-errno '
'-fno-trapping-math -Werror=format '
'-Werror=cast-function-type '
'-Wno-stringop-overflow, LAPACK_INFO=mkl, '
'PERF_WITH_AVX=1, PERF_WITH_AVX2=1, '
'PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, '
'USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, '
'USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, '
'USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, '
'USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n',
'Python': '3.10.12 (main, Jul  5 2023, 18:54:27) [GCC 11.2.0]',
'TorchVision': '0.14.1+cu117',
'numpy_random_seed':<PHONE_NUMBER>,
'opencompass': '0.1.2+b2d602f',
'sys.platform': 'linux'}
重现问题 - 代码/配置示例
pls
重现问题 - 命令或脚本
CUDA_VISIBLE_DEVICES=5  python /home/opencompass/run.py /home/opencompass/configs/eval_demo.py -w /home/opencompass/outputs/demo --debug
重现问题 - 错误信息
/opt/miniconda/lib/python3.10/site-packages/torch/cuda/__init__.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
  return torch._C._cuda_getDeviceCount() > 0
/opt/miniconda/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments
  warnings.warn(
08/26 21:38:14 - OpenCompass - INFO - Partitioned into 40 tasks.
08/26 21:38:14 - OpenCompass - DEBUG - Task 0: [opt350m/siqa_0]
08/26 21:38:14 - OpenCompass - DEBUG - Task 1: [opt350m/siqa_1]
08/26 21:38:14 - OpenCompass - DEBUG - Task 2: [opt350m/siqa_2]
08/26 21:38:14 - OpenCompass - DEBUG - Task 3: [opt350m/siqa_3]
08/26 21:38:14 - OpenCompass - DEBUG - Task 4: [opt350m/siqa_4]
08/26 21:38:14 - OpenCompass - DEBUG - Task 5: [opt350m/siqa_5]
08/26 21:38:14 - OpenCompass - DEBUG - Task 6: [opt350m/siqa_6]
08/26 21:38:14 - OpenCompass - DEBUG - Task 7: [opt350m/siqa_7]
08/26 21:38:14 - OpenCompass - DEBUG - Task 8: [opt350m/siqa_8]
08/26 21:38:14 - OpenCompass - DEBUG - Task 9: [opt350m/siqa_9]
08/26 21:38:14 - OpenCompass - DEBUG - Task 10: [opt350m/siqa_10]
08/26 21:38:14 - OpenCompass - DEBUG - Task 11: [opt350m/siqa_11]
08/26 21:38:14 - OpenCompass - DEBUG - Task 12: [opt350m/siqa_12]
08/26 21:38:14 - OpenCompass - DEBUG - Task 13: [opt350m/siqa_13]
08/26 21:38:14 - OpenCompass - DEBUG - Task 14: [opt350m/siqa_14]
08/26 21:38:14 - OpenCompass - DEBUG - Task 15: [opt350m/siqa_15]
08/26 21:38:14 - OpenCompass - DEBUG - Task 16: [opt350m/siqa_16]
08/26 21:38:14 - OpenCompass - DEBUG - Task 17: [opt350m/siqa_17]
08/26 21:38:14 - OpenCompass - DEBUG - Task 18: [opt350m/siqa_18]
08/26 21:38:14 - OpenCompass - DEBUG - Task 19: [opt350m/siqa_19]
08/26 21:38:14 - OpenCompass - DEBUG - Task 20: [opt125m/siqa_0]
08/26 21:38:14 - OpenCompass - DEBUG - Task 21: [opt125m/siqa_1]
08/26 21:38:14 - OpenCompass - DEBUG - Task 22: [opt125m/siqa_2]
08/26 21:38:14 - OpenCompass - DEBUG - Task 23: [opt125m/siqa_3]
08/26 21:38:14 - OpenCompass - DEBUG - Task 24: [opt125m/siqa_4]
08/26 21:38:14 - OpenCompass - DEBUG - Task 25: [opt125m/siqa_5]
08/26 21:38:14 - OpenCompass - DEBUG - Task 26: [opt125m/siqa_6]
08/26 21:38:14 - OpenCompass - DEBUG - Task 27: [opt125m/siqa_7]
08/26 21:38:14 - OpenCompass - DEBUG - Task 28: [opt125m/siqa_8]
08/26 21:38:14 - OpenCompass - DEBUG - Task 29: [opt125m/siqa_9]
08/26 21:38:14 - OpenCompass - DEBUG - Task 30: [opt125m/siqa_10]
08/26 21:38:14 - OpenCompass - DEBUG - Task 31: [opt125m/siqa_11]
08/26 21:38:14 - OpenCompass - DEBUG - Task 32: [opt125m/siqa_12]
08/26 21:38:14 - OpenCompass - DEBUG - Task 33: [opt125m/siqa_13]
08/26 21:38:14 - OpenCompass - DEBUG - Task 34: [opt125m/siqa_14]
08/26 21:38:14 - OpenCompass - DEBUG - Task 35: [opt125m/siqa_15]
08/26 21:38:14 - OpenCompass - DEBUG - Task 36: [opt125m/siqa_16]
08/26 21:38:14 - OpenCompass - DEBUG - Task 37: [opt125m/siqa_17]
08/26 21:38:14 - OpenCompass - DEBUG - Task 38: [opt125m/siqa_18]
08/26 21:38:14 - OpenCompass - DEBUG - Task 39: [opt125m/siqa_19]
08/26 21:38:14 - OpenCompass - DEBUG - Modules of opencompass's task registry have been automatically imported from opencompass.tasks
08/26 21:38:14 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"
08/26 21:38:14 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer
/opt/miniconda/lib/python3.10/site-packages/torch/cuda/__init__.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
  return torch._C._cuda_getDeviceCount() > 0
08/26 21:38:22 - OpenCompass - INFO - Task [opt350m/siqa_0]
/opt/miniconda/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments
  warnings.warn(
/opt/miniconda/lib/python3.10/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')
Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/social_i_qa/674d85e42ac7430d3dcd4de7007feaffcb1527c535121e09bab2803fbcc925f8 (last modified on Sat Aug 26 21:21:43 2023) since it couldn't be found locally at social_i_qa., or remotely on the Hugging Face Hub.
08/26 21:38:42 - OpenCompass - INFO - Start inferencing [opt350m/siqa_0]
[2023-08-26 21:38:42,334] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...
  0%|                                                                                | 0/2 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 147, in <module>
    inferencer.run()
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 76, in run
    self._inference()
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 119, in _inference
    inferencer.inference(retriever,
  File "/home/opencompass/opencompass/openicl/icl_inferencer/icl_gen_inferencer.py", line 122, in inference
    results = self.model.generate_from_template(
  File "/home/opencompass/opencompass/models/base.py", line 117, in generate_from_template
    return self.generate(inputs, max_out_len=max_out_len, **kwargs)
  File "/home/opencompass/opencompass/models/huggingface.py", line 145, in generate
    return sum((self._single_generate(
  File "/home/opencompass/opencompass/models/huggingface.py", line 145, in <genexpr>
    return sum((self._single_generate(
  File "/home/opencompass/opencompass/models/huggingface.py", line 213, in _single_generate
    outputs = self.model.generate(input_ids=input_ids,
  File "/opt/miniconda/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/generation/utils.py", line 1515, in generate
    return self.greedy_search(
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/generation/utils.py", line 2332, in greedy_search
    outputs = self(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 938, in forward
    outputs = self.model.decoder(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 651, in forward
    inputs_embeds = self.project_in(inputs_embeds)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 4716) of binary: /opt/miniconda/bin/python
Traceback (most recent call last):
  File "/opt/miniconda/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==1.13.1+cu117', 'console_scripts', 'torchrun')())
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
    return f(*args, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/run.py", line 762, in main
    run(args)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/run.py", line 753, in run
    elastic_launch(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 246, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
/home/opencompass/opencompass/tasks/openicl_infer.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2023-08-26_21:38:44
  host      : e9106e301747
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 4716)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
08/26 21:38:45 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"
08/26 21:38:45 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer
/opt/miniconda/lib/python3.10/site-packages/torch/cuda/__init__.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
  return torch._C._cuda_getDeviceCount() > 0
08/26 21:38:53 - OpenCompass - INFO - Task [opt350m/siqa_1]
/opt/miniconda/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments
  warnings.warn(
/opt/miniconda/lib/python3.10/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')
08/26 21:40:15 - OpenCompass - INFO - Start inferencing [opt350m/siqa_1]
[2023-08-26 21:40:15,223] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...
  0%|                                                                                | 0/2 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 147, in <module>
    inferencer.run()
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 76, in run
    self._inference()
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 119, in _inference
    inferencer.inference(retriever,
  File "/home/opencompass/opencompass/openicl/icl_inferencer/icl_gen_inferencer.py", line 122, in inference
    results = self.model.generate_from_template(
  File "/home/opencompass/opencompass/models/base.py", line 117, in generate_from_template
    return self.generate(inputs, max_out_len=max_out_len, **kwargs)
  File "/home/opencompass/opencompass/models/huggingface.py", line 145, in generate
    return sum((self._single_generate(
  File "/home/opencompass/opencompass/models/huggingface.py", line 145, in <genexpr>
    return sum((self._single_generate(
  File "/home/opencompass/opencompass/models/huggingface.py", line 213, in _single_generate
    outputs = self.model.generate(input_ids=input_ids,
  File "/opt/miniconda/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/generation/utils.py", line 1515, in generate
    return self.greedy_search(
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/generation/utils.py", line 2332, in greedy_search
    outputs = self(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 938, in forward
    outputs = self.model.decoder(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 651, in forward
    inputs_embeds = self.project_in(inputs_embeds)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 5071) of binary: /opt/miniconda/bin/python
Traceback (most recent call last):
  File "/opt/miniconda/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==1.13.1+cu117', 'console_scripts', 'torchrun')())
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
    return f(*args, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/run.py", line 762, in main
    run(args)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/run.py", line 753, in run
    elastic_launch(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 246, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
/home/opencompass/opencompass/tasks/openicl_infer.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2023-08-26_21:40:20
  host      : e9106e301747
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 5071)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
08/26 21:40:21 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"
08/26 21:40:21 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer
/opt/miniconda/lib/python3.10/site-packages/torch/cuda/__init__.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
  return torch._C._cuda_getDeviceCount() > 0
08/26 21:40:29 - OpenCompass - INFO - Task [opt350m/siqa_2]
/opt/miniconda/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments
  warnings.warn(
'(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 0ce01cbb-c9f4-4644-b995-baad3e105cf6)')' thrown while requesting HEAD https://huggingface.co/facebook/opt-350m/resolve/main/generation_config.json
/opt/miniconda/lib/python3.10/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')
Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/social_i_qa/674d85e42ac7430d3dcd4de7007feaffcb1527c535121e09bab2803fbcc925f8 (last modified on Sat Aug 26 21:21:43 2023) since it couldn't be found locally at social_i_qa., or remotely on the Hugging Face Hub.
08/26 21:40:50 - OpenCompass - INFO - Start inferencing [opt350m/siqa_2]
[2023-08-26 21:40:50,391] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...
  0%|                                                                                | 0/2 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 147, in <module>
    inferencer.run()
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 76, in run
    self._inference()
  File "/home/opencompass/opencompass/tasks/openicl_infer.py", line 119, in _inference
    inferencer.inference(retriever,
  File "/home/opencompass/opencompass/openicl/icl_inferencer/icl_gen_inferencer.py", line 122, in inference
    results = self.model.generate_from_template(
  File "/home/opencompass/opencompass/models/base.py", line 117, in generate_from_template
    return self.generate(inputs, max_out_len=max_out_len, **kwargs)
  File "/home/opencompass/opencompass/models/huggingface.py", line 145, in generate
    return sum((self._single_generate(
  File "/home/opencompass/opencompass/models/huggingface.py", line 145, in <genexpr>
    return sum((self._single_generate(
  File "/home/opencompass/opencompass/models/huggingface.py", line 213, in _single_generate
    outputs = self.model.generate(input_ids=input_ids,
  File "/opt/miniconda/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/generation/utils.py", line 1515, in generate
    return self.greedy_search(
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/generation/utils.py", line 2332, in greedy_search
    outputs = self(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 938, in forward
    outputs = self.model.decoder(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 651, in forward
    inputs_embeds = self.project_in(inputs_embeds)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 5426) of binary: /opt/miniconda/bin/python
Traceback (most recent call last):
  File "/opt/miniconda/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==1.13.1+cu117', 'console_scripts', 'torchrun')())
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
    return f(*args, **kwargs)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/run.py", line 762, in main
    run(args)
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/run.py", line 753, in run
    elastic_launch(
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/opt/miniconda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 246, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
/home/opencompass/opencompass/tasks/openicl_infer.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2023-08-26_21:40:51
  host      : e9106e301747
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 5426)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
08/26 21:40:51 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"
08/26 21:40:51 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer
/opt/miniconda/lib/python3.10/site-packages/torch/cuda/__init__.py:88: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
  return torch._C._cuda_getDeviceCount() > 0
08/26 21:40:59 - OpenCompass - INFO - Task [opt350m/siqa_3]
/opt/miniconda/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments
  warnings.warn(
'(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: d766e7b0-d587-41ff-aef5-8d96f0936e0c)')' thrown while requesting HEAD https://huggingface.co/facebook/opt-350m/resolve/main/tokenizer_config.json

其他信息
pls

/opt/miniconda3/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments

warnings.warn(

08/26 15:44:42 - OpenCompass - INFO - Partitioned into 40 tasks.

08/26 15:44:42 - OpenCompass - DEBUG - Task 0: [opt350m/siqa_0]

08/26 15:44:42 - OpenCompass - DEBUG - Task 1: [opt350m/siqa_1]

08/26 15:44:42 - OpenCompass - DEBUG - Task 2: [opt350m/siqa_2]

08/26 15:44:42 - OpenCompass - DEBUG - Task 3: [opt350m/siqa_3]

08/26 15:44:42 - OpenCompass - DEBUG - Task 4: [opt350m/siqa_4]

08/26 15:44:42 - OpenCompass - DEBUG - Task 5: [opt350m/siqa_5]

08/26 15:44:42 - OpenCompass - DEBUG - Task 6: [opt350m/siqa_6]

08/26 15:44:42 - OpenCompass - DEBUG - Task 7: [opt350m/siqa_7]

08/26 15:44:42 - OpenCompass - DEBUG - Task 8: [opt350m/siqa_8]

08/26 15:44:42 - OpenCompass - DEBUG - Task 9: [opt350m/siqa_9]

08/26 15:44:42 - OpenCompass - DEBUG - Task 10: [opt350m/siqa_10]

08/26 15:44:42 - OpenCompass - DEBUG - Task 11: [opt350m/siqa_11]

08/26 15:44:42 - OpenCompass - DEBUG - Task 12: [opt350m/siqa_12]

08/26 15:44:42 - OpenCompass - DEBUG - Task 13: [opt350m/siqa_13]

08/26 15:44:42 - OpenCompass - DEBUG - Task 14: [opt350m/siqa_14]

08/26 15:44:42 - OpenCompass - DEBUG - Task 15: [opt350m/siqa_15]

08/26 15:44:42 - OpenCompass - DEBUG - Task 16: [opt350m/siqa_16]

08/26 15:44:42 - OpenCompass - DEBUG - Task 17: [opt350m/siqa_17]

08/26 15:44:42 - OpenCompass - DEBUG - Task 18: [opt350m/siqa_18]

08/26 15:44:42 - OpenCompass - DEBUG - Task 19: [opt350m/siqa_19]

08/26 15:44:42 - OpenCompass - DEBUG - Task 20: [opt125m/siqa_0]

08/26 15:44:42 - OpenCompass - DEBUG - Task 21: [opt125m/siqa_1]

08/26 15:44:42 - OpenCompass - DEBUG - Task 22: [opt125m/siqa_2]

08/26 15:44:42 - OpenCompass - DEBUG - Task 23: [opt125m/siqa_3]

08/26 15:44:42 - OpenCompass - DEBUG - Task 24: [opt125m/siqa_4]

08/26 15:44:42 - OpenCompass - DEBUG - Task 25: [opt125m/siqa_5]

08/26 15:44:42 - OpenCompass - DEBUG - Task 26: [opt125m/siqa_6]

08/26 15:44:42 - OpenCompass - DEBUG - Task 27: [opt125m/siqa_7]

08/26 15:44:42 - OpenCompass - DEBUG - Task 28: [opt125m/siqa_8]

08/26 15:44:42 - OpenCompass - DEBUG - Task 29: [opt125m/siqa_9]

08/26 15:44:42 - OpenCompass - DEBUG - Task 30: [opt125m/siqa_10]

08/26 15:44:42 - OpenCompass - DEBUG - Task 31: [opt125m/siqa_11]

08/26 15:44:42 - OpenCompass - DEBUG - Task 32: [opt125m/siqa_12]

08/26 15:44:42 - OpenCompass - DEBUG - Task 33: [opt125m/siqa_13]

08/26 15:44:42 - OpenCompass - DEBUG - Task 34: [opt125m/siqa_14]

08/26 15:44:42 - OpenCompass - DEBUG - Task 35: [opt125m/siqa_15]

08/26 15:44:42 - OpenCompass - DEBUG - Task 36: [opt125m/siqa_16]

08/26 15:44:42 - OpenCompass - DEBUG - Task 37: [opt125m/siqa_17]

08/26 15:44:42 - OpenCompass - DEBUG - Task 38: [opt125m/siqa_18]

08/26 15:44:42 - OpenCompass - DEBUG - Task 39: [opt125m/siqa_19]

08/26 15:44:43 - OpenCompass - DEBUG - Modules of opencompass's task registry have been automatically imported from opencompass.tasks

08/26 15:44:43 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

/opt/miniconda3/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments

warnings.warn(

08/26 15:44:43 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:44 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:44 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:45 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:45 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:46 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:46 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:47 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:47 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:49 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:49 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:50 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:50 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:51 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:51 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:52 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:52 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:53 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:53 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:54 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:54 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:56 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:56 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:57 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:57 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:58 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:58 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:44:59 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:44:59 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:00 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:00 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:01 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:01 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:03 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:03 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:04 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:04 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:05 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:05 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:06 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:06 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:07 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:07 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:08 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:08 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:09 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:09 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:10 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:10 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:12 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:12 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:13 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:13 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:14 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:14 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:15 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:15 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:16 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:16 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:17 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:17 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:18 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:18 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:20 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:20 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:21 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:21 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:22 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:22 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:23 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:23 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:24 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:24 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:25 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:25 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:26 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:26 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:28 - OpenCompass - DEBUG - Get class `OpenICLInferTask` from "task" registry in "opencompass"

08/26 15:45:28 - OpenCompass - DEBUG - An `OpenICLInferTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_infer

/bin/sh: torchrun: command not found

08/26 15:45:29 - OpenCompass - INFO - Partitioned into 2 tasks.

08/26 15:45:29 - OpenCompass - DEBUG - Task 0: [opt350m/siqa]

08/26 15:45:29 - OpenCompass - DEBUG - Task 1: [opt125m/siqa]

08/26 15:45:29 - OpenCompass - DEBUG - Get class `OpenICLEvalTask` from "task" registry in "opencompass"

08/26 15:45:29 - OpenCompass - DEBUG - An `OpenICLEvalTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_eval

/opt/miniconda3/lib/python3.10/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning

warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')

08/26 15:45:32 - OpenCompass - DEBUG - Modules of opencompass's load_dataset registry have been automatically imported from opencompass.datasets

08/26 15:45:32 - OpenCompass - DEBUG - Get class `siqaDataset_V2` from "load_dataset" registry in "opencompass"

Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/social_i_qa/674d85e42ac7430d3dcd4de7007feaffcb1527c535121e09bab2803fbcc925f8 (last modified on Sat Aug 26 08:30:37 2023) since it couldn't be found locally at social_i_qa., or remotely on the Hugging Face Hub.

08/26 15:47:15 - OpenCompass - DEBUG - An `siqaDataset_V2` instance is built from registry, and its implementation can be found in opencompass.datasets.siqa

08/26 15:47:15 - OpenCompass - ERROR - /home/opencompass/opencompass/tasks/openicl_eval.py - _score - 163 - Task [opt350m/siqa]: No predictions found.

08/26 15:47:15 - OpenCompass - DEBUG - Get class `OpenICLEvalTask` from "task" registry in "opencompass"

/opt/miniconda3/lib/python3.10/site-packages/mmengine/utils/manager.py:113: UserWarning: <class 'mmengine.logging.logger.MMLogger'> instance named of OpenCompass has been created, the method `get_instance` should not accept any other arguments

warnings.warn(

08/26 15:47:15 - OpenCompass - DEBUG - An `OpenICLEvalTask` instance is built from registry, and its implementation can be found in opencompass.tasks.openicl_eval

08/26 15:47:17 - OpenCompass - DEBUG - Get class `siqaDataset_V2` from "load_dataset" registry in "opencompass"

Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/social_i_qa/674d85e42ac7430d3dcd4de7007feaffcb1527c535121e09bab2803fbcc925f8 (last modified on Sat Aug 26 08:30:37 2023) since it couldn't be found locally at social_i_qa., or remotely on the Hugging Face Hub.

08/26 15:47:17 - OpenCompass - DEBUG - An `siqaDataset_V2` instance is built from registry, and its implementation can be found in opencompass.datasets.siqa

08/26 15:47:17 - OpenCompass - ERROR - /home/opencompass/opencompass/tasks/openicl_eval.py - _score - 163 - Task [opt125m/siqa]: No predictions found.

dataset version metric mode opt350m opt125m

--------- --------- -------- ------ --------- ---------

siqa - - - - -

08/26 15:47:17 - OpenCompass - INFO - write summary to /home/jovyan/taoheng/data/opencompass/outputs/demo/20230826_154442/summary/summary_20230826_154442.txt
![image](https://github.com/InternLM/opencompass/assets/6941820/64782fb7-a127-4400-a15d-790cda0ede28)

08/26 15:47:17 - OpenCompass - INFO - write csv to /home/jovyan/taoheng/data/opencompass/outputs/demo/20230826_154442/summary/summary_20230826_154442.csv
![image](https://github.com/InternLM/opencompass/assets/6941820/a1df232e-1a7c-4f86-b26d-52d5d4ab8bb2)

换了一台V100跑，运行无异常，没有输出

Does
import torch
print(torch.cuda.is_available())

returns True? Similar issues are often caused by unsuccessful pytorch-cuda integration.

Feel free to re-open if needed.

pip uninstall torch
pip3 install torch torchvision torchaudio


Issues with Categorical Data

So I'm working on a survey data where I am trying to figure out cause and effect relationship between a person's responses to the survey questions and his/her final preference towards that product. The data is categorical entirely. On using Causal Inference 360's evaluation plots, I got the following error:
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric precision could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric recall could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric f1 could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric roc_auc could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: multi_class must be in ('ovo', 'ovr')
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric avg_precision could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: multiclass format is not supported
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric hinge could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: The shape of pred_decision cannot be 1d arraywith a multiclass target. pred_decision shape must be (n_samples, n_classes), that is (1977, 3). Got: (1977,)
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric brier could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: Only binary classification is supported. The type of the target is multiclass.
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric roc_curve could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: multiclass format is not supported
warnings.warn(str(v))
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:106: UserWarning: metric pr_curve could not be evaluated
warnings.warn(f"metric {metric_name} could not be evaluated")
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/metrics.py:107: UserWarning: multiclass format is not supported
warnings.warn(str(v))
KeyError                                  Traceback (most recent call last)
 in
3
4 eval_results = evaluate(ipw, X, a, y)
----> 5 eval_results.plot_all()
6 eval_results.plot_covariate_balance(kind="love");
8 frames
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/plots/mixins.py in plot_all(self, phase)
343         """
344         phases_to_plot = self.predictions.keys() if phase is None else [phase]
--> 345         multipanel_plot = {
346             plotted_phase: self._make_multipanel_evaluation_plot(
347                 plot_names=self.all_plot_names, phase=plotted_phase
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/plots/mixins.py in (.0)
344         phases_to_plot = self.predictions.keys() if phase is None else [phase]
345         multipanel_plot = {
--> 346             plotted_phase: self._make_multipanel_evaluation_plot(
347                 plot_names=self.all_plot_names, phase=plotted_phase
348             )
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/plots/mixins.py in _make_multipanel_evaluation_plot(self, plot_names, phase)
353     def _make_multipanel_evaluation_plot(self, plot_names, phase):
354         phase_fig, phase_axes = plots.get_subplots(len(plot_names))
--> 355         named_axes = {
356             name: self._make_single_panel_evaluation_plot(name, phase, ax)
357             for name, ax in zip(plot_names, phase_axes.ravel())
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/plots/mixins.py in (.0)
354         phase_fig, phase_axes = plots.get_subplots(len(plot_names))
355         named_axes = {
--> 356             name: self._make_single_panel_evaluation_plot(name, phase, ax)
357             for name, ax in zip(plot_names, phase_axes.ravel())
358         }
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/plots/mixins.py in _make_single_panel_evaluation_plot(self, plot_name, phase, ax, **kwargs)
379         plot_func = plots.lookup_name(plot_name)
380         plot_data = self.get_data_for_plot(plot_name, phase=phase)
--> 381         return plot_func(*plot_data, ax=ax, **kwargs)
/usr/local/lib/python3.8/dist-packages/causallib/evaluation/plots/plots.py in plot_mean_features_imbalance_love_folds(table1_folds, cv, aggregate_folds, thresh, plot_semi_grid, ax)
813     aggregated_table1 = aggregated_table1.groupby(aggregated_table1.index)
814
--> 815     order = aggregated_table1.mean().sort_values(by="unweighted", ascending=True).index
816
817     if aggregate_folds:
/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
309                     stacklevel=stacklevel,
310                 )
--> 311             return func(*args, **kwargs)
312
313         return wrapper
/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py in sort_values(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)
6257
6258             by = by[0]
-> 6259             k = self._get_label_or_level_values(by, axis=axis)
6260
6261             # need to rewrap column in Series to apply key function
/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py in _get_label_or_level_values(self, key, axis)
1777             values = self.axes[axis].get_level_values(key)._values
1778         else:
-> 1779             raise KeyError(key)
1780
1781         # Check for duplicates
KeyError: 'unweighted'
Can you help me out please?

Hi, I'm sorry you encountered a problem, and thank you for bringing that up.
It's hard to say without the input data, but on the face of it, it seems you have a multiple-treatment setting that is not well-defined for most evaluations which require a binary treatment.
For example, the Love plot take a covariate x compute a the difference of means between two groups: x|a=1 and x|a=0. However, if you have more than two groups, then this difference is no longer well-defined.
(Same argument goes for the other evaluations. For example, ROC-curves are not well-defined for non-binary target, etc.)
Let me know if that isn't the case. And if so, if you could please share a small synthetic data sample (or snippet) that reproduces the problem, it would make it easier.

Thank you @ehudkr .. will try for binary targets once and will see how it goes. Will post it here

It worked.. changed the target parameter to binary instead of 3 classes.. thanks a lot @ehudkr ..
But kindly work out something for multiple class problems as well


Initial Dockerfile contribution

Hi,
we created a Dockerfile to build a container for deployment on our build cluster. Maybe this Dockerfile could be a template for an automatic deployment to a container registry. Would this be an option for the project?
Thanks & Bye,
Chris

i'm happy with your changes. I also try to find some time add some description to the README.md

I revised this and will accept the PR as is, it is better than not having anything at all, and can be a starting point for #277. Unfortunately it has a conflict, so I cannot merge it as is. Could you resolve the conflict @sa-ChristianNiessner ? We can add info in the readme in a separate PR if you do not have the resources or the motivation to do so right now ;)

Since we now merged #342, I will close this PR. If you still want, you could rebase and tweak the Dockerfile anytime, it would be appreciated.


NTR: decreased protein localization to cell cortex during meiotic prophase I

child of
FYPO:0000928 | abnormal protein localization to cell cortex
decreased protein localization to cell cortex during meiotic prophase I
synonym decreased protein localization to cell cortex during in zygote
ref
PMID:28292899

abnormal protein localization to cell cortex FYPO:0006867 (FYPO:0000928 is 'vegetative', now explicit in its name)
decreased protein localization to cell cortex during meiotic prophase I FYPO:0006868
synonym is related because 'zygote' is broader than 'prophase I'
edit file: 01395f5f57543ab4fa25ab90630158a963daf505
release: 6a27a43c29768356dcf4af581cabcd42c7b17158

I dont see this under
abnormal protein localization to cell cortex FYPO:0006867?

I do (which makes sense, as the link is asserted).


again bots go to inaccessible and invisible waypoint

Hi @jeefo I noticed that since the last edits it went to the unreachable node in the map. This issue was resolved before (#432) but then this issue reappeared, I don't know where this issue originates from.

value of yb_path_astar_post_smooth?

value of yb_path_astar_post_smooth?

The yb_path_astar_post_smooth value was on. After closing it, the problem was solved. Sorry for opening a useless thread.

value of yb_path_astar_post_smooth?

I set the value of yb_path_astar_post_smooth to both 0 and 1, the result is the same unfortunately @jeefo

There's no need. I think I solved the problem, thanks anyway


Move checkout store to core library

The checkout store is wired into the component library. As part of GH-250, we move ngrx store outside the component library and only interact with the facades.

Can start this one when facades refactoring is done

Checkout store is moved to the core correctly.


add database.json file on config folder

In CloudBread-Admin-Web project v2.5,
database.json is used by Sequelize ORM - https://github.com/sequelize/sequelize
Adding database.json file with MSSQL - Azure SQL Database empty connection properties.

sequelize 로 database.json 파일을 만든 후 어디에다 넣어야 하는거죠?


For #213 Creating volumes with parameters

For #213:

creating volumes with parameters like driver, driver options and labels
also returning proper Volume object on create.

Pull Request Test Coverage Report for Build 464

15 of 16 (93.75%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.03%) to 89.314%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

src/main/java/com/amihaiemil/docker/RtVolumes.java
15
16
93.75%

Totals

Change from base Build 461:
-0.03%

Covered Lines:
677

Relevant Lines:
758

💛  - Coveralls

Job #262 is now in scope, role is REV

This pull request #262 is assigned to @bkuzmic/z, here is why; the budget is 15 minutes, see §4; please, read §27 and when you decide to accept the changes, inform @amihaiemil/z (the architect) right in this ticket; if you decide that this PR should not be accepted ever, also inform the architect; this blog post will help you understand what is expected from a code reviewer; there will be a monetary reward for this job

@amihaiemil Is this a 0crat bug? I am asked to do the review of my own PR :)

@bkuzmic seems like it, yes. Can you submit it to zerocrat/farm? You should get paid if you are a member there :)

@rultor merge it.

@rultor merge it.

@bkuzmic Thanks for your request. @amihaiemil Please confirm this.

@rultor good to merge

@rultor good to merge

@amihaiemil OK, I'll try to merge now. You can check the progress of the merge here

@rultor good to merge

@amihaiemil Done! FYI, the full log is here (took me 2min)

Job was finished in 1 hours, bonus for fast delivery is possible (see §36)

The project doesn't have enough funds, can't make a payment

The job #262 is now out of scope

Order was finished: +20 point(s) just awarded to @bkuzmic/z

The project doesn't have enough funds, can't make a payment

Payment to ARC for a closed pull request, as in §28: +10 point(s) just awarded to @amihaiemil/z


[Update Bug]: Palworld Proton

Panel Version
1.0.0-beta13
Wings Version
latest
Service
update process
Modified
No, I did not modify the egg
Expected Behavior
that the game was updated.
Actual Behavior
Auto update is active at startup. Today when I restarted it to update it, the following error appeared and the game is still in the same version:
[pelican Daemon]: Updating process configuration files...
[pelican Daemon]: Ensuring file permissions are set correctly, this could take a few seconds...
container@panel~ Server marked as starting...
[pelican Daemon]: Pulling Docker container image, this could take a few minutes to complete...
Pulling from parkervcp/steamcmd
Status: Image is up to date for ghcr.io/parkervcp/steamcmd:proton
Digest: sha256:hiden
[pelican Daemon]: Finished pulling Docker container image
steam user is not set.
Using anonymous user.
WARNING: setlocale('en_US.UTF-8') failed, using locale: 'C'. International characters may not work.
Redirecting stderr to '/home/container/.steam/steam/logs/stderr.txt'
Logging directory: '/home/container/.steam/steam/logs'
[  0%] Checking for available updates...
[----] Verifying installation...
UpdateUI: skip show logoSteam Console Client (c) Valve Corporation - version<PHONE_NUMBER>
-- type 'quit' to exit --
Loading Steam API...OK
Connecting anonymously to Steam Public...OK
Waiting for client config...OK
Waiting for user info...OK
"@sSteamCmdForcePlatformType" = "windows"
Success! App '1007' already up to date.
 Update state (0x3) reconfiguring, progress: 0.00 (0 / 0)
 Update state (0x3) reconfiguring, progress: 0.00 (0 / 0)
IPC function call IClientAppManager::GetUpdateInfo took too long: 47 msec
Error! App '2394010' state is 0x6 after update job.
:/home/container$ ./PalworldServerConfigParser; (while read cmd; do rcon -s -a "localhost:$RCON_PORT" -p "$ADMIN_PASSWORD" "$cmd";done) < /dev/stdin & proton run /home/container/Pal/Binaries/Win64/PalServer-Win64-Shipping.exe -publiclobby -useperfthreads -NoAsyncLoadingThread -UseMultithreadForDS -port=28020 -publicport=28020 -servername="[ESP]Nymeriaark.es 001 PVE x2 exp/x3 farm/x1 Hatch" -players=32 $(if [ -n "$SERVER_PASSWORD" ]; then echo "-serverpassword=\"\""; fi) -adminpassword="hiden" -rcon
Program Version: v1.0.16

Steps To Reproduce
install?
Install logs
dont work

the error 0x6 (LoggedInElsewhere) It is supposed to be "same user logged in elsewhere" the thing is that it uses anonymous login. The egg does work to install a new server with the latest version but it is impossible to update a server already installed.
the same error appears in the console all the time:
steam user is not set.
Using anonymous user.
WARNING: setlocale('en_US.UTF-8') failed, using locale: 'C'. International characters may not work.
Redirecting stderr to '/home/container/.steam/steam/logs/stderr.txt'
Logging directory: '/home/container/.steam/steam/logs'
[  0%] Checking for available updates...
[----] Verifying installation...
UpdateUI: skip show logoSteam Console Client (c) Valve Corporation - version<PHONE_NUMBER>
-- type 'quit' to exit --
Loading Steam API...OK
Connecting anonymously to Steam Public...OK
Waiting for client config...OK
Waiting for user info...OK
"@sSteamCmdForcePlatformType" = "windows"
Success! App '1007' already up to date.
 Update state (0x3) reconfiguring, progress: 0.00 (0 / 0)
IPC function call IClientAppManager::GetUpdateInfo took too long: 45 msec
 Update state (0x3) reconfiguring, progress: 0.00 (0 / 0)
Error! App '2394010' state is 0x6 after update job.


Scale to 0 if <0

Prevents possible ValueError exceptions

Ah, yes. Great fix. Should have done that in the first place. Thanks!

This change seems to have vanished from develop and master. Was this intended and there was an issues with this?

Odd, it appears git has somehow reverted it... I'll re-add it :)

While 3.9.3 contains this fix now, it's gone again in master and develop.
Looks like some accidental force push got rid of the commit or something like that?

I haven't done a force push so I'm guessing something went wrong when merging (I generally do rebase merges) but I'm not entirely sure what... I've merged everything back so it's now included again including a few extra tests to make sure it won't break again :)
Thanks for all the help @TimoRoth


Solved the import error

The library is being installed as "annotated_text", not "st_annotated_text", therefore example provided in readme file showcasing how to use the module is not working, therefore I have updated it.

Thanks for the fix!!! And apologies for the long delay in reviewing :wink:


Enhance Extensibility Documentation

This PR contains daily documentation updates based on the following suggestion:
Improve documentation on how to extend and customize the Sublayer framework with custom actions, agents, and generators.
description of file changes: File: docs/concepts/index.md

Add detailed guides or links to existing guides on creating custom actions, agents, and generators.

File: docs/custom_components/index.md

Update to include more examples and detailed steps on extending the framework.

here it almost sounds like a good suggestion, but then you realize "custom generators/actions/agents" is redundant. the point of sublayer's 3 core concepts is to only make custom ones haha.
silly change 1/3


Use storybook syntax-highlighter

This makes it so the code is theme-able from storybook 🎉
But it does change the default visual look of the addon quite a bit.
It's the same as addon-source.. WDYT @stof ?

Published PR with canary version: 7.1.12-canary.92.253

@ndelangen this makes perfect sense to me.
Note that I'm not using addon-jsx myself (I'm using @storybook/html so I don't have any JSX at all). I contributed a PR on this repo mainly to get rid of the weekly github vulnerability report that I started receiving after being invited in the storybookjs organization.

@hipstersmoothie shall we do a minor release?

:rocket: PR was released in v7.1.12 :rocket:


Add pushsafer.com notification service

Please note: GitHub will only accept pull requests to existing services that implement bug fixes or security improvements. We no longer accept feature changes to existing services.

As you've noted in your PR, we do not accept new services or feature changes to existing services. As such, I'm closing this PR. Please use webhooks: https://developer.github.com

Hi,  how i can realize user inputs in webhooks
Kevin


Clarify License

Based on initial conversations, this code was to be made available under a permissive open source license. Can the proper file be added to the repository, so this is clear?

We added this a while ago (to our private repo, before we made it public)
Not sure where it got lost
Will add it back

https://github.com/Twilio-org/phonebank/pull/184


WriteTo.MSSqlServer to .net Core 2.1 not working

I am having problem while trying to set the configurations via C # by .WriteTo.MSSqlServer does not work!
Just when I do .ReadFrom.Configuration(Configuration)  works.
ASP.NET MVC Core 2.1 Project

            var logDB = settings.ConnectionString.AuditoriaConnection;
            var logTable = "LogError";
            var options = new ColumnOptions();
            options.Store.Add(StandardColumn.LogEvent);
            options.LogEvent.DataLength = 2048;
            options.PrimaryKey = options.TimeStamp;
            options.TimeStamp.NonClusteredIndex = true;

            Log.Logger = new LoggerConfiguration()
                .WriteTo.MSSqlServer(
                    connectionString: logDB,
                    tableName: logTable,
                    columnOptions: options,
                    appConfiguration: Configuration)
                .CreateLogger();

also tried without the appConfiguration: Configuration),
direct:
.WriteTo.MSSqlServer (logDB, logTable)
My appsetting.json
"Serilog": {
    "MinimumLevel": "Information",
    "WriteTo": [
      {
        "Name": "MSSqlServer",
        "Args": {
          "connectionString": "Data Source=xxx.yyy.zzz",
          "tableName": "LogError",
          "autoCreateSqlTable": true,
          "restrictedToMinimumLevel": "Information ", //https://github.com/serilog/serilog/wiki/Writing-Log-Events
          "columnOptionsSection": {
            "customColumns": [
              {
                "ColumnName": "SetorErro",
                "DataType": "int",
                "AllowNull": true
              },
              {
                "ColumnName": "LocalErro",
                "DataType": "nvarchar",
                "DataLength": 150,
                "AllowNull": true
              }
            ]
          }
        }
      }
    ]
  }

symptom:
it does not create the table and consequently does not write any log
only works via app setting.json if trying via code does not work if it makes hybrid worse yet.
I have read all the documentation, including doing exactly as recommended and nothing
https://github.com/serilog/serilog-sinks-mssqlserver#code-only-any-net-target

I have some issue - any help please?
Thanks!

I've solved the issue by upgrading it to dev version
https://www.nuget.org/packages/Serilog.Sinks.MSSqlServer/5.1.3-dev-00232

Try to add restrictedToMinimumLevel to .MSSqlServer()

I have a similar issue - I don't see the sinks created in the logger - you can see the content from the appsetting.json  when I dig into it with the configuration object.  Additionally the documentation references using the appsetting.json but the example provides the connection string  see this : var log = new LoggerConfiguration()
.WriteTo.MSSqlServer(
connectionString: logDB,
tableName: logTable,
columnOptions: opts,
appConfiguration: appSettings
).CreateLogger();
.CreateLogger();
Here's what I did to verify the appsetting.Json has the content -
IConfigurationSection wt = _configuration.GetSection("Serilog:WriteTo");
var ArgsArray = wt.AsEnumerable();
Here's the logger & args.... why doesn't serilog load ?

I have a similar issue.  It has something to do with security I can access the DB and do queries.  I see setting the AccessToken in the config setting.
var adoConn   = "Server=tcp:sql-zzzzznonprod.database.windows.net,1433;Initial Catalog=AzureHRODS-NonProd;Persist Security Info=False;MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;";
Question:
How can Serialog set the AccessToken that it needs to open the connection.
connection.AccessToken = GetAccessToken("a0zzz2fd-zzzz-409e-91a8-bb7zzzzda0");
connection.Open();

Hi @mastronardif Azure user managed identities (which is what you need) is not supported by the sink but there is an open feature request (#302) for that and a PR is always welcome. :)

Since this report is quite old and we have rewritten a lot of the code in the meantime, I encourage everyone to update to the newest version of the sink. Perhaps the problem is gone, because there were no reports of this for a long time. If the problem reappears with the latest version, pleas open a new but.
I'm closing this issue.


Support page-specific language

Preserves the existing behaviour for those who do not set, but allows it to be overriden at the page level.
This is similar to how jekyll-seo-tag works.

Correct me if I'm wrong, but seems the broken build has nothing to do with the changes in this PR.


[DOCS] Fix URLs in weak supervision with sentence transformers tutorial

Which page or section is this issue related to?

In a couple different places (linked below), it references an earlier/previous weak supervision tutorial but the link leads you to the same page.

https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-sentencetransformers-weaksupervision.html#:~:text=Weak supervision with Argilla tutorial
https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-sentencetransformers-weaksupervision.html#appendix:-visualize-changes:~:text=as in the-,previous tutorial,-%2C but on the
https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-sentencetransformers-weaksupervision.html#appendix:-visualize-changes:~:text=model from the-,previous tutorial,-. Remember we achieved

Under the "Detailed Workflow" section, the following hyperlink does not exist at all.

https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-sentencetransformers-weaksupervision.html#:~:text=weak supervision guide

References to the Appendix (which is at the bottom of the page) in step 5 do not work because the URLs don't match the capitalization for the header permalinks.

Great work Garima, I assigned the issues to you:)


sync to live

Docs Build status updates of commit f12788d:
:x: Validation status: errors
Please follow instructions here which may help to resolve issue.

File
Status
Preview URL
Details

windows-iotcore/index.yml
:x:Error

Details

windows-iotcore/index.yml

[Error] Unable to load file 'index.yml' via processor 'ManagedReferenceDocumentProcessor': (Line: 5, Col: 1, Idx: 242) - (Line: 5, Col: 1, Idx: 242): While scanning a simple key, could not find expected ':'.
[Warning] Please add `YamlMime` as the first line of file, e.g.: `### YamlMime:ManagedReference`, otherwise the file will be not treated as ManagedReference source file in near future.

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


Consider using SubtlexCN data

SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles

Arguably better quality for teaching than Junda written corpora.
See also #52

I love this idea. I'm not sure this repo is under active maintenance atm, though. I'd be open to contributing and maybe helping with this integration, or putting together another npm package.
@hugolpz any interest?


A second single splat for def parameters

Defining a variadic #[] is easy:
class Foo(T)
  def [](*x : Int) : T
    # ...
  end
end

Defining a variadic #[]= is hard and ugly: (we presume that redefining the signature and normalization form for #[]= is too big a breaking change to make)
class Foo(T)
  # *value* is a required named parameter
  def []=(*indices : Int, value : T) : T
    value
  end
end

x = Foo(String).new
x[1, 2, 3] = "foo"        # Error: missing argument: value
x[1, 2, value: "foo"] = 3 # works, but not what we want to write at all

class Foo(T)
  # i don't remember why `Us` can be inferred here, but it works
  def []=(*indices_and_value : *{*Us, T}) : T forall Us
    {% for U in Us %}
      {% raise "..." unless U <= Int %}
    {% end %}
    *indices, value = indices_and_value
    value
  end
end

x = Foo(String).new
x[1, 2, 3] = "foo" # okay

class Foo(T)
  def []=(indices : Tuple, value : T) : T
    check_indices(*indices)
    value
  end

  private def check_indices(*args : Int); end
end

x = Foo(String).new
x[{1, 2, 3}] = "foo" # okay, note the extra curly braces

This comes from Crystal's inability to specify positional parameters after the single splat. Ruby doesn't have this issue because its named parameters use a completely different syntax:
class Foo
  # okay, *value* is a positional parameter
  def []=(*indices, value)
    value
  end

  # here *value* is a required named parameter
  def []=(*indices, value:)
    value
  end
end

So I propose that we allow a second single splat parameter in the list of def parameters. If there are two single splats, the first one must be named, the second one must be unnamed, and all parameters between the first and second (which must be non-empty) are positional:
class Foo(T)
  # okay, trailing bare * is allowed if it is a second one
  def []=(*indices : Int, value : T, *) : T
    value
  end

  def bad(*args, *); end # Error: expected parameter between first and second *
  def bad(*, *); end     # Error: first * cannot be bare
  def bad(*x, *y); end   # Error: second * must be bare
end

x = Foo(String).new
x[1, 2, 3] = "foo" # okay

Positional arguments are matched in a similar manner as multi-assigns or block arguments. Positional parameters between the single splats must be passed as positional arguments, never named:
def foo(a, b, *c, d, e, *, f, g, **h)
  [a, b, c, d, e, f, g, h]
end

foo(1, 2, 3, 4, 5, 6, f: 7, g: 8, h: 9, i: 10) # => [1, 2, {3, 4}, 5, 6, 7, 8, {h: 9, i: 10}]
foo(1, 2, 3, 4, f: 5, g: 6)                    # => [1, 2, {}, 3, 4, 5, 6, {}]
foo(1, 2, 3, 4, g: 5, f: 6, e: 7)              # Error: parameter 'e' cannot be a named argument
foo(1, 2, 3, f: 4, g: 5)                       # Error: wrong number of arguments (expected 4+)

def bar(*args, x, *, **opts); end # okay, *x* is a positional parameter
def bar(*args, x, **opts); end    # *x* is a named parameter

Those defs can still be converted into Proc pointers because all argument types must be spelt out, even for existing defs, provided they have no required named parameters.
If we have #10711, then only the notion of corresponding parameters needs to be updated for the overload ordering algorithm.
The biggest breaking change, of course, is Crystal::Macros::Def will now have two splat indices, and macros that use #splat_index to infer named parameters will break no matter how Def's interface changes.

One important aspect of this second single splat is that we can now reconcile other forms of splat collection as well. Specifically, they implicitly insert a * before the named entities if only a named single splat is found, and all entities after the bare * must be named. This opens the doors for supporting named entities on them:
# equivalent to `Foo(T, *U, V, *)`; no named generic type parameters follow
class Foo(T, *U, V); end

# hypothetical generic type with a named parameter `U`, instantiated like `Bar(Int32, u: String)`
# bare splat is optional here because `u:` is enough to mark the start of named entities
class Bar(T, *, u: U); end

# hypothetical generic type with a named parameter and a double splat
# an implicit `*` exists after `R`
struct MyProc(*T, R, call_convention: CC, **U)
  def call(*args : *T, **opts : **U) : R; end
end

# equivalent to `a, *b, c, * = ...`
a, *b, c = [1, 2, 3, 4, 5]

# hypothetical multi-assign form for named targets
*, x, y = {x: 100, y: 200}

# nothing can destructure to both positional and named assignment targets, but
# `yield` and `next` might?

module Foo(*T)
  # equivalent to `*T, Int32, * ->`
  def self.foo(& : *T, Int32 ->); end

  # hypothetical `ProcNotation` with named parameters
  # bare splat is optional here because `x:` is enough to mark the start of named entities
  def self.bar(& : *T, Int32, *, x: String ->)
    yield ..., x: "bar"
  end
end

Foo.foo { |*a, b| }       # equivalent to `|*a, b, *|`
Foo.bar { |*a, b, *, x| } # hypothetical form for named block parameters
Foo.bar { |*a, **v| }     # *v* collects all named yield arguments

This is not suggesting all those other forms of splat collection are feasible (e.g. the Proc type must be completely rewritten), but this is a good starting point syntactically.

I like this but I think it's confusing that the meaning of *splat changes depending on whether there's * after it or not. For example:
def foo(x, *y, z) # Here z is a named argument
def foo(x, *y, z, *, w) # Here z is a positional argument

But maybe it's not a big deal. I'm trying to think of some other syntax, but in any case that new syntax would mean that the current one changes meaning. I was thinking...
def foo(x, y, :, z) # ":" means named arguments start
def foo(x, *y, z, :, w) # it's a bit clearer that `*` is a splat and `:` begins the named arguments list
def foo(x, *y, z) # but now this would mean z is a positional argument, but right now that's not the case :(

A bare * indicating the start of a named arguments list threw me the first time I saw it, since it seemed to overload splatting with requiring keywords.  This conceptual overload I think is what's causing syntactical conflicts with this "infix splatting" feature that @HertzDevil wants to implement.
I like the : as an alternative indicator, but yeah, it's a breaking change, and has some things we'd have to work out.  If we're brainstorming breaking changes, what about using a trailing : on the keyword itself (similar to Ruby) to represent required named parameters instead of "everything after *"?
def foo(x, *y, z) # Here z is a positional argument, y is an infix splat (if there are only 2 incoming params, then y is nothing?)
def foo(x, *y, z:) # Here z is a required named argument, y is a postfix splat for "the rest"
def foo(x, *y, z, w:) # Here z is a positional argument, y is an infix splat, and w is a required named argument

I'm not sure if this conflicts with using : for Type restrictions (i.e. are y: and y : String in conflict or would the user have to do y: : String?)

I think it conflicts a bit with type restrictions, or it could also lead to confusion.
Another idea is to use a different syntax to say "I want more positional arguments after the splat." For example:
def foo(x, *y, .., z, w)

where .. means exactly that.
Regardless of the syntax, this would be something nice to have.

A little bit out of topic, but one thing I have seen some other languages doing is allowing the user to opt in for new syntax/semantic features file by file with a "language version". Dart does it with a comment on the first line. And Rust does it with the "edition" setting. The nice thing is that the files with different semantics can always be mixed together without issues. We could adopt something similar to change parser behavior without causing a breaking change. This is similar to the multi assign flag, but more structured.


Source plan environment variables after prepare and execute steps

Environment variables can now be written inside the TMT_PLAN_ENVIRONMENT_FILE. Variables defined in this file are sourced immediately after the prepare step, making them accessible in the tests and across all subsequent steps. Variables inside the TMT_PLAN_ENVIRONMENT_FILE have a lower priority than environment and environment-file fmf keys and can be overwritten by them.
Pull Request Checklist:

[x] implement the feature
[x] write the documentation
[x] extend the test coverage
[x] mention the version

Fixes: #2276

One question though:
Variables inside the `TMT_PLAN_ENVIRONMENT_FILE` have a lower
priority than `environment` and `environment-file` fmf keys
and can be overwritten by them.

Wouldn't it be better to switch the priority to allow overriding environment defined in the plan from the running process? I can imagine to put some default value in the plan config and then, based on a specific situation/condition, modify it during prepare or execute step. @happz, @lukaszachy, thoughts?

For reference, summary from the adhoc chat:

Revert the change to make plan environment stronger than TMT_PLAN_ENVIRONMENT_FILE
Explicitly document the priority in the variable definition to make it clear

Reasoning:

This is kind of consistent with the fact that plan environment is stronger than test environment
Using this approach we will avoid documenting weird corner case for the script field in plan
The other approach would require to somehow ignore certain fields in Plan._expand_node_data()

The change was reverted. The variables specified by the TMT_PLAN_ENVIRONMENT_FILE now have lower priority than variables specified by the environment key, environment-file key, or the command line. I also documented the priority and added some more tests.


Remove hardcoding of data-tools@ email address in en.js

Blocked by #104

Need to deliver the settings to the frontend, probably using a new API endpoint like in #32


Update for futures v0.3.0-alpha.15

Remove FutureObj usage and futures_api feature
Blocked on an http-service release with https://github.com/rustasync/http-service/pull/23

Description

Motivation and Context

How Has This Been Tested?

Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[x] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[ ] I have read the CONTRIBUTING document.
[ ] I have added tests to cover my changes.
[x] All new and existing tests passed.

@yoshuawuyts any chance of an http-service-mock v0.2.0 release? That's the only remaining blocker for this.

@Nemo157 published!

I've had to add a hack to the Cargo.toml to workaround a breaking change in ascii-canvas v1.0.1, there appears to already be an open issue about it so this can be removed once it gets yanked.


Add admin order management APIs

Pull Request
Title
Order Management APIs
Description
Admin has control on the order created by the users and access to the following endpoints.
The APIs have rigorous validation checkings and error loggings.
// Create a new order (Rental)
router.post('/rent-orders', adminCreateRental );
// Get all orders
router.get('/rent-orders', getAllOrders);
// Get a specific order by rentalId
router.get('/rent-orders/:rentalId', getSpecificOrder );
// Update an order (change rental status or quantity)
router.put('/rent-orders/:rentalId',updateOrder );
// Delete an order
router.delete('/rent-orders/:rentalId', deleteOrder )
// Bulk update orders (e.g., change multiple order statuses at once)
router.put('/rent-orders/bulk-update', bulkUpdateOrders);``
Related Issues
fixes #911
Checklist

[X] I have tested the changes locally
[X] Documentation has been updated (if necessary)
[X] Changes are backward-compatible

Screenshots (if applicable)

@manikumarreddyu These are crucial APIs, please increase the level of the PR!


Update README.md

in music_player.py all the dir to the music folder started with capital M? Is this a mistake since it starts w lower case?

How do you play the game? no matter what i entered it didnt do anyhting?

How do you play the game? no matter what i entered it didnt do anyhting?

You must tap the white box, and enter your name.

Is this running on your computer??

@Raulj123 any update?