TensorRT Segmentation fault

I'm trying to run the TensorRT version of the docker container according to instructions, but am getting a segfault whenever I attempt to transcribe any audio. The same audio works with the Faster whisper backend.
This happens for both live transcription and submission of file
System info: Debian 12 VM with a RTX 3090 passthrough to it. Driver version 545.23.06
Full log:
(base) whiskas@debian-gpu:/mnt/samsung/projects/WhisperLive$ docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it ghcr.io/collabora/whisperlive-tensorrt
root@c3f2d94d2f68:/app# bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small.en
Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com
Requirement already satisfied: tensorrt_llm==0.9.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (0.9.0)
Requirement already satisfied: tiktoken in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (0.7.0)
Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (3.0.0)
Requirement already satisfied: kaldialign in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (0.9.1)
Requirement already satisfied: openai-whisper in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (20231117)
Collecting librosa
  Downloading librosa-0.10.2.post1-py3-none-any.whl (260 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 260.1/260.1 KB 1.9 MB/s eta 0:00:00
Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (0.12.1)
Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (0.4.5)
Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 10)) (4.38.2)
Requirement already satisfied: janus in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 11)) (1.0.0)
Installing collected packages: librosa
Successfully installed librosa-0.10.2.post1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Downloading small.en...
--2024-09-17 07:04:29--  https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt
Resolving openaipublic.azureedge.net (openaipublic.azureedge.net)... <IP_ADDRESS>, 2620:1ec:bdf::67
Connecting to openaipublic.azureedge.net (openaipublic.azureedge.net)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 483615683 (461M) [application/octet-stream]
Saving to: 'assets/small.en.pt'

small.en.pt                                     100%[=====================================================================================================>] 461.21M  3.22MB/s    in 2m 53s

2024-09-17 07:07:24 (2.66 MB/s) - 'assets/small.en.pt' saved [483615683/483615683]

Download completed: small.en.pt
whisper_small_en
Running build script for small.en with output directory whisper_small_en
[TensorRT-LLM] TensorRT-LLM version: 0.9.0
[09/17/2024-07:07:26] [TRT-LLM] [I] plugin_arg is None, setting it as float16 automatically.
[09/17/2024-07:07:26] [TRT-LLM] [I] plugin_arg is None, setting it as float16 automatically.
[09/17/2024-07:07:26] [TRT-LLM] [I] plugin_arg is None, setting it as float16 automatically.
[09/17/2024-07:07:26] [TRT] [I] [MemUsageChange] Init CUDA: CPU +13, GPU +0, now: CPU 598, GPU 17025 (MiB)
[09/17/2024-07:07:28] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1799, GPU +312, now: CPU 2533, GPU 17337 (MiB)
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter dtype is None, using default dtype: DataType.FLOAT, it is recommended to always specify dtype explicitly
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter dtype is None, using default dtype: DataType.FLOAT, it is recommended to always specify dtype explicitly
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter dtype is None, using default dtype: DataType.FLOAT, it is recommended to always specify dtype explicitly
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter dtype is None, using default dtype: DataType.FLOAT, it is recommended to always specify dtype explicitly
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter dtype is None, using default dtype: DataType.FLOAT, it is recommended to always specify dtype explicitly
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter dtype is None, using default dtype: DataType.FLOAT, it is recommended to always specify dtype explicitly
[09/17/2024-07:07:28] [TRT-LLM] [I] Loading encoder weights from PT...
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.HALF but set to DataType.FLOAT
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.FLOAT but set to DataType.HALF
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.FLOAT but set to DataType.HALF
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.FLOAT but set to DataType.HALF
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.FLOAT but set to DataType.HALF
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.FLOAT but set to DataType.HALF
[09/17/2024-07:07:28] [TRT-LLM] [W] Parameter was initialized as DataType.FLOAT but set to DataType.HALF
[09/17/2024-07:07:28] [TRT-LLM] [I] Set bert_attention_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set gpt_attention_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set gemm_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set smooth_quant_gemm_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set identity_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set layernorm_quantization_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set rmsnorm_quantization_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set nccl_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set lookup_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set lora_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set weight_only_groupwise_quant_matmul_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set weight_only_quant_matmul_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set quantize_per_token_plugin to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set quantize_tensor_plugin to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set moe_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set mamba_conv1d_plugin to None.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set context_fmha to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set context_fmha_fp32_acc to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set paged_kv_cache to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set remove_input_padding to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set use_custom_all_reduce to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set multi_block_mode to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set enable_xqa to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set attention_qk_half_accumulation to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set use_paged_context_fmha to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set use_fp8_context_fmha to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set use_context_fmha_for_generation to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set multiple_profiles to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set paged_state to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set streamingllm to False.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set gemm_plugin to float16.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set bert_attention_plugin to float16.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set context_fmha to True.
[09/17/2024-07:07:28] [TRT-LLM] [I] Set context_fmha_fp32_acc to False.
[09/17/2024-07:07:28] [TRT] [W] IElementWiseLayer with inputs WhisperEncoder/SHUFFLE_0_output_0 and WhisperEncoder/conv1/SHUFFLE_1_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:07:28] [TRT] [W] IElementWiseLayer with inputs WhisperEncoder/conv1/SHUFFLE_1_output_0 and WhisperEncoder/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:07:28] [TRT] [W] IElementWiseLayer with inputs WhisperEncoder/SHUFFLE_2_output_0 and WhisperEncoder/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:07:28] [TRT] [W] IElementWiseLayer with inputs WhisperEncoder/conv1/SHUFFLE_1_output_0 and WhisperEncoder/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:07:28] [TRT] [W] IElementWiseLayer with inputs WhisperEncoder/SHUFFLE_3_output_0 and WhisperEncoder/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:07:28] [TRT-LLM] [I] Build TensorRT engine Unnamed Network 0
[09/17/2024-07:07:28] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2736, GPU 17363 (MiB)
[09/17/2024-07:07:28] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +2, GPU +10, now: CPU 2738, GPU 17373 (MiB)
[09/17/2024-07:07:28] [TRT] [W] TensorRT was linked against cuDNN 8.9.6 but loaded cuDNN 8.9.2
[09/17/2024-07:07:28] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[09/17/2024-07:08:06] [TRT] [I] Detected 2 inputs and 1 output network tensors.
[09/17/2024-07:08:06] [TRT] [I] Total Host Persistent Memory: 32384
[09/17/2024-07:08:06] [TRT] [I] Total Device Persistent Memory: 0
[09/17/2024-07:08:06] [TRT] [I] Total Scratch Memory: 33602560
[09/17/2024-07:08:06] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 258 steps to complete.
[09/17/2024-07:08:06] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 5.03566ms to assign 6 blocks to 258 nodes requiring 217874944 bytes.
[09/17/2024-07:08:06] [TRT] [I] Total Activation Memory: 217874432
[09/17/2024-07:08:06] [TRT] [I] Total Weights Memory: 176329728
[09/17/2024-07:08:06] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2966, GPU 17569 (MiB)
[09/17/2024-07:08:06] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2966, GPU 17579 (MiB)
[09/17/2024-07:08:06] [TRT] [W] TensorRT was linked against cuDNN 8.9.6 but loaded cuDNN 8.9.2
[09/17/2024-07:08:06] [TRT] [I] Engine generation completed in 38.3389 seconds.
[09/17/2024-07:08:06] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 5 MiB, GPU 1126 MiB
[09/17/2024-07:08:06] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +0, GPU +169, now: CPU 0, GPU 169 (MiB)
[09/17/2024-07:08:06] [TRT] [I] [MemUsageStats] Peak memory usage during Engine building and serialization: CPU: 4912 MiB
[09/17/2024-07:08:06] [TRT-LLM] [I] Total time of building Unnamed Network 0: 00:00:38
[09/17/2024-07:08:06] [TRT-LLM] [I] Config saved to whisper_small_en/encoder_config.json.
[09/17/2024-07:08:06] [TRT-LLM] [I] Serializing engine to whisper_small_en/whisper_encoder_float16_tp1_rank0.engine...
[09/17/2024-07:08:07] [TRT-LLM] [I] Engine serialized. Total time: 00:00:00
[09/17/2024-07:08:07] [TRT] [I] [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 2961, GPU 17373 (MiB)
[09/17/2024-07:08:07] [TRT-LLM] [W] Invalid timing cache, using freshly created one
[09/17/2024-07:08:07] [TRT-LLM] [I] Loading decoder weights from PT...
[09/17/2024-07:08:07] [TRT-LLM] [I] Set bert_attention_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set gpt_attention_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set gemm_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set smooth_quant_gemm_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set identity_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set layernorm_quantization_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set rmsnorm_quantization_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set nccl_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set lookup_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set lora_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set weight_only_groupwise_quant_matmul_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set weight_only_quant_matmul_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set quantize_per_token_plugin to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set quantize_tensor_plugin to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set moe_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set mamba_conv1d_plugin to None.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set context_fmha to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set context_fmha_fp32_acc to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set paged_kv_cache to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set remove_input_padding to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set use_custom_all_reduce to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set multi_block_mode to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set enable_xqa to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set attention_qk_half_accumulation to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set use_paged_context_fmha to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set use_fp8_context_fmha to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set use_context_fmha_for_generation to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set multiple_profiles to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set paged_state to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set streamingllm to False.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set gemm_plugin to float16.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set gpt_attention_plugin to float16.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set context_fmha to True.
[09/17/2024-07:08:07] [TRT-LLM] [I] Set context_fmha_fp32_acc to False.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/embedding/vocab_embedding/GATHER_0_output_0 and DecoderModel/embedding/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/embedding/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/0/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/0/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/0/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/0/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/0/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/0/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/0/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/0/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/0/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/0/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/1/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/1/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/1/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/1/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/1/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/1/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/1/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/1/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/1/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/1/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/2/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/2/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/2/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/2/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/2/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/2/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/2/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/2/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/2/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/2/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/3/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/3/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/3/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/3/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/3/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/3/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/3/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/3/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/3/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/3/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/4/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/4/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/4/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/4/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/4/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/4/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/4/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/4/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/4/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/4/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/5/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/5/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/5/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/5/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/5/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/5/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/5/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/5/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/5/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/5/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/6/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/6/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/6/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/6/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/6/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/6/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/6/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/6/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/6/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/6/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/7/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/7/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/7/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/7/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/7/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/7/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/7/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/7/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/7/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/7/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/8/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/8/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/8/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/8/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/8/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/8/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/8/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/8/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/8/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/8/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/9/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/9/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/9/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/9/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/9/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/9/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/9/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/9/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/9/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/9/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/10/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/10/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/10/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/10/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/10/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/10/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/10/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/10/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/10/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/10/ELEMENTWISE_SUM_2_output_0 and DecoderModel/decoder_layers/11/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/11/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[TensorRT-LLM][WARNING] Fall back to unfused MHA because of cross attention.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/ELEMENTWISE_SUM_1_output_0 and DecoderModel/decoder_layers/11/SHUFFLE_2_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/mlp/SHUFFLE_0_output_0 and DecoderModel/decoder_layers/11/mlp/fc/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/11/mlp/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/mlp/SHUFFLE_2_output_0 and DecoderModel/decoder_layers/11/mlp/ELEMENTWISE_POW_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/mlp/fc/ELEMENTWISE_SUM_0_output_0 and DecoderModel/decoder_layers/11/mlp/ELEMENTWISE_PROD_1_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/mlp/SHUFFLE_3_output_0 and DecoderModel/decoder_layers/11/mlp/ELEMENTWISE_SUM_0_output_0: first input has type Float but second input has type Half.
[09/17/2024-07:08:07] [TRT] [W] IElementWiseLayer with inputs DecoderModel/decoder_layers/11/ELEMENTWISE_PROD_2_output_0 and DecoderModel/decoder_layers/11/mlp/proj/ELEMENTWISE_SUM_0_output_0: first input has type Half but second input has type Float.
[09/17/2024-07:08:07] [TRT-LLM] [I] Build TensorRT engine Unnamed Network 0
[09/17/2024-07:08:07] [TRT] [W] Unused Input: cross_kv_cache_gen
[09/17/2024-07:08:07] [TRT] [W] [RemoveDeadLayers] Input Tensor cross_kv_cache_gen is unused or used only at compile-time, but is not being removed.
[09/17/2024-07:08:07] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 3015, GPU 17381 (MiB)
[09/17/2024-07:08:07] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 3015, GPU 17389 (MiB)
[09/17/2024-07:08:07] [TRT] [W] TensorRT was linked against cuDNN 8.9.6 but loaded cuDNN 8.9.2
[09/17/2024-07:08:07] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[09/17/2024-07:08:17] [TRT] [I] [GraphReduction] The approximate region cut reduction algorithm is called.
[09/17/2024-07:08:17] [TRT] [I] Detected 38 inputs and 25 output network tensors.
[09/17/2024-07:08:17] [TRT] [I] Total Host Persistent Memory: 51472
[09/17/2024-07:08:17] [TRT] [I] Total Device Persistent Memory: 0
[09/17/2024-07:08:17] [TRT] [I] Total Scratch Memory: 232116992
[09/17/2024-07:08:17] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 350 steps to complete.
[09/17/2024-07:08:17] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 14.0794ms to assign 16 blocks to 350 nodes requiring 459499520 bytes.
[09/17/2024-07:08:17] [TRT] [I] Total Activation Memory: 459497984
[09/17/2024-07:08:17] [TRT] [I] Total Weights Memory: 386860032
[09/17/2024-07:08:17] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 3067, GPU 17771 (MiB)
[09/17/2024-07:08:17] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 3067, GPU 17779 (MiB)
[09/17/2024-07:08:17] [TRT] [W] TensorRT was linked against cuDNN 8.9.6 but loaded cuDNN 8.9.2
[09/17/2024-07:08:17] [TRT] [I] Engine generation completed in 10.4557 seconds.
[09/17/2024-07:08:17] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 153 MiB, GPU 1126 MiB
[09/17/2024-07:08:17] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +0, GPU +369, now: CPU 0, GPU 369 (MiB)
[09/17/2024-07:08:17] [TRT] [I] [MemUsageStats] Peak memory usage during Engine building and serialization: CPU: 5244 MiB
[09/17/2024-07:08:18] [TRT-LLM] [I] Total time of building Unnamed Network 0: 00:00:10
[09/17/2024-07:08:18] [TRT-LLM] [I] Config saved to whisper_small_en/decoder_config.json.
[09/17/2024-07:08:18] [TRT-LLM] [I] Serializing engine to whisper_small_en/whisper_decoder_float16_tp1_rank0.engine...
[09/17/2024-07:08:18] [TRT-LLM] [I] Engine serialized. Total time: 00:00:00
Whisper small.en TensorRT engine built.
=========================================
Model is located at: /app/TensorRT-LLM-examples/whisper/whisper_small_en
root@c3f2d94d2f68:/app# python3 run_server.py --port 9090 \
                      --backend tensorrt \
                      --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en"
[TensorRT-LLM] TensorRT-LLM version: 0.9.0
--2024-09-17 07:11:35--  https://github.com/snakers4/silero-vad/raw/v4.0/files/silero_vad.onnx
Resolving github.com (github.com)... <IP_ADDRESS>
Connecting to github.com (github.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/snakers4/silero-vad/v4.0/files/silero_vad.onnx [following]
--2024-09-17 07:11:36--  https://raw.githubusercontent.com/snakers4/silero-vad/v4.0/files/silero_vad.onnx
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1807522 (1.7M) [application/octet-stream]
Saving to: ‘/root/.cache/whisper-live/silero_vad.onnx’

/root/.cache/whisper-live/silero_vad.onnx       100%[=====================================================================================================>]   1.72M  --.-KB/s    in 0.09s

2024-09-17 07:11:36 (19.7 MB/s) - ‘/root/.cache/whisper-live/silero_vad.onnx’ saved [1807522/1807522]

[c3f2d94d2f68:00045] *** Process received signal ***
[c3f2d94d2f68:00045] Signal: Segmentation fault (11)
[c3f2d94d2f68:00045] Signal code: Address not mapped (1)
[c3f2d94d2f68:00045] Failing at address: 0x18
[c3f2d94d2f68:00045] [ 0] /usr/lib/x86_64-linux-gnu/libc.so.6(+0x42520)[0x7efd9a0fe520]
[c3f2d94d2f68:00045] [ 1] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN12tensorrt_llm4thop14TorchAllocator6mallocEmb+0x88)[0x7efc04ba9dc8]
[c3f2d94d2f68:00045] [ 2] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6common10IAllocator8reMallocIiEEPT_S4_mb+0xb4)[0x7efc0e65f144]
[c3f2d94d2f68:00045] [ 3] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6layers18DynamicDecodeLayerI6__halfE14allocateBufferEv+0x3f)[0x7efc0e662b7f]
[c3f2d94d2f68:00045] [ 4] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6layers18DynamicDecodeLayerI6__halfE10initializeEv+0x1c6)[0x7efc0e664ca6]
[c3f2d94d2f68:00045] [ 5] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6layers18DynamicDecodeLayerI6__halfEC2ERKNS_7runtime12DecodingModeEiiiiP11CUstream_stSt10shared_ptrINS_6common10IAllocatorEEP14cudaDevicePropSt8optionalIiESH_+0x230)[0x7efc0e665150]
[c3f2d94d2f68:00045] [ 6] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN9torch_ext15FtDynamicDecodeI6__halfEC1Emmmmii+0x2ce)[0x7efc04b86fee]
[c3f2d94d2f68:00045] [ 7] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN9torch_ext15DynamicDecodeOp14createInstanceEv+0x8a)[0x7efc04b6bdba]
[c3f2d94d2f68:00045] [ 8] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN9torch_ext15DynamicDecodeOpC1EllllllN3c1010ScalarTypeE+0x84)[0x7efc04b6bf04]
[c3f2d94d2f68:00045] [ 9] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZNSt17_Function_handlerIFvRSt6vectorIN3c106IValueESaIS2_EEEZN5torch6class_IN9torch_ext15DynamicDecodeOpEE12defineMethodIZNSB_3defIJllllllNS1_10ScalarTypeEEEERSB_NS7_6detail5typesIvJDpT_EEESsSt16initializer_listINS7_3argEEEUlNS1_14tagged_capsuleISA_EEllllllSE_E_EEPNS7_3jit8FunctionESsT_SsSN_EUlS5_E_E9_M_invokeERKSt9_Any_dataS5_+0xf8)[0x7efc04b873f8]
[c3f2d94d2f68:00045] [10] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0xa0f34e)[0x7efd982de34e]
[c3f2d94d2f68:00045] [11] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0xa0c8df)[0x7efd982db8df]
[c3f2d94d2f68:00045] [12] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0xa0e929)[0x7efd982dd929]
[c3f2d94d2f68:00045] [13] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0x47de04)[0x7efd97d4ce04]
[c3f2d94d2f68:00045] [14] python3(+0x15adae)[0x55a567c1fdae]
[c3f2d94d2f68:00045] [15] python3(_PyObject_MakeTpCall+0x25b)[0x55a567c1652b]
[c3f2d94d2f68:00045] [16] python3(+0x169680)[0x55a567c2e680]
[c3f2d94d2f68:00045] [17] python3(+0x28139b)[0x55a567d4639b]
[c3f2d94d2f68:00045] [18] python3(_PyObject_MakeTpCall+0x25b)[0x55a567c1652b]
[c3f2d94d2f68:00045] [19] python3(_PyEval_EvalFrameDefault+0x6f0b)[0x55a567c0f16b]
[c3f2d94d2f68:00045] [20] python3(_PyFunction_Vectorcall+0x7c)[0x55a567c206ac]
[c3f2d94d2f68:00045] [21] python3(_PyObject_FastCallDictTstate+0x16d)[0x55a567c1576d]
[c3f2d94d2f68:00045] [22] python3(+0x1657a4)[0x55a567c2a7a4]
[c3f2d94d2f68:00045] [23] python3(_PyObject_MakeTpCall+0x1fc)[0x55a567c164cc]
[c3f2d94d2f68:00045] [24] python3(_PyEval_EvalFrameDefault+0x7611)[0x55a567c0f871]
[c3f2d94d2f68:00045] [25] python3(_PyFunction_Vectorcall+0x7c)[0x55a567c206ac]
[c3f2d94d2f68:00045] [26] python3(_PyEval_EvalFrameDefault+0x8cb)[0x55a567c08b2b]
[c3f2d94d2f68:00045] [27] python3(_PyFunction_Vectorcall+0x7c)[0x55a567c206ac]
[c3f2d94d2f68:00045] [28] python3(_PyObject_FastCallDictTstate+0x16d)[0x55a567c1576d]
[c3f2d94d2f68:00045] [29] python3(+0x1657a4)[0x55a567c2a7a4]
[c3f2d94d2f68:00045] *** End of error message ***
Segmentation fault (core dumped)

#276 should resolve this.

Unfortunately, does not seem like it.
(base) whiskas@debian-gpu:~$ docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it whisper-live-trt:latest
root@7f39b90ea7e6:/app# nvidia-smi
Thu Sep 19 11:45:35 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03              Driver Version: 560.35.03      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 3090        On  |   00000000:01:00.0 Off |                  N/A |
|  0%   36C    P8             12W /  420W |       4MiB /  24576MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
root@7f39b90ea7e6:/app# bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small.en
Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com
Requirement already satisfied: tensorrt_llm==0.10.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (0.10.0)
Requirement already satisfied: tiktoken in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (0.3.3)
Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (3.0.0)
Requirement already satisfied: kaldialign in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (0.9.1)
Requirement already satisfied: openai-whisper in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (20231117)
Collecting librosa
  Downloading librosa-0.10.2.post1-py3-none-any.whl (260 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 260.1/260.1 KB 1.9 MB/s eta 0:00:00
Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (0.12.1)
Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (0.4.5)
Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 10)) (4.40.2)
Requirement already satisfied: janus in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 11)) (1.0.0)
Installing collected packages: librosa
Successfully installed librosa-0.10.2.post1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Downloading small.en...
--2024-09-19 11:46:00--  https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt
Resolving openaipublic.azureedge.net (openaipublic.azureedge.net)... <IP_ADDRESS>, 2620:1ec:29:1::67
Connecting to openaipublic.azureedge.net (openaipublic.azureedge.net)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 483615683 (461M) [application/octet-stream]
Saving to: 'assets/small.en.pt'

small.en.pt                                      100%[=======================================================================================================>] 461.21M  16.6MB/s    in 20s

2024-09-19 11:46:20 (23.0 MB/s) - 'assets/small.en.pt' saved [483615683/483615683]

Download completed: small.en.pt
whisper_small_en
Running build script for small.en with output directory whisper_small_en
[TensorRT-LLM] TensorRT-LLM version: 0.10.0
[09/19/2024-11:46:22] [TRT-LLM] [I] plugin_arg is None, setting it as float16 automatically.
[09/19/2024-11:46:22] [TRT-LLM] [I] plugin_arg is None, setting it as float16 automatically.
[09/19/2024-11:46:22] [TRT-LLM] [I] plugin_arg is None, setting it as float16 automatically.
[09/19/2024-11:46:23] [TRT] [I] [MemUsageChange] Init CUDA: CPU +14, GPU +0, now: CPU 594, GPU 263 (MiB)
[09/19/2024-11:46:24] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +2132, GPU +396, now: CPU 2882, GPU 659 (MiB)
[09/19/2024-11:46:24] [TRT] [W] profileSharing0806 is on by default in TensorRT 10.0. This flag is deprecated and has no effect.

...

[09/19/2024-11:46:53] [TRT] [I] Total Weights Memory: 386860032 bytes
[09/19/2024-11:46:53] [TRT] [I] Compiler backend is used during engine execution.
[09/19/2024-11:46:53] [TRT] [I] Engine generation completed in 7.84096 seconds.
[09/19/2024-11:46:53] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 153 MiB, GPU 1126 MiB
[09/19/2024-11:46:53] [TRT] [I] [MemUsageStats] Peak memory usage during Engine building and serialization: CPU: 5786 MiB
[09/19/2024-11:46:53] [TRT-LLM] [I] Total time of building Unnamed Network 0: 00:00:07
[09/19/2024-11:46:53] [TRT-LLM] [I] Config saved to whisper_small_en/decoder_config.json.
[09/19/2024-11:46:53] [TRT-LLM] [I] Serializing engine to whisper_small_en/whisper_decoder_float16_tp1_rank0.engine...
[09/19/2024-11:46:53] [TRT-LLM] [I] Engine serialized. Total time: 00:00:00
Whisper small.en TensorRT engine built.
=========================================
Model is located at: /app/TensorRT-LLM-examples/whisper/whisper_small_en
root@7f39b90ea7e6:/app# python3 run_server.py --port 9090 \
                      --backend tensorrt \
                      --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en"
[TensorRT-LLM] TensorRT-LLM version: 0.10.0
--2024-09-19 11:47:44--  https://github.com/snakers4/silero-vad/raw/v4.0/files/silero_vad.onnx
Resolving github.com (github.com)... <IP_ADDRESS>
Connecting to github.com (github.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/snakers4/silero-vad/v4.0/files/silero_vad.onnx [following]
--2024-09-19 11:47:44--  https://raw.githubusercontent.com/snakers4/silero-vad/v4.0/files/silero_vad.onnx
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1807522 (1.7M) [application/octet-stream]
Saving to: ‘/root/.cache/whisper-live/silero_vad.onnx’

/root/.cache/whisper-live/silero_vad.onnx        100%[=======================================================================================================>]   1.72M  --.-KB/s    in 0.09s

2024-09-19 11:47:45 (19.3 MB/s) - ‘/root/.cache/whisper-live/silero_vad.onnx’ saved [1807522/1807522]

[7f39b90ea7e6:00362] *** Process received signal ***
[7f39b90ea7e6:00362] Signal: Segmentation fault (11)
[7f39b90ea7e6:00362] Signal code: Address not mapped (1)
[7f39b90ea7e6:00362] Failing at address: 0x18
[7f39b90ea7e6:00362] [ 0] /usr/lib/x86_64-linux-gnu/libc.so.6(+0x42520)[0x7f27331b6520]
[7f39b90ea7e6:00362] [ 1] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN12tensorrt_llm4thop14TorchAllocator6mallocEmb+0x88)[0x7f251c570d58]
[7f39b90ea7e6:00362] [ 2] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6layers18DynamicDecodeLayerI6__halfE14allocateBufferEv+0xd4)[0x7f253570f434]
[7f39b90ea7e6:00362] [ 3] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6layers18DynamicDecodeLayerI6__halfE10initializeEv+0x128)[0x7f2535713108]
[7f39b90ea7e6:00362] [ 4] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libtensorrt_llm.so(_ZN12tensorrt_llm6layers18DynamicDecodeLayerI6__halfEC2ERKNS_7runtime12DecodingModeERKNS0_13DecoderDomainEP11CUstream_stSt10shared_ptrINS_6common10IAllocatorEE+0xb1)[0x7f2535713311]
[7f39b90ea7e6:00362] [ 5] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN9torch_ext15FtDynamicDecodeI6__halfEC1Emmmmii+0x270)[0x7f251c550c70]
[7f39b90ea7e6:00362] [ 6] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN9torch_ext15DynamicDecodeOp14createInstanceEv+0x8a)[0x7f251c5340ca]
[7f39b90ea7e6:00362] [ 7] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZN9torch_ext15DynamicDecodeOpC1EllllllN3c1010ScalarTypeE+0x84)[0x7f251c534214]
[7f39b90ea7e6:00362] [ 8] /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libth_common.so(_ZNSt17_Function_handlerIFvRSt6vectorIN3c106IValueESaIS2_EEEZN5torch6class_IN9torch_ext15DynamicDecodeOpEE12defineMethodIZNSB_3defIJllllllNS1_10ScalarTypeEEEERSB_NS7_6detail5typesIvJDpT_EEESsSt16initializer_listINS7_3argEEEUlNS1_14tagged_capsuleISA_EEllllllSE_E_EEPNS7_3jit8FunctionESsT_SsSN_EUlS5_E_E9_M_invokeERKSt9_Any_dataS5_+0xf8)[0x7f251c551058]
[7f39b90ea7e6:00362] [ 9] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0xa0f34e)[0x7f27312de34e]
[7f39b90ea7e6:00362] [10] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0xa0c8df)[0x7f27312db8df]
[7f39b90ea7e6:00362] [11] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0xa0e929)[0x7f27312dd929]
[7f39b90ea7e6:00362] [12] /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so(+0x47de04)[0x7f2730d4ce04]
[7f39b90ea7e6:00362] [13] python3(+0x15cb2e)[0x55af3c364b2e]
[7f39b90ea7e6:00362] [14] python3(_PyObject_MakeTpCall+0x25b)[0x55af3c35b2db]
[7f39b90ea7e6:00362] [15] python3(+0x16b6b0)[0x55af3c3736b0]
[7f39b90ea7e6:00362] [16] python3(+0x2826fb)[0x55af3c48a6fb]
[7f39b90ea7e6:00362] [17] python3(_PyObject_MakeTpCall+0x25b)[0x55af3c35b2db]
[7f39b90ea7e6:00362] [18] python3(_PyEval_EvalFrameDefault+0x6b17)[0x55af3c353d27]
[7f39b90ea7e6:00362] [19] python3(_PyFunction_Vectorcall+0x7c)[0x55af3c36542c]
[7f39b90ea7e6:00362] [20] python3(_PyObject_FastCallDictTstate+0x16d)[0x55af3c35a51d]
[7f39b90ea7e6:00362] [21] python3(+0x1674b4)[0x55af3c36f4b4]
[7f39b90ea7e6:00362] [22] python3(_PyObject_MakeTpCall+0x1fc)[0x55af3c35b27c]
[7f39b90ea7e6:00362] [23] python3(_PyEval_EvalFrameDefault+0x72ea)[0x55af3c3544fa]
[7f39b90ea7e6:00362] [24] python3(_PyFunction_Vectorcall+0x7c)[0x55af3c36542c]
[7f39b90ea7e6:00362] [25] python3(_PyEval_EvalFrameDefault+0x8ab)[0x55af3c34dabb]
[7f39b90ea7e6:00362] [26] python3(_PyFunction_Vectorcall+0x7c)[0x55af3c36542c]
[7f39b90ea7e6:00362] [27] python3(_PyObject_FastCallDictTstate+0x16d)[0x55af3c35a51d]
[7f39b90ea7e6:00362] [28] python3(+0x1674b4)[0x55af3c36f4b4]
[7f39b90ea7e6:00362] [29] python3(_PyObject_MakeTpCall+0x1fc)[0x55af3c35b27c]
[7f39b90ea7e6:00362] *** End of error message ***
Segmentation fault (core dumped)

I'm running into the exact same issue. Using a GeForce GTX 1050, if that matters.

Docker image updated on ghcr. Le us know if the issue still persists.


rye init issue

When I type "rye init" command without define a project name, it automaticlly creates initial files in the local folder.
I think it's better to show the --help menu instead of just create the file directly.
Thanks.

This would feel unnatural to me since I interpret rye init as "init here" (rye init .). I don't interpret it as "tell me more about this command".

OK, I didn't realize rye init = rye init .  The dot is a default directory.
Since it is used for creating a new project, should it better to have user manually define the project name?
Or maybe specifly in documentation that rye init means created project in local folder?
Just some thoughts, thanks.

@yalunren let me know what you think about #401. Whenever I'm unsure of the usage for a command I'd use --help. So hopefully that PR at least makes it more clear.

@yalunren let me know what you think about #401. Whenever I'm unsure of the usage for a command I'd use --help. So hopefully that PR at least makes it more clear.

Got it. Thanks for the update!


see if you quality

Hello!
see if you quality
the phone number is in an approved country, you can add a phone number in the settings.
but he is confirmed
thank you

i can get airdrop?

Такая же проблема, а в настройках номер телефона указан.

Basically, big ---k you from them:


Remove RunTimeWarning

As mentioned here:
https://github.com/databio/pepatac/issues/186
Seeking bowtie2 indexes yield a warning
/usr/local/lib/python3.8/site-packages/refgenconf/refgenconf.py:773: RuntimeWarning: For genome 'rCRSd' path to the asset 'bowtie2_index/None:default' doesn't exist: /alias/rCRSd/bowtie2_index/default/rCRSd
warnings.warn(msg, RuntimeWarning)

This warning doesn't make sense and should be eliminated, since it's intended that this be a prefix and not a path.

Docs appear to be wrong, here. Please clarify.

The docs are correct. There was a bug in the code, which is now fixed.


Application crashes when trying to create resources from template

Describe the bug
Tried to create resources from the template. Received an error and the UI is locked.
To Reproduce
Steps to reproduce the behavior:

Open an empty project with an empty folder
Go to templates and select RoleBinding and ServiceAccount
Click Start when the modal pops-up

Expected behavior
Create the resource
Screenshots

Desktop (please complete the following information):

OS: MacOS
Version 1.4.0

Additional context
Add any other context about the problem here.

this happens because #1098 hasn't been merged yet

@erdkse is this working now?

@erdkse is this working now?

Yes, @olensmar everything seems fine now.


URL Fetch Limits Error

Has anyone run into URL Fetch Limits with the connector?
We have a large number of reports through GDS and we hit the fetch limit frequently however we have limited the call numbers. Are there other factor that Google decides to use to restrict? Is there a transfer rate limit per second for example?

I am receiving ThrottlingException errors frequently from the report I built with the connector. The report also seems to be pulling new data constantly even though I'm not editing any charts and the connector says the data freshness interval is 12 hours. I'm also trying to find a way to keep this from happening.
I don't know if this is an issue with the connector or data studio, but I thought my experience might be relevant. @pdhebar-ci have you found a solution to this issue?

I am receiving ThrottlingException errors frequently from the report I built with the connector. The report also seems to be pulling new data constantly even though I'm not editing any charts and the connector says the data freshness interval is 12 hours. I'm also trying to find a way to keep this from happening.
I don't know if this is an issue with the connector or data studio, but I thought my experience might be relevant. @pdhebar-ci have you found a solution to this issue?

I am receiving the same ThrottlingException errors, have you been able to solve this without limiting too much the data, @alex-wenzel ?

No @rdgrocha, from my recollection, I ended up creating an intermediate script that aggregated data into a Google Sheet and then pulled it into Data Studio from there.

I was trying to avoid Google Sheets but I don't think a have choice anymore. Can you share the script you used?

Hi everyone,
I'm getting the same error 'ThrottlingException' with Google Data Studio and an Athena Connector, someone found a solution that avoid the use of a Google Sheet to collect the data?
Thanks


Unable to access 'fields' and 'values' in a validator when using @validate_call

Initial Checks

[X] I confirm that I'm using Pydantic V2

Description
Hi, I've come across a possible loss of functionality with v2...
In v1 is was possible to access 'fields' and 'values' in a validator when using @validate_arguments. Is this still possible in v2 with @validate_call?
The example explains what I'm after, using the case of validating later parameters depending on the values received by an earlier parameter (in the same way that a model's validators can access the values of earlier fields).
I raised the question in #6345 with a more convoluted example using custom types. I'm not so concerned which approach to use as simply being able to do this in some way.
Possibly related, this part of the docs notes that:
These names (together with "args" and "kwargs") may or may not (depending on the function's signature) appear as fields on the internal Pydantic model accessible via .model
...but from where can .model be accessed? Am I not seeing the elephant?
Thank you.
Example Code
from pydantic import BeforeValidator, validate_call
from typing import Annotated

def validator_a(a_val) -> str:
    # Validate something.
    return a_val

def validator_b(b_val) -> str:
    # Validate something according to the value received by `func`s 'a' parameter, i.e.
    # how can I access in this scope the object received by `func`s 'a' parameter and/or
    # as returned by `validator_a`?

    # If validation fails, raise a custom exception that references the parameter name,
    # i.e. can the parameter name be accessed from this scope ('b' in this case)?

    return b_val

@validate_call
def func(
    a: Annotated[str, BeforeValidator(validator_a)],
    b: Annotated[str, BeforeValidator(validator_b)],
):
    return a + b

Python, Pydantic & OS Version
pydantic version: 2.0.3
pydantic-core version: 2.3.0 release build profile
install path: C:\.venv\Lib\site-packages\pydanticpython version: 3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]
platform: Windows-10-10.0.22621-SP0
optional deps. installed: ['typing-extensions']

Selected Assignee: @Kludex

There is no longer a .model in v2 — we don't use an auxiliary basemodel to do the validation in v2, but just perform the validation directly using pydantic-core.
Unfortunately, it seems that section of the docs was overlooked and is no longer correct and should be updated.
For what it's worth, in principle I think it should be possible to create a model from a callable by combining inspect.signature and pydantic.create_model if you need this functionality.

Thanks for the reply @dmontagu.
So it seems the functionality to access  'fields' and 'values' in a validator when using @validate_call has been lost in v2(?). If this is the case then could you label this issue as a feature request please? Thank you.

Could you provide an example of how you want to use these things? Would a function that accepts a callable and builds a model based on the signature of that callable be sufficient for your purposes?

For what it's worth, this is an attempt at replicating the v1 logic:
import inspect
from typing import Callable, Any

from pydantic import create_model, Field
from pydantic.alias_generators import to_pascal

def validate_call_model(f: Callable[..., Any]):
    signature = inspect.signature(f)
    parameters = signature.parameters
    field_definitions: dict[str, Any] = {}
    for name, param in parameters.items():
        annotation, default = param.annotation, param.default
        if annotation is param.empty:
            annotation = Any
        if default is param.empty:
            default = Field(...)
        field_definitions[name] = (annotation, default)

    model = create_model(to_pascal(f.__name__), __module__=str(f.__module__), **field_definitions)
    return model

# Usage example:
def my_func(x: int, y, z: str = 2) -> float:
    return 1.1

my_func_model = validate_call_model(my_func)
print(my_func_model)
# > <class '__main__.MyFunc'>
print(my_func_model.model_fields)
# > {'x': FieldInfo(annotation=int, required=True), 'y': FieldInfo(annotation=Any, required=True), 'z': FieldInfo(annotation=str, required=False, default=2)}

Thank you for looking at this further.

Could you provide an example of how you want to use these things?

Really just as the example in my initial comment, i.e. to be able to validate later function parameters depending on the values received by earlier parameters (in the same way that a model's validators can access the values of earlier fields). My reference to .model  came about from looking through the doc and thinking it might be something to explore to this end, although not being able to find it in v2.

Sorry, I was confused about your request, I understand now.
Yeah, this is definitely a piece of functionality that is not present now.
This is pretty gross, but it's the best way I can figure out to achieve this today:
from typing import Tuple, Any, Dict

from pydantic import validate_call
from pydantic_core import core_schema

def func(a: str, b: str):
    return a + b

def _validate_func_arguments(args_kwargs: Tuple[Tuple[Any, ...], Dict[str, Any]]):
    args, kwargs = args_kwargs
    a = kwargs.get('a', args[0])
    b = kwargs.get('b', args[1])

    if a == b:
        raise ValueError('a and b must not be equal')

    return args, kwargs

def _get_pydantic_core_schema_func(source_type, handler):
    schema = handler(source_type)

    schema['arguments_schema'] = core_schema.no_info_after_validator_function(
        _validate_func_arguments, schema['arguments_schema']
    )

    return schema

func.__get_pydantic_core_schema__ = _get_pydantic_core_schema_func

func = validate_call(func)

print(func('abc', 'def'))
#> 'abcdef

func('abc', 'abc')
"""
pydantic_core._pydantic_core.ValidationError: 1 validation error for func
  Value error, a and b must not be equal [type=value_error, input_value=ArgsKwargs(('abc', 'abc')), input_type=ArgsKwargs]
    For further information visit https://errors.pydantic.dev/2.1/v/value_error
"""

That said, I wouldn't promise that this code snippet will work forever; while I don't see any reason we'd change the relevant functionality, it's also making use of various implementation details of ValidateCallWrapper etc.
I think the best thing we could do in terms of feature work to better support this sort of thing would be to add a better way to provide after-validators that can modify the args/kwargs, and to provide some sort of cleaner api for accessing their values by name regardless of how they were passed into the function.
I suspect the amount of interest in this feature relative to the amount of work it would take to support it would be fairly high, but I would be open to reviewing related PRs. (Might require getting hands dirty with pydantic core though.) If you wanted to go down this route though it would probably be best to get buy-in on the idea before spending too much time implementing though.

Another viable path might be creating a separate decorator that actually builds a model and accepts model validators, and then passes the values to the function. Not sure if that would be of interest to you but it would probably be easier to do without modifying existing internals, at the expense of a little additional overhead.

Wow, I would never have got there! Thank you for that example and for the thought you've given the issue.
I use pydantic to abstract away the validatation and parsing of input to public functions, including through these custom types. How I use it is a bit clunky although I found it preferable to validating input at the start of each function or letting errors that are rooted in invalid inputs arise where they may with unintelligible messages.
I'd love to be in a position to offer a PR, although my dependence on pydantic isn't sufficient to justify the time that I'd require to get up to speed with the internals (and learning Rust hasn't made it off my todo list yet).
I've drafted out a simple library to verify function inputs against type annotations (the tests give an idea of what it does). With the changes in v2, I think my path of least resistance has shifted to dropping pydantic in favour of this more limited, specific solution.
Thank you again, I'll certainly keep an eye on this feature request - interested to see if there are other users with similar needs.

I've published the library as valimp. It's limited to validating, parsing and coercing inputs to public functions. I've included a note to the README on how it compares to the Pydantic V1 @validate_argumnents and V2 @validate_call decorators. If you feel I've overlooked anything, let me know and I'll revise it. Thanks.

I think this is the same issue as #7448, I think we should considered reverting the behaviour to allow field_name and data to be available in all validator functions.


Update Cron construct documentation

As far as I can tell, it should be enabled: !app.local if you want to "[d]isable the cron job from automatically running while developing" ? Otherwise it's only enabled if running locally.

Of course! Thanks @abirtley.


Mkdocs build fails while building documentation for fastai

The pytkdocs tool is unable to load Python objects for classes that are inherited from the 'GetAttr' class.


Keep sensitive settings as GitHub defaults

Description

I would strongly suggest keeping sensitive github settings as default in redined-github, or having a walk-through when installing the extension to changes default github behavior such as PR head branch auto delete.

Correct me if I am wrong, installed this amazing extensions a long time ago, cannot really remember if I was prompted to check the PR head branch auto delete.

I thought there were some actions or branch settings or default settings do delete the head branch after PR merged, then I checked all of them, and just realized that I have refine-github installed (Gotta admit, you are doing an amazing job with this extension! I haven't even realized it's a third party tool lmao).

Post Edit: I did not see "via Refined GitHub" in the branch delete log as shown in the official demo screenshot:

Instead, I just got:

Example URLs

Discussed many times


Bumping version to python 3.11 leads to OverrideNeeded/Assertion error

error.txt
Figure out what blocks python 3.11. Probably could be related to kfp, but the error hints to jinja/mlflow.
Googling leads to not related issue: https://github.com/python-poetry/poetry/issues/5791

Limiting it even further (removing both mlflow/kedro-mlflow has lead to the fact that underlying error for all those cases is this AssertionError. https://github.com/python-poetry/poetry/issues/8202 Hints what could be going on


where can I get the token? (i have 3 different ones)

Meta sucks, and here are some different tokens you can get:
User Token & app token
you can get them both here: https://developers.facebook.com/tools/accesstoken/

system user token
this is generated inside meta's business settings

which token is needed to use heyoo?

I will now close this


[Task]: Fix flashed sections on selected candy machine

When you select a candy machine, there is a flash of unminted NFTs that shouldnt be there

I would appreciate being one of the reviewers of the PR linked to this task

I can't reproduce this bug in the application @begonaalvarezd

It doesnt happen anymore


How can I timeout a socket?

How can I timeout a socket and close it if it stops sending something to the server after a set time

<?php
require_once __DIR__ . '/Workerman/Autoloader.php';
use Workerman\Worker;
use Workerman\Lib\Timer;

// Time out
define('HEARTBEAT_TIME', 55);

$worker = new Worker('websocket://<IP_ADDRESS>:1234');

$worker->onMessage = function($connection, $msg) {
    $connection->lastMessageTime = time();
    // Other codes....
};

$worker->onWorkerStart = function($worker) {
    Timer::add(1, function()use($worker){
        $time_now = time();
        foreach($worker->connections as $connection) {
            if (empty($connection->lastMessageTime)) {
                $connection->lastMessageTime = $time_now;
                continue;
            }
            if ($time_now - $connection->lastMessageTime > HEARTBEAT_TIME) {
                $connection->close();
            }
        }
    });
};

Worker::runAll();

<?php
require_once __DIR__ . '/Workerman/Autoloader.php';
use Workerman\Worker;
use Workerman\Lib\Timer;

// Time out
define('HEARTBEAT_TIME', 55);

$worker = new Worker('websocket://<IP_ADDRESS>:1234');

$worker->onMessage = function($connection, $msg) {
    $connection->lastMessageTime = time();
    // Other codes....
};

$worker->onWorkerStart = function($worker) {
    Timer::add(1, function()use($worker){
        $time_now = time();
        foreach($worker->connections as $connection) {
            if (empty($connection->lastMessageTime)) {
                $connection->lastMessageTime = $time_now;
                continue;
            }
            if ($time_now - $connection->lastMessageTime > HEARTBEAT_TIME) {
                $connection->close();
            }
        }
    });
};

Worker::runAll();

Thank you very much for your help, but nevertheless the client is closing in the established time, transmit or not, how can I do to fix it, the idea is that if the client is transmitting, do not remove it from the server, I only remove it if after a time that I have not transmitted anything to the attached server my code:
onMessage = function($connection, $msg) {
    $connection->lastMessageTime = time();

};

// recibe data enviada
$worker->onMessage = function ($connection, $data) {
    // Send data to client
    echo $data;
    $connection->send("Hello  \n");

};

// funcion timer , si el cliente no trasmite lo saca del servidor
$worker->onWorkerStart = function($worker) {
    Timer::add(1, function()use($worker){
        $time_now = time();
        foreach($worker->connections as $connection) {
            if (empty($connection->lastMessageTime)) {
                $connection->lastMessageTime = $time_now;
                continue;
            }
            if ($time_now - $connection->lastMessageTime > HEARTBEAT_TIME) {
                $connection->close();
            }
        }
    });
};

Worker::runAll();

There are two onmessage conflicts. Please delete the below and keep the above test again

So it worked perfect, however, I cannot take the data that it sends me and process it, in which way can I take the data sent by the client, and do a process and then respond to something

I think I have made it, I really appreciate your help

You are welcome

I know this is old but I have a client software that sends "ping" requests which doesn't get catched at onMessage, is there a solution to keep such connection alive while using this method of timeout, or is there another method for timeout that works?


Distinguish between responsive inputs and array inputs

currently, we store responsive values as arrays. however, we also store some types of inputs as arrays (like box shadow). We currently distinguish between them by having only a small set of inputs be responsive.

Wonder if we want to do something similar to what I did for "multidimensional inputs" where I made a specific type to differentiate between its array value and a responsive array:
type MultidimensionalLength = {
  type: 'multidimensionalLength',
  value: Length[]
}


rustic-babel: support auto wrap src block body in a fn main if none exists

close #267(Maybe)
related: #266
:main
Auto wrap whole block body in a fn main function call if none exists.
Since this is very handy in most code snippets, so the default value is yes.
no if you don't want this feature(for example, you don't want regex search slow things down).
You can also set a default value by:
;; By setq this default to `nil`, you'll have to explict set params to ":main yes" in each block
(setq rustic-babel-auto-wrap-main nil)

#+begin_src rust :main yes
let x = vec![1, 2, 3].iter().map(|&x| x + 1).collect::<Vec<_>>();
println!("{:?}", x);
#+end_src

#+results:
: [2, 3, 4]

Finally, by rm the following lines, I've got the tests passed:
  (let* ((string "let x = \"fn main(){}\";")
         (buf (rustic-test-get-babel-block string)))
    (with-current-buffer buf
      (rustic-test-babel-execute-block buf)
      (should (eq (rustic-test-babel-check-results buf) nil))))

Not quite know where's the problem.

Not quite know where's the problem.

Yeah, sometimes this fails like magic =)
Thanks for your work!


Master

some small changes with removing width to center box and added max-width to text

review?


Support rails 7.2

~The pull request is currently in draft status.
Once the beta release of Rails is replaced by a stable release, I will open the pull request.~
Rails 7.2.0 has been released, I have added support for activerecord-multitenant.
The changes were commented on in the review.

@serprex  Rails 7.2 has been released and PR is now open. Please review 😄 🙏

@alpaca-tc static-checks are failing

@gurkanindibay fixed 💛

@alpaca-tc thanks for your contribution

@gurkanindibay @alpaca-tc
Thank you for addressing this issue. This fix is crucial for users who have upgraded to Rails 7.2, as the current version throws an ArgumentError with the find_by method.
Is there an estimated timeline for when we might see a new release that includes this change? Many of us are eagerly waiting for this fix to be available in a stable release.
Thanks again for your work on this valuable gem!


OpenCV3

I have issue with OpenCV3 and the convert_video.py. It says:
AttributeError: 'module' object has no attribute 'cv'

@enzyme69 Is this still an issue?
Recommend closing if not.


/wild [BUG]

Describe the bug
I'm on Spawn world and trying tp to world
To Reproduce
Steps to reproduce the behavior:

Change the config as mine: https://pastebin.com/wynFAA5z
Go to 'another world'
try to tp yourself into world
error pops up

Screenshots
https://pastebin.com/A6WMam30

Did you figure out ypur issue?

Did you figure out ypur issue?

Yes


Heroku db is different?

On my local db, we can run the query
SELECT * FROM performances WHERE openAt < '2016-06-01T04:56:03.690Z'::date
ORDER BY openAt desc LIMIT 1;

To get the next performance. When we run on Heroku's db with the same data, it doesn't return anything. The performances are there with the correct times, but it doesn't return anything

@tarebyte ...help? I'm stumped on this one

@atareshawty there shouldn't be any difference, between the two data sets outside of the time it was created. Unless your literally migrating over the data from dev to prod.

Idk now, but it's not 'not' broken... @tarebyte


Split out SphericalMercator to library?

Before realising a lot of the translating between SRSs could be offloaded to PostGIS, I wrote some projection code to deal with EPSG3857. Might as well track that somewhere - but it doesn't belong in hastile. hastile might depend on that library for the stuff it needs though.

Did this. Half baked code dumped in another library. It's not really fit for publishing, but if anyone wants it let me know.


refactoring #approve_project

Calling the #approve_project method on a project sets the status to Project::APPROVED_STATUS
Calling the #approve_project method on a project records the mediaflux id in project.mediaflux_id

closes #639

Thanks for the review @carolyncole!


issue-2715 added handling of the .play() promise errors to vimeo player

Coverage remained the same at 74.487% when pulling 9eba1f4a7839c5a973cd22080745c6eeb4f24d11 on invdigdev:issue-2715-play-pause-race into f083cbad9e2f380e6f1dcf1b7fd37f45c7e52046 on mediaelement:master.

Thanks for the PR :) Will be available in upcoming 2.5.1


picture might have been taken down

i just installed this theme with colored text and it didnt have the background on it so i found the picture you used and uploaded it to imgur @ https://imgur.com/gallery/BmkMz but it didnt work

ok so i fixed it use this link here instead https://i.imgur.com/AXXwhUn.jpg


 Agents must be provided a valid licenseKey and applicationID at runtime.

Overview
Shut down the agent with a new warning code if invalid licenseKey or App ID are provided.  Also fixed an issue where the warning code did not link correctly.

Related Issue(s)
NR-257211

Testing
Tests have been updated to reflect the new behavior

I am still seeing harvests locally when I comment out the license key in the test server.

Thanks, im going to move this logic to just before the rum call. itll be simpler and more effective for harvest cancelling


remove text-only tokens from button component

Summary | Résumé
Small refactor of the button component. Since we are adding a gcds-link component, we decided to discontinue support for a text-only button style for the gcds-button component.
This PR is removing the text-only tokens.
Notes BEFORE merging:

This PR can't be merged until the gcds-link component + its guidance is live.

Components PR

This looks great but I will hold off on approving until we have the Link component in place

Link is published now, can we revisit this and close it?


MSID: 98922 Version: 1 DOI: 10.1101/2<IP_ADDRESS>4154

MSID: 98922
Version: 1
Preprint DOI: https://doi.org/10.1101/2<IP_ADDRESS>4154
Step 1. Awaiting reviews
Editorial to post reviews via hypothesis
Useful links:

DocMap: https://data-hub-api.elifesciences.org/enhanced-preprints/docmaps/v2/by-publisher/elife/get-by-manuscript-id?manuscript_id=98922
New model tracking: https://docs.google.com/spreadsheets/d/1_fHaoOy7hjyocptKtVJRijeNpUY4hBS7Ck_aVmx6ZJk/
Reviews on sciety: https://sciety.org/articles/activity/10.1101/2<IP_ADDRESS>4154

For trouble shooting (e.g. no Docmaps available):

DocMap issue addressing: https://miro.com/app/board/uXjVNCwK6EI=/
Explore DataHub DocMaps API: https://lookerstudio.google.com/reporting/4c2f0368-babb-4beb-b5b3-497e7e7b0f08/page/ejphD
Unmatched submissions and preprints: https://lookerstudio.google.com/u/0/reporting/9f86204f-3bf7-477c-9b18-5c5ef141bf69/page/p_gxi57ha93c
Unmatched manuscripts spreadsheet: https://docs.google.com/spreadsheets/d/15QcK8w-ssB7109RQEDtFpJPZ0J5HTGxoHa_2TtpMBbg/edit#gid=1336081641

Step 2. Preview reviewed preprint
Production QC content ahead of publication
Instructions:

QC preview: https://prod--epp.elifesciences.org/previews/98922v1
Update ticket with any problems (add blocked label)
When QC OK, add QC OK label to ticket and add publication date and time to https://docs.google.com/spreadsheets/d/1amAlKvdLcaDp5W8Z8g77NmkwbMF5n_u89ArSqPMO8jg
Move card to next column
(At end of the day post link in #enhanced-preprint and ask for PDF to be generated)

Useful links:

Preprint DOI :  https://doi.org/10.1101/2<IP_ADDRESS>4154
Confirm reviews returned by EPP: https://prod--epp.elifesciences.org/api/reviewed-preprints/98922/v1/reviews
To update the MECA path in the docmap: https://docs.google.com/spreadsheets/d/1mctCQuNFBjSn97Lihy7_vBO6z7-N-oqyLv4clyi6zHg

Step 3: Awaiting search reindex
This step adds the reviewed preprint to the homepage: https://elifesciences.org
The search reindex is triggered once an hour. We need the reviewed preprint to be indexed as the search application serves the journal homepage.
Useful links:

Jenkins pipeline to reindex search can be triggered sooner or monitored here: https://alfred.elifesciences.org/job/process/job/process-reindex-reviewed-preprints/

Step 4: Published! PDF requested
Waiting for PDF to be generated
Useful links:

PDF tracking: https://docs.google.com/spreadsheets/d/106_XeDjmuBae7gexOTNzg60lapeqjl2aRn9DzupGyS8/

Step 5: Introduce PDF to data folder and git repo
Upload PDF to relevent folder in git repo https://github.com/elifesciences/enhanced-preprints-data/
Step 6: Done!

[ ] Kettle is on!

warn: [journal-ref-text-content] This journal reference (id c40) has untagged textual content - (1 APR), 1–14. . Is it tagged correctly?
error: [journal-ref-article-title] This journal reference (id c75) has no article-title element.


Fix typo bug in System Sizing macros

Description
Change variable 'montn' to 'mount'
Note: this fix hasn't been tested
Type of change

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

Checklist:
If you have added a new compute module in a SSC pull request related to this one, be sure to check the Process Requirements.

[x] My code follows the style guidelines of this project
[ ] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
[ ] Any dependent changes have been merged and published in downstream modules
[ ] I have checked my code and corrected any misspellings

Please also fix the typo in .../macros/PV battery/System Sizing.lk

Other typo?

Please also fix the typo in .../macros/PV battery/System Sizing.lk

@cpaulgilman please clarify, I still don't see the typo you're asking me to fix. I've scoured the graphic to find it unsuccessfully. Can you point it out ?

Please also fix the typo in .../macros/PV battery/System Sizing.lk

@cpaulgilman please clarify, I still don't see the typo you're asking me to fix. I've scoured the graphic to find it unsuccessfully. Can you point it out ?

The typo is fixed in both copies of the script. Thank you.


Database fields are not being created for interfaces

Adding interface to my datamodel, and running graphback generate includes the interface in the generated schema. But database fields of the interface are not being created in the types on running graphback db. We should be creating database fields and #297 had the work for creating them. @craicoverflow

Hi @ankitjena. I have been unable to reproduce this bug. Could you show your GraphQL schema?
Could it be that you are not actually providing the field to your type? You must still include the field in your type, it does not automatically inherit from an implemented interface.

I have found issue about graphback - build process overrides permissions so everytime we build we loosing global npm link and we executing globally available command.
This leads to many things simply not working when breaking changes were introduced.
This can be case here.

Could it be that you are not actually providing the field to your type? You must still include the field in your type, it does not automatically inherit from an implemented interface.

Oh my bad then, I wasn't including the field again in the type thinking that it was handled. Don't you think that creates redundancy?

I have found issue about graphback - build process overrides permissions so everytime we build we loosing global npm link and we executing globally available command.
This leads to many things simply not working when breaking changes were introduced.
This can be case here.

Not the case here. I saw the bug so edited my scripts a bit in local machine.

Oh my bad then, I wasn't including the field again in the type thinking that it was handled. Don't you think that creates redundancy?

From the GraphQL specification:

GraphQL interfaces represent a list of named fields and their arguments. GraphQL objects can then implement these interfaces which requires that the object type will define all fields defined by those interfaces.

This implies that the input schema would be invalid if a type does not provide a required interface field.
My original plan was to generate these automatically, but I changed it as this would be a bit misleading as the input schema should be valid.

Don't you think that creates redundancy?

A little bit, but that is how interfaces should work. Object extensions would would be more suitable if a developers wishes for their fields to automatically inherit (though I don't believe we have this yet).

From the GraphQL specification:

GraphQL interfaces represent a list of named fields and their arguments. GraphQL objects can then implement these interfaces which requires that the object type will define all fields defined by those interfaces.

This implies that the input schema would be invalid if a type does not provide a required interface field.
My original plan was to generate these automatically, but I changed it as this would be a bit misleading as the input schema should be valid.

Okay got it. Thanks for clearing that out.


Bottle no longer running with Jython

Bottle was running with version 0.11, the current development version "'0.12-dev" seem to drop jython support.
File "/path/to/bottle/bottle.py", line 1989, in
class _iterchain(itertools.chain):
TypeError: Error when calling the metaclass bases
reflectedfunction(): expected 1 args; got 3

For Jython 2.7.1, we fixed an underlying Jython bug in our regex implementation, namely lack of support for zero-width matches. So @defnull, that was a correct hypothesis, although it took us some time to find the specific problem! So now Jython supports SimpleTemplate processing. See https://hg.python.org/jython/rev/636b124a7587 for the specific change set.
I didn't look at all servers in the test suite (which are the remaining errors), and I know some are not supported on Jython like eventlet (possibly never will support eventlet), but servers like CherryPy run without problem. But for Jython, usually Bottle apps should run in the context of a WSGI/servlet bridge like Fireside or ModJy, which should just work now.

Thanks for the follow up :)


pdctl: use cobra print function

What problem does this PR solve?
Cobra has its own print functions which can be used to specify the output. In addition, we can use it to write some pd-ctl tests.
What is changed and how it works?
This PR replaces all standard print functions in pd-ctl with cobra's print functions.
Check List
Tests

Manual test

@siddontang Yes, the same as before.


Error using mne.read_evoked_fieldtrip: Key Error Data Name

I have a .mat file with averages for different conditions that were computed in Fieldtrip. I am trying to use mne.read_evoked_fieldtrip, but when I run this code I get an error: KeyError:  's2ICAOK_OFF_D_T1'. I read the epochs with no issue.
import matplotlib.pyplot as plt

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

import mne
from mne.datasets import sample
from mne.decoding import GeneralizingEstimator

print(__doc__)

data_epochs = mne.read_epochs_fieldtrip('s21ICAOK.mat', None,data_name='s21ICAOK')
data_on = mne.read_evoked_fieldtrip('s2ICAOK_OFF_D_T1.mat', None, data_name='s2ICAOK_OFF_D_T1')

are you sure data_name is correct? how did you save the file with fieldtrip? did you manually edit the matlab structure?

Hello,
When I load data using mne.read_evoked_fieldtrip, I am not able to read times, I added tmin value in the function by reading it from the fieldtrip structure as follows.
`    time= np.squeeze(ft_struct['time'])
tmin = time[0]
info = _create_info(ft_struct, info)  # create info structure
data_evoked = ft_struct['avg']  # create evoked data
evoked = EvokedArray(data_evoked, info, comment=comment, tmin=tmin)

`
This works fine and I am able to plot the data as expected. However, to do baseline correction in this evoked data, I had to add one hack to read_evoked_fieldtrip function:
    if(comment==None):
        comment = ''

If I don't do this any evoked method that reads the comment field gives error: object of type 'NoneType' has no len().
This error is coming from the following patch:
 def __repr__(self):  # noqa: D105
        max_comment_length = 1000
        if len(self.comment) > max_comment_length:
            comment = self.comment[:max_comment_length]
            comment += "..."

This is just for information.

Hello,
When I load data using mne.read_evoked_fieldtrip, I am not able to read times, I added tmin value in the function by reading it from the fieldtrip structure as follows.
tmin = time[0]

info = _create_info(ft_struct, info)  # create info structure
data_evoked = ft_struct['avg']  # create evoked data
evoked = EvokedArray(data_evoked, info, comment=comment, tmin=tmin)

This works fine and I am able to plot the data as expected. However, to do baseline correction in this evoked data, I had to add one hack to read_evoked_fieldtrip function:
if(comment==None):
    comment = ''

If I don't do this any evoked method that reads the comment field gives error: object of type 'NoneType' has no len().
This error is coming from the following patch:
def repr(self):  # noqa: D105
max_comment_length = 1000
if len(self.comment) > max_comment_length:
comment = self.comment[:max_comment_length]
comment += "..."
This is just for information.

@transpersonify I've migrated your comment into a new issue, #11296. Please post any follow-ups there

closing, as >1 year with no response from OP.


(Suggestion) Opacity Setting

I used a resource pack that lowered the opacity of minecraft rain but I've since started using this mod because it looks much nicer, however I'd love the ability to lower the opacity for the particles so that I can keep them dense but less obstructive. Thanks!

resourcepack support and an additional opactity slider are available in the upcomincg v3 update

Sweet! Thank you