❓ [Question] How do you find the exact line of python code that triggers a backend compiler error?

I was trying to compile the huggingface Llama 2 model using the following code:
import os
import torch
import torch_tensorrt
import torch.backends.cudnn as cudnn
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch._dynamo as dynamo
from optimum.onnxruntime import ORTModelForCausalLM

base_model = 'llama-2-7b'
comp_method = 'magnitude_unstructured'
comp_degree = 0.2

model_path = f'vita-group/{base_model}_{comp_method}'
model = AutoModelForCausalLM.from_pretrained(
       model_path,
       revision=f's{comp_degree}',
       torch_dtype=torch.float16,
       low_cpu_mem_usage=True,
       device_map="auto")
model.save_pretrained("model_ckpt/")
model.eval()

# setting
# torch._dynamo.config.suppress_errors = True
enabled_precisions = {torch.float, torch.int, torch.long}
debug = False
workspace_size = 20 << 30
min_block_size = 7
torch_executed_ops = {}

compilation_kwargs = {
    "enabled_precisions": enabled_precisions,
    "debug": debug,
    "workspace_size": workspace_size,
    "min_block_size": min_block_size,
    "torch_executed_ops": torch_executed_ops,
}

with torch.no_grad():
    optimized_model = torch.compile(
            model.generate,
            backend="torch_tensorrt",
            dynamic=True,
            options=compilation_kwargs,
            )

    tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')
    input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids.cuda()

    #outputs = model.generate(input_ids, max_new_tokens=128)
    outputs = optimized_model(input_ids, max_new_tokens=128)

And here is the complete log:
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:0 supported operations detected in subgraph containing 0 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:0 supported operations detected in subgraph containing 0 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:0 supported operations detected in subgraph containing 0 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:0 supported operations detected in subgraph containing 0 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:0 supported operations detected in subgraph containing 0 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:1 supported operations detected in subgraph containing 2 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

/usr/local/lib/python3.10/dist-packages/torch/overrides.py:111: UserWarning: 'has_cuda' is deprecated, please use 'torch.backends.cuda.is_built()'
  torch.has_cuda,
/usr/local/lib/python3.10/dist-packages/torch/overrides.py:112: UserWarning: 'has_cudnn' is deprecated, please use 'torch.backends.cudnn.is_available()'
  torch.has_cudnn,
/usr/local/lib/python3.10/dist-packages/torch/overrides.py:118: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'
  torch.has_mps,
/usr/local/lib/python3.10/dist-packages/torch/overrides.py:119: UserWarning: 'has_mkldnn' is deprecated, please use 'torch.backends.mkldnn.is_available()'
  torch.has_mkldnn,
Traceback (most recent call last):
  File "/workspace/workspace/scripts/vita/test.py", line 59, in <module>
    outputs = optimized_model(input_ids, max_new_tokens=128)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 333, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/external_utils.py", line 17, in inner
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1408, in generate
    self._validate_model_class()
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1415, in <resume in generate>
    new_generation_config = GenerationConfig.from_model_config(self.config)
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1426, in <resume in generate>
    generation_config = copy.deepcopy(generation_config)
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1429, in <resume in generate>
    self._validate_model_kwargs(model_kwargs.copy())
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1429, in <resume in generate>
    self._validate_model_kwargs(model_kwargs.copy())
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1432, in <resume in generate>
    logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1433, in <resume in generate>
    stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1602, in <resume in generate>
    return self.greedy_search(
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2404, in greedy_search
    eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2450, in <resume in greedy_search>
    outputs = self(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 493, in catch_errors
    return callback(frame, cache_size, hooks, frame_state)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 624, in _convert_frame
    result = inner_convert(frame, cache_size, hooks, frame_state)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 132, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 370, in _convert_frame_assert
    return _compile(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 554, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 180, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 465, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/bytecode_transformation.py", line 1028, in transform_code_object
    transformations(instructions, code_options)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 432, in transform
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2071, in run
    super().run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2159, in RETURN_VALUE
    self.output.compile_subgraph(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 836, in compile_subgraph
    self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 936, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 180, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 992, in call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 988, in call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/repro/after_dynamo.py", line 117, in debug_wrapper
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/usr/local/lib/python3.10/dist-packages/torch/__init__.py", line 1586, in __call__
    return self.compiler_fn(model_, inputs_, **self.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/backend/backends.py", line 36, in torch_tensorrt_backend
    compiled_mod: torch.nn.Module = DEFAULT_BACKEND(gm, sample_inputs, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/backend/backends.py", line 55, in aot_torch_tensorrt_aten_backend
    return aot_module_simplified(
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 3795, in aot_module_simplified
    compiled_fn = create_aot_dispatcher_function(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 180, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 3333, in create_aot_dispatcher_function
    compiled_fn = compiler_fn(flat_fn, fake_flat_args, aot_config, fw_metadata=fw_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 2120, in aot_wrapper_dedupe
    return compiler_fn(flat_fn, leaf_flat_args, aot_config, fw_metadata=fw_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 2300, in aot_wrapper_synthetic_base
    return compiler_fn(flat_fn, flat_args, aot_config, fw_metadata=fw_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 1574, in aot_dispatch_base
    compiled_fw = compiler(fw_module, flat_args)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 1492, in f
    out_f = compiler(fx_g, inps)
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/backend/backends.py", line 80, in _pretraced_backend
    trt_compiled = compile_module(
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/compile.py", line 220, in compile_module
    trt_mod = convert_module(
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/conversion.py", line 40, in convert_module
    Input.from_tensors(inputs, disable_memory_format_check=True),
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 376, in from_tensors
    return [
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 377, in <listcomp>
    cls.from_tensor(t, disable_memory_format_check=disable_memory_format_check)
  File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
    return cls(shape=t.shape, dtype=t.dtype, format=frmt)
torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
AttributeError: 'SymInt' object has no attribute 'shape'

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True

If I add  torch._dynamo.config.suppress_errors = True, it will show the folloowing message:
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 772
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:30:11,458] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 614
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:04,803] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT _prepare_decoder_attention_mask /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 591
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:05,499] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT _make_causal_mask /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 43
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:05,658] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT _expand_mask /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 61
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:05,856] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

INFO:torch_tensorrt.dynamo.conversion._TRTInterpreter:TRT INetwork construction elapsed time: 0:00:00.019075
INFO:torch_tensorrt.dynamo.conversion._TRTInterpreter:Build TRT engine elapsed time: 0:00:09.090515
INFO:torch_tensorrt.dynamo.conversion._TRTInterpreter:TRT Engine uses: 360710144 bytes of Memory
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 396
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/conversion.py", line 37, in <listcomp>
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING]     output_dtypes = [output.dtype for output in module_outputs]
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING] AttributeError: 'int' object has no attribute 'dtype'
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:23,793] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 292
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/dynamo/conversion/conversion.py", line 37, in <listcomp>
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING]     output_dtypes = [output.dtype for output in module_outputs]
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'dtype'
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:25,133] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:6 supported operations detected in subgraph containing 6 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT apply_rotary_pos_emb /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 180
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:26,538] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:5 supported operations detected in subgraph containing 6 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:0 supported operations detected in subgraph containing 0 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 202
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING]
[2023-10-02 01:31:27,967] torch._dynamo.convert_frame: [WARNING]

I'd like to know which specific line of code causes this problem, and what the error message means. This warning AttributeError: 'SymInt' object has no attribute 'shape' was present throughout the compilation process and most forward functions are avoided as the result, which really compromises the performance gain from compilation. It seems this error has something to do with the dynamic shape and possible represent a symbolic variable but I'm not sure of the specifics.

Hi @BDHU - thanks for the report. Our torch.compile backend currently does not support dynamic shape compilation, so the dynamic=True specification is likely causing the issue here. If you specify dynamic=False, the code should proceed as expected. We are actively working on solutions for decoder models which will make them functional with static shape, and we plan on having full dynamic shape support in the future.

Thank you for your response. After I disable dynamic shape with dynamic=False, I get some error messages:
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 772
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/fx/utils.py", line 104, in unified_dtype_converter
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING]     raise TypeError("%s is not a supported dtype" % dtype)
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] TypeError: torch.int64 is not a supported dtype
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] While executing %arg357_1 : [num_users=1] = placeholder[target=arg357_1]
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] Original traceback:
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] None
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:28:57,269] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 614
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/fx/utils.py", line 104, in unified_dtype_converter
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING]     raise TypeError("%s is not a supported dtype" % dtype)
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] TypeError: torch.int64 is not a supported dtype
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] While executing %arg356_1 : [num_users=1] = placeholder[target=arg356_1]
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] Original traceback:
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] None
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:24,088] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT _prepare_decoder_attention_mask /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 591
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/fx/utils.py", line 104, in unified_dtype_converter
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING]     raise TypeError("%s is not a supported dtype" % dtype)
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] TypeError: torch.int64 is not a supported dtype
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] While executing %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] Original traceback:
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] None
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:30,932] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

WARNING:torch_tensorrt.dynamo.compile:5 supported operations detected in subgraph containing 7 computational nodes. Skipping this subgraph, since min_block_size was detected to be 7
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT _expand_mask /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 61
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/fx/utils.py", line 104, in unified_dtype_converter
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING]     raise TypeError("%s is not a supported dtype" % dtype)
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] TypeError: torch.int64 is not a supported dtype
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] While executing %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] Original traceback:
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] None
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 02:29:31,040] torch._dynamo.convert_frame: [WARNING]
INFO:torch_tensorrt.dynamo.utils:Using Default Torch-TRT Runtime (as requested by user)
INFO:torch_tensorrt.dynamo.utils:Compilation Settings: CompilationSettings(precision=torch.float32, debug=False, workspace_size=21474836480, min_block_size=7, torch_executed_ops={}, pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_long_and_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False)

It seems torch.int64 is not supported. Is it possible to simply convert all torch.int64 to torch.int32?
In addition, I noticed this error message again:
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py line 292
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING] due to:
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING]   File "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/_Input.py", line 357, in from_tensor
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING]     return cls(shape=t.shape, dtype=t.dtype, format=frmt)
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='torch_tensorrt' raised:
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING] AttributeError: 'SymInt' object has no attribute 'shape'
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING]
[2023-10-03 03:23:13,387] torch._dynamo.convert_frame: [WARNING]

I don't understand why attention layer still has unknown shape in this case. In order to possible mitigate this, how do I find which specific line of code causes errors like these?

@BDHU - I see, yes there is a compilation argument, truncate_long_and_double, which will handle the casting of int64 to int32 directly. You would just specify "truncate_long_and_double": True as a kwarg. It is strange that the SymInt objects are still appearing in the trace with dynamic=False.
The line which causes the error in the model source code is as listed in modeling_llama.py. To see the line in Torch-TRT, pass the compilation argument debug=True, which will print more verbose logging of the issue.


Update the hash for the new version of anydesk

Anydesk update to version 7.0.14
This is the new hash of the exe file

Thanks for the fix.
However the hash was fixed by the bot during version update, so this PR can be closed.


Initial implementation of differential fuzzing

What was wrong?

Needs fuzzing of contracts.

How was it fixed?
It consists of the fuzzing setup for the erc20.fe and erc20.sol and has the rearrangements for the compiler tests as an independent directory to avoid circular dependency and it will also decrease the test execution time as it doesn't require rebuilding the dependencies again.
To-Do

[x] Need to add fuzzing for ERC20.sol.

[ ] Add support for output gathering to differentiate outputs of the contract.

[ ] Add support for the gas analyzing for the contracts

[ ] OPTIONAL: Update Spec if applicable

[ ] Add entry to the release notes (may forgo for trivial changes)

[ ] Clean up commit history

Codecov Report

Merging #385 (417ac5b) into master (df7c1d6) will decrease coverage by 0.12%.
The diff coverage is 87.50%.

@@            Coverage Diff             @@
##           master     #385      +/-   ##
==========================================
- Coverage   86.07%   85.95%   -0.13%
==========================================
  Files          67       68       +1
  Lines        4008     4285     +277
==========================================
+ Hits         3450     3683     +233
- Misses        558      602      +44

Impacted Files
Coverage Δ

test-utils/src/lib.rs
84.83% <87.50%> (ø)

compiler/src/lowering/names.rs
66.66% <0.00%> (-6.67%)
:arrow_down:

Continue to review full report at Codecov.

Legend - Click here to learn more
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@satyamakgec Could you rebase your changes onto the master branch? It's hard to review the changes

Whoops, misclicked! This laptop has a touchscreen, and there was some dirt over the 'close' button

@satyamakgec Trying to run the fuzzer fails with
error[E0433]: failed to resolve: could not find `erc20` in `fe_compiler_fuzz`
 --> fuzz_targets/erc20_target.rs:5:23
  |
5 |     fe_compiler_fuzz::erc20::fuzz_fe_erc20_constructor(data);
  |                       ^^^^^ could not find `erc20` in `fe_compiler_fuzz`

Same results with either cd fuzz && cargo build or cargo +nightly fuzz erc20_target. I'm not sure what's going on here. erc20 is clearly exposed by the lib, but maybe it's a simple issue with the file layout or cargo.toml that I'm not noticing. Ideas? Does this build for you? Should I be doing something else?

ohh sorry @sbillig I should have mention this in the readme. It only works with the feature flag i.e solc-backend so the right command to run it cargo +nightly fuzz run erc20_target --features solc-backend. And yes right now it is a mess up in the commits of the PR, I am going to re-open a clean new PR with these changes so it will be very easy to look over to the changes.

Closing in the favour of #421


Credit Card input field doesn't play nicely with Chrome's credit card auto fill feature

Chrome has a secure way to store credit card info and auto-fill it. The input field (under /routes/credit-card) doesn't seem to play nicely with it. When I start typing in the field, I expect Chrome to suggest saved credit cards. In the case of Gratipay, the suggestion doesn't happen unless I type a little and then hit backspace.

Interesting. Okay, thanks for the report, @JVimes.

Just FYI, I ran into this a second time today (previous credit card cancelled). I know it's not the highest priority, but should be an easy client side fix ☺

https://developers.google.com/web/updates/2015/06/checkout-faster-with-autofill for future reference.

Sorry @JVimes. 😕  Do you happen to know what the fix is off-hand?
@mattbk Can you see what the fix is from the link there?

@whit537, @mattbk I found two things that seem to work, at least in devtools:

Removing autocomplete="off" from the form. Oddly, this doesn't work on the original form, but does work if I copy/paste the form so there are now two forms, and I edit the new copy.
Disabling JavaScript fixes it without editing HTML or messing with autocomplete.

Thanks for digging in, @JVimes. I can't play with this until tonight at the earliest.

Maybe someone from 24 Pull Requests could pick this one off?
https://twitter.com/Gratipay/status/806918094199230464

Locally I'm not secure!


Corrections made

FatKeyframe_t FatKeyframe_t(in Keyframe_t keyframe);
Why is this a constructor having a return type?

can you keep all code in this PR please?
https://github.com/Devsh-Graphics-Programming/Nabla/pull/429


Security update: ensure nodemon^1.18.7

A critical security issue with flatmap-stream was traced to a sub-debpendency of nodemon. This is fixed in<EMAIL_ADDRESS>I realize that nodemon is only a devDependency that is installed when you feathers generate app so it shouldn't affect production apps, and that since no version is currently specified it should get the latest version (which is fixed). However, I just updated my global @feathersjs/cli and ran feathers generate app and it installed<EMAIL_ADDRESS>which still contained the vulnerability.
For the time being, it seems wise to specify<EMAIL_ADDRESS>in the app generator just to make sure people don't inadvertently get the malicious code on their systems. This can probably be removed in the near future, but I don't think it could hurt to make this the minimum version, for now.

I've been thinking about doing that, thank you. Too bad that npm seems to be so flaky when it comes to actually pulling in the latest version.


[SPARK-13601] [TESTS] use 1 partition in tests to avoid race conditions

What changes were proposed in this pull request?
Fix race conditions when cleanup files.
How was this patch tested?
Existing tests.

cc @zsxwing

Test build #52429 has started for PR 11507 at commit 499bb8f.

LGTM. Tested 20 times locally and all passed.

Thanks, I'm going to merge this to hotfix master (since we only touch the flaky tests here).

Test build #52429 has finished for PR 11507 at commit 499bb8f.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/52429/
Test PASSed.

Merged build finished. Test PASSed.


Unix Socket Permissions - Nginx Unable to Connect

When using Unix Sockets, is it possible to change the permissions of the created .sock file so that nginx has the correct access rights.
I am using the following code in Program.cs to start the server:
public static void Main(string[] args)
{
    var host = new WebHostBuilder()
        .UseKestrel()
        .UseContentRoot(Directory.GetCurrentDirectory())
        .UseUrls("http://unix:/run/kestrel-test.sock")
        .UseIISIntegration()
        .UseStartup<Startup>()
        .Build();

    host.Run();
}

The file is created at  /run/kestrel-test.sock. The problem is nginx does not have permission to write to the file unless it is modified with the following command:
sudo chmod 666 /run/kestrel-test.sock

Is there a way for kestrel to create the file with 666 permissions or am I doing something wrong?
Thanks

One option is to run Kestrel and nginx as the same user.  The nginx user is set via the user directive in nginx.conf: http://nginx.org/en/docs/ngx_core_module.html#user.  This way the file permissions work correctly with no changes.
If you run Kestrel and nginx as different users, you'll need to modify the file permissions on your own.

Thanks Mike. That worked.
I changed the nginx user from www-data to the local user I login to to the server with. Would this be secure though? Or would I need to leave nginx running with www-data and make Kestrel run with www-data?
I also had to change the directory the socket was created in from /run to /tmp because the local user running Kestrel did not have permission to write to the /run folder.

We recommend running Kestrel as www-data.  See https://docs.asp.net/en/latest/publishing/linuxproduction.html.

When I run Kestrel as www-data I get the following error message:
Access to the path '/home/viewmodel/cli-samples/HelloMvc/obj/Debug/netcoreapp1.0/.SDKVersion' is denied.
I assume this is because www-data does not have any permissions to access the folder. What would the recommended permissions be and on what folders.
Thanks

Turns out it was the -H flag changing the home directory. I removed this and now it works.


Allow board members to target themselves

Expected behavior
“I should be able to target myself if that is who I am editing”
Actual current behavior
The board_member option is optional because the command implicitly assumes that the user is editing themselves. The option is intended to be used by admins because only admins should be able to target someone other than themselves.
Relevant code location
Resolution
Add a simple check to not throw an error if the targeted board member is the user of the invoked slash command.
Screenshot

Clarified by Diamond:

[9:06 AM]Diamond (she/they): the same error happens without that option
[9:06 AM]Diamond (she/they): I think if this is the intended behavior, clarification in the description would be nice
[9:06 AM]Diamond (she/they): "Use the targeted board member instead of yourself"


Add "allow_pickle=True"

This makes it compatible with the latest numpy, otherwise, there would be an Object arrays cannot be loaded when allow_pickle=False error.

Thanks @lucifer1004


[stable/k8s-spot-termination-handler] Add priorityClass support

What this PR does / why we need it:
This PR adds the possibility of specifying a priorityClassName for the pod.
With this in place we can guarantee that the DS will be the one of the first to be scheduled , and guarantee that will be always have resources for it .

/assign

/ok-to-test

/lgtm


1.2 -> 1.3 differences page problems

Just above where the URL below links too, the documentation goes all funky and is impossible to interpret
URL for relevant page?
https://docs.influxdata.com/influxdb/v1.3/administration/differences/#tagkey
What products and version are you using?
Chrome Version 59.0.3071.115
Where did you look before opening the issue?
I searched the issues for differences to see if this had been opened already?

It seems to render fine in Githubs markdown parser: https://github.com/influxdata/docs.influxdata.com/blob/master/content/influxdb/v1.3/administration/differences.md
but somewhere around line 130 causes the influx website to not parse the markdown in the same way.

I'm also using Chrome 59.0.3071.115 ...but I see what you mean.  I'll attempt to clean this up.

@Ultimation -- I believe I've slayed the markdown demons!  Have a look!


Make it possible to see overlapping jobs (WithoutOverlapping middleware)

When using the WithoutOverlapping middleware it would be nice to react on the fact when no lock could be acquired and also no releaseAfter is set (https://github.com/laravel/framework/blob/9.x/src/Illuminate/Queue/Middleware/WithoutOverlapping.php#L77)
This could be helpful to generate a log message. Is this somehow possible or how could this be done (add event...)? I could also provide a pull request if i know how this should be done.

Heya, thanks for submitting this.
This seems like a feature request or an improvement so I'm converting this to a discussion instead. It's best to post these in the discussions section in the future to get support for your idea. After that you may send a PR to the framework. Please only use the laravel/framework issue tracker to report bugs and issues with the framework.
Thanks!


FastAPI is not using enough threads

First Check

[X] I added a very descriptive title to this issue.
[X] I used the GitHub search to find a similar issue and didn't find it.
[X] I searched the FastAPI documentation, with the integrated search.
[X] I already searched in Google "How to X in FastAPI" and didn't find any information.
[X] I already read and followed all the tutorial in the docs and didn't find an answer.
[X] I already checked if it is not related to FastAPI but to Pydantic.
[X] I already checked if it is not related to FastAPI but to Swagger UI.
[X] I already checked if it is not related to FastAPI but to ReDoc.

Commit to Help

[X] I commit to help with one of those options 👆

Example Code
..

Description
When I send too many requests to my fastapi application it spends most of the time doing nothing which I think is because of threading switching and there being not enough threads in the pool. However, the CPU usage is nowhere near 100% and the memory is less than 10% I am wondering how can I force fastapi to use more threads ? I could not find any resource to set the thread to a higher number in the latest version and what is the default number of threads ?
Operating System
Linux
Operating System Details
No response
FastAPI Version
0.73
Python Version
3.9
Additional Context
No response

Maybe you should add more workers?

Maybe you should add more workers?

I mean thats one approach but I want to use more threads. Is there a way to specify the max threads ?

How do you run fastapi, with uvicorn , gunicorn ?

I too have this issue
I'm running fastapi with gunicron with only 1 worker, and looking at the profiler I see that most of time is in the await part

You need to retrieve the CapacityLimiter using current_default_thread_limiter() and change the number of total_tokens (attribute).

You need to retrieve the default CapacityLimiter using current_default_thread_limiter() and change the number of total_tokens (attribute).
Default is 40.

I believe I am using an older version that doesn't use AnyIO but can I ask where you found that the default is 40 ? Would be great if you can paste a link or someting

Just in case...
import anyio
from fastapi import FastAPI

app = FastAPI()

@app.on_event("startup")
async def startup():
    limiter = anyio.to_thread.current_default_thread_limiter()
    limiter.total_tokens = 100

@app.get("/")
async def home():
    print(anyio.to_thread.current_default_thread_limiter().total_tokens)  # you'll see 100


[Feature request] Exit code on failure

Feature Request
I would like to propose a feature to get the exit code of the chief pod via TFJob.
I know that the TFJob already takes care of whether-to-restart based on restartPolicy and the exit code internally.
Can we expose that information via TFJob? Currently we want to handle the restart externally and it seems it is hard to get that information via TFJob CRD.

@tonykim-moloco what info do you need via TFJob ?

TFJob currently only exposes a wrapped information - Succeeded | Created | Running | Failed
However, in failure cases, I would like to know the exit code about the pod to understand about why it failed.
For example, if a worker pod crashed due to OOM (exit code 137), I would like to look up TFJob to see that exit code was 137 on one of the workers.

Probably, we can support this request once we introduce batch/Job.
#1718


Benchmark by memory, cache, network, concurrency, language, silicon factors

Benchmarks bound by for example on chip cache
https://github.com/ObrienlabsDev/blog/issues/91
see work (newer to older)
current generic repo - https://github.com/ObrienlabsDev/performance
https://github.com/ObrienlabsDev/cuda
https://github.com/ObrienlabsDev/collatz
(above extends below)
https://github.com/obrienlabs/benchmark
https://github.com/obrienlabs/collatz/blob/master/collatz-nbi/src/main/java/cloud/obrienlabs/collatz/process/ComputeImpl.java
parallax microcontroller machine language reference
https://github.com/obrienlabs/electronics/blob/master/propCollatz_13_20120317h.spin#L244
Criteria

Java BigInteger is 10x slower than using native long (but tops out at 63 bits)

VS 2022 optimizations
https://learn.microsoft.com/en-us/cpp/build/reference/o-options-optimize-code?view=msvc-170


feat(protocol-designer): add ux analytics with opt in settings and modal

overview

Add fullstory integration to Protocol Designer. We now ask users if they would like to participate
in analytics with a modal, and persist their decision in local storage. They are always able to
change their selection in the new settings tab.
Closes #2119, Closes #2172
changelog

Added Settings tab to main nav, as well as settings page and routes
Because the new PD settings share design with Run-App settings. Added 'control' components from App Settings into shared components, with an aim to have a follow-up PR move the App dependency to use the component library version too.
Integrate Fullstory behind an opt in modal that appears when the main ProtocolEditor component mounts (if there isn't an "opt in value" already stored in localStorage).

review requests
Test out modal launching and value being saved between sessions:
Do this by launching the app. If you respond "yes" to the opt-in prompt, you should be able to navigate to the settings page and see that analytics is toggled "on".

Likewise, if you select "no" on the opt-in prompt, you should see the correct setting toggled off and you not see the modal appear after refresh.
If you would like to clear the persistent opt-in value and test the user experience that occurs when you start the app for the first time before opting in or out, open up the developer console [ in Google Chrome Cmd - Alt(option) - j ] and type window.localStorage.removeItem('optedInToAnalytics') and press enter. This will remove the persisted decision and treat you as a new user until you opt in or out again.

@b-cooper
Can the label and toggle be vertically aligned? they're currently off

Also worth noting that the run app has an almost identical component in their More > App page


Video quality is not good for few initial seconds

Be sure to search for your issue before opening a new one.
Current Behavior

Expected Behavior

Steps to Reproduce

Environment

URL attempting to play:
Browser:
Operating system:
jsFiddle example: https://jsfiddle.net/sv5x3ug1

Other Information

React player will render the browser's native video player when using progressively downloaded mp4's so there is nothing React player can do about the quality. I recommend using video streaming like hls.js or Dash to get better quality video.


re-integrate the Tutorials using public Galaxy at usegalaxy.org tutorials

@nekrut can we re-integrate the Tutorials using public Galaxy at usegalaxy.org into other sections. The annotation which training can be run with which Galaxy instance should happen as Metadata.

@nekrut what do you think about re-integrating the tutorials of usegalaxy in other sections?

In will work on this today.


Adjust the semantics of max_output_len to be consistent with TRT-LLM

Ensure that the saved engine config.json norm_epsilon value is consistent with hf config.json, and adjust the semantics of max_output_len to be consistent with TRT-LLM.

test ok!


ENH Do not output core code deprecation messages by default

Issue https://github.com/silverstripe/silverstripe-framework/issues/11428

Bunch of stuff above still not done?


7.1.1 Add Jacoco and SonarQube

Configure Jacoco and SonarQube for better testing analyze of the project

Only added Jacoco


Please clarify whether VOLT will convert legacy encoded text into Unicode when new font file is created with correct Unicode points

We have a corpus of Devanagari text using a legacy font (APS_C_DV_Priyanka.ttf). I have in the process of converting the legacy encoded text to Unicode. Reading through a number of online docs, I still don't get if VOLT has the ability to read legacy encoded text and convert it to Unicode or if it more specifically provides a way to update a legacy font so that it can be used with Unicode encoded text.
I guess the question is essentially: do I still need to find a way to convert the original legacy-encoded Devanagari text to Unicode Devnagari and then run an update VOLT font to view the text so it appears the same or does VOLT offer a way to take the original text and produce Unicode encoded text from the updated rules?
https://docs.microsoft.com/en-us/typography/develop/convert-a-devangarai-font-to-unicode-otl

So, suppose you have a font that was used to print Devanagari in some proprietary encoding. What does it take to convert it to Unicode? Where do I start?

It would be great if the above paragraph were expanded to allay the confusion of those like me and make clear what problem VOLT actually solves at which point it is used to solve it. The problem in my case, and I imagine many of those need to updated legacy fonts is the following:

text was typed out using a legacy (Devanagari) font
the underlying encoding looks like gibberish (‚ã®ãÄãäØã¶ãã) in other programs and text cannot be searched for in a standard way
a script has been created to convert the legacy encoded text to Unicode encoded text so now the orginal text is correctly viewable with Unicode (Devanagari) fonts but not in the original legacy font
some tool is used to update the legacy font to the correct Unicode points so that the updated Unicode encoding can now be viewed to look visually the same as the font from the original in step 1

Does VOLT fall into step 4 or can it be used as a tool in step 3 as well?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 1eaef1fe-85fe-38d2-53c6-0e3cf5e183d1
Version Independent ID: a3f17066-d403-2fd2-d90e-2b4d5234fa35
Content: Convert a Devanagari font to Unicode / OTL - Typography
Content Source: typographydocs/develop/convert-a-devangarai-font-to-unicode-otl.md
Product: typography
GitHub Login: @alib-ms
Microsoft Alias: alib

VOLT does not do any encoding conversion.
VOLT can read the cmap table of a font opened in VOLT, provides the ability to change or add Unicode encodings in the VOLT glyph data editing window, and can write a new cmap table (you can specify which platform encodings and formats it should write).
It is possible to manually edit the Unicode mapping for each individual glyph in the VOLT glyph data editing window, but that is very slow and prone to errors. My recommendation is to do the encoding conversion in another, more general font editing tool such as FontLab or Glyphs, or via a cmap table dump/fuse script with TTX. You can probably use the script that you have for text conversion as a basis for glyph encoding conversion in the font, or make a simple name mapping chart.
I use FontLab for this sort of thing, and the easiest method is probably to create a .nam file which is a list mapping Unicode characters to glyph names (I am presuming you want to keep the glyph names in your font; if not, you will also want a .ren file that maps from old names to new names). You can use the .nam file within FontLab to generate new Unicode encodings based on the glyph names. You then export a TTF from FontLab and open it in VOLT.

Feel free to reopen if you have further questions.

@tiroj

VOLT does not do any encoding conversion.

Thanks for the above and for the suggested process with the alternate softwares you provided. I'll look into those.
@alib-ms

Converting previously created Devanagari text using non-Unicode encoded fonts to Unicode encoded text is outside the scope of VOLT.

Got it, thank you.

You can read more about VOLT here
Visual OpenType Layout Tool - Typography | Microsoft Docs

I think I read every post about VOLT there is and was still unclear :). I'll provide some page feedback for a few lines that may be considered for adding to clarify for future searchers like me. The use of "substitution" in a larger workflow that involves lots of substitutions is where I got tripped up. I had no font internals experience until reading up on the topic recently.


Opens new buffer with some random name after running tests

I'm using Vim (gvim) v7.4.1689. with gvim as vim command in the plugin configuration.
Whenever I execute :make run or :make test it runs all the test cases fine and creates a new buffer with the some name like ok 1 - "0.

and when I press enter to get back to my program code buffer, it takes me to the newly created buffer which is blank.

The problem is that Vim is interpreting the test program output as if it were compiler output. It does this because it is trying to populate the quickfix list.
To work around this, you could use :!make run instead of :make run (notice the extra bang in there). This will run make without causing Vim to try to parse its output.
Alternatively, you could fiddle around with your errorformat setting and try to get it to ignore lines from the test program. Something like this could work:
:set errorformat^=%Enot\ ok\ %l\ %m,%-Gok\ %l\ %m,%-G#%m,%-GTAP\ version\ %*\\d,%-G%*\\d..%*\\d,%-Gmake:\ %m,

This just marks lines that start with "not ok" as errors and ignores some other patterns. See :help errormarker for more info.

The command :!make run works nicely. I'll look out for errorFormat setting as well.
Thank you!


IP ban working in Neko standalone but not with Neko-Rooms

Hi. the IP ban function works fine when running Neko standalone installed without Neko-Rooms, but in a room launched by Neko-Rooms, an IP ban on one user bans everyone and then no one can enter the room. It's as if it's not detecting individual IP addresses and the ban happens on an address that everyone uses.
It's not a server issue because I've tried it on both Linode VPS and Vultr making sure I'm not behind any NodeBalance or reverse proxy and I checked with Linode techs and they figure it should be a config issue with Neko, and as stated it works fine with Neko standalone so it should be something in the Neko-Rooms installation script or some other Neko-Rooms config setting that I need to check to get it to work with Neko-Rooms.
Any help is much appreciated. Thanks in advance.

Hi, are you using neko-rooms with or without traefik? When you mention Neko-Rooms installation script then i assume you are using it with traefik.

I added NEKO_PROXY to neko rooms. Please pull latest code, latest images and restart traefik with new config, then recreate rooms. It should now see the real IP.

I added NEKO_PROXY to neko rooms. Please pull latest code, latest images and restart traefik with new config, then recreate rooms. It should now see the real IP.

Thank you! That fixed it and it works perfectly well. Much appreciated.


Unable to use custom_check if it requires Quartz

Agent status
===============
Agent (v6.22.0)
===============

  Status date: 2020-12-02 19:21:24.813952 GMT
  Agent start: 2020-12-02 19:21:20.023927 GMT
  Pid: 39330
  Go Version: go1.13.11
  Python Version: 3.8.5
  Build arch: amd64
  Agent flavor: agent
  Check Runners: 6
  Log Level: info

Output of the info page (if this is a bug)
{
	"StandardOutPath" = "/var/log/datadog/launchd.log";
	"LimitLoadToSessionType" = "Aqua";
	"StandardErrorPath" = "/var/log/datadog/launchd.log";
	"Label" = "com.datadoghq.agent";
	"OnDemand" = true;
	"LastExitStatus" = 19968;
	"Program" = "/opt/datadog-agent/bin/agent/agent";
	"ProgramArguments" = (
		"/opt/datadog-agent/bin/agent/agent";
		"run";
	);
};

Describe what happened:
I have created a custom check that needs pyobjc-framework-Quartz python module. I installed it with this command /opt/datadog-agent/embedded/bin/pip3 install pyobjc-framework-Quartz. After I tested with the following command: datadog-agent check sleep_checker it has been terminated with the following stacktrace:
* Python Check Loader: unable to import module 'sleep_checker': Traceback (most recent call last):
  File "/opt/datadog-agent/etc/checks.d/sleep_checker.py", line 8, in <module>
    import Quartz
  File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/Quartz/__init__.py", line 6, in <module>
    import AppKit
  File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/AppKit/__init__.py", line 10, in <module>
    import Foundation
  File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/Foundation/__init__.py", line 9, in <module>
    import CoreFoundation
  File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/CoreFoundation/__init__.py", line 9, in <module>
    import objc
  File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/objc/__init__.py", line 6, in <module>
    from . import _objc
ImportError: dlopen(/opt/datadog-agent/embedded/lib/python3.8/site-packages/objc/_objc.cpython-38-darwin.so, 2): no suitable image found.  Did find:
	/opt/datadog-agent/embedded/lib/python3.8/site-packages/objc/_objc.cpython-38-darwin.so: code signature in (/opt/datadog-agent/embedded/lib/python3.8/site-packages/objc/_objc.cpython-38-darwin.so) not valid for use in process using Library Validation: mapped file has no cdhash, completely unsigned? Code has to be at least ad-hoc signed.

Describe what you expected:
Check executed without error.
Steps to reproduce the issue:
Put the following script under /opt/datadog-agent/etc/checks.d/:
try:
    # first, try to import the base class from new versions of the Agent...
    from datadog_checks.base import AgentCheck
except ImportError:
    # ...if the above failed, the check is running in Agent version < 6.6.0
    from checks import AgentCheck

import Quartz

__version__ = "1.0.0"

class SleepChecker(AgentCheck):
    def check(self, instance):
        return

Put the config file under /opt/datadog-agent/etc/conf.d/ with the following content:
init_config:

instances:
  - min_collection_interval: 15

Additional environment details (Operating System, Cloud provider, etc):
The agent is running on MacOS. system_profiler output:
Software:

    System Software Overview:

      System Version: macOS 10.15.7 (19H2)
      Kernel Version: Darwin 19.6.0
      Boot Volume: Macintosh HD
      Boot Mode: Normal
      Computer Name: administrator’s Mac mini
      User Name: administrator (administrator)
      Secure Virtual Memory: Enabled
      System Integrity Protection: Enabled
      Time since boot: 42 days 57 minutes

When I try the import Quartz from a simple script on the same host with the installed python it works as expected.
Can you guide me what could be the source of the problem and how can I solve it?

Can I get some help here?


[IMPROVEMENT] fix: link to documentation website in README

What changes were proposed in this pull request?

Link to documentation website

Why are the changes needed?

Link to documentation website instead of documentation directory for easier access

Fix: #5109
Does this PR introduce any user-facing change?

No user-facing changes

How was this patch tested?

Link to website shows in README upon updating text

@JunpingDu please let me know if I missed anything


moment.js supports typescript

Adding the moment.js script directly in the html page is probably an unusual way for Angular2 developers using typescript. Additionally, Moment.js supports typescript (http://momentjs.com/docs/#/use-it/typescript/) so it would be probably easy to support that in this module

I ran out of idea now. Could you make a PR for this? I would appreciate if you do

Tried moment with typescript
However, IMO, momentjs is not ready for Typescript and webpack.
I will close this until someone successfully use momentjs in typescrpt and webpack.


abbreviation problem

what is sol？(like sol-renderer)
Are there any notes on abbreviations?

SOL is short for Speed of Light and it's an internal abbreviation used by NVIDIA. IIRC, it's essentially the mindset of learning, acting and moving super fast with extreme performance, something NVIDIA strives for. In our case, sol-renderer was the final form of our real-time renderer before we released the paper.


Update progress of Currently Reading book when updated on Goodreads

Best way I can think of right now is to just set a couple more fields in currently-reading.yml. One for total pages, one for current page. Update manually.

Goodreads API is bad, really really bad. I'd like to be able to hit the API with a static site such as this, but I'm not seeing or finding a good way to do it.


Docs: "Install Compose on Linux systems" - broken link

File: compose/install.md
https://docs.docker.com/compose/install/#install-compose-on-linux-systems
1. Run this command to download the current stable release of Docker Compose:
   sudo curl -L "https://github.com/docker/compose/releases/download/v1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

is getting an 'v' in the v1.29.2. It should be:
sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

/v1.29.2/ (wrong)
/1.29.2/ (right)
Does {{site.compose_version}} include the 'v' ?
sudo curl -L "https://github.com/docker/compose/releases/download/{{site.compose_version}}/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

Duplicate


ci: Allow snapshot creation for release-v0.33.x

Fixes #N/A
Description

Allow snapshot creation for release branches to allow running the E2E tests

How was this change tested?

N/A

Does this change impact docs?

[ ] Yes, PR includes docs updates
[ ] Yes, issue opened: #
[x] No

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 82.433%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
4913

Relevant Lines:
5960

💛  - Coveralls


Typing a slash character in the "Search npm packages" input switches focus to the "Search within library" input

I noticed this issue when I was viewing the docs for the xstate package and tried to search using the top input for the @xstate/react package. The slash switches focus to the other input before you can finish typing.
Cool project btw!

Thanks for the report, should be fixed soon.


Access token expiry utc time is earlier than current utc time?

Does this occur consistently?
Repro steps:

Action: appService.Deploy
Error type: ExpiredAuthenticationToken
Error Message: The access redacted:key UTC time '5/14/redacted:id redacted:id PM' is earlier than current UTC time '5/14/redacted:id redacted:id PM'.
Version: 0.24.8
OS: win32
OS Release: 10.0.22621
Product: Visual Studio Code
Product Version: 1.78.2
Language: en

Call Stack
handleErrorResponse extension.bundle.js:2:1607519
deserializeResponseBody extension.bundle.js:2:1608250

Duplicate of #2464, please follow that issue for future updates.


Inference

Hello,  thanks for this project. I was able to correctly train a structshot using the train script. Could you show how to correctly run the inference for an input sequence?
In my understanding, the loading would look like
import os
import torch

from fewnerd.util.word_encoder import BERTWordEncoder
from fewnerd.model.proto import Proto
from fewnerd.model.nnshot import NNShot

# cache dir
cache_dir = os.getenv("cache_dir", "../../models")
model_path = 'structshot-inter-5-5-seed0.pth.tar'
model_name = 'structshot'
pretrain_ckpt = 'bert-base-uncased'
max_length = 100

# BERT word encoder
word_encoder = BERTWordEncoder(
        pretrain_ckpt,
        max_length)

if model_name == 'proto':
    # use dot instead of L2 distance for proto
    model = Proto(word_encoder, dot=True)
elif model_name == 'nnshot':
    model = NNShot(word_encoder, dot=False)
elif model_name == 'structshot':
    model = NNShot(word_encoder, dot=False)

model.load_state_dict(torch.load(os.path.join(cache_dir, model_path)))

but I get some errors on the state dicts (RuntimeError: Error(s) in loading state_dict for NNShot:) in this way...
Thank you in advance!

to load the model properly, you can refer to
https://github.com/thunlp/Few-NERD/blob/84c0b59fc0f8a8b265d025c6da2ebc139fdd2057/util/framework.py#L353

@loretoparisi  are you able to do inferencing on the trained model?

@loretoparisi  are you able to do inferencing on the trained model?

thanks for the question, it was time ago, but I think so with some trick, I have to check the notebook for the code...

@loretoparisi  are you able to do inferencing on the trained model?

thanks for the question, it was time ago, but I think so with some trick, I have to check the notebook for the code...

Ok. Thanks. Could you please attach your notebook code and data please.  @loretoparisi

@cyl628 For a single input sequence, how should we write the inference script?

@loretoparisi  are you able to do inferencing on the trained model?

thanks for the question, it was time ago, but I think so with some trick, I have to check the notebook for the code...

Ok. Thanks. Could you please attach your notebook code and data please.  @loretoparisi

I have something here I will add the inference code there!

@loretoparisi  are you able to do inferencing on the trained model?

thanks for the question, it was time ago, but I think so with some trick, I have to check the notebook for the code...

Ok. Thanks. Could you please attach your notebook code and data please.  @loretoparisi

I have something here I will add the inference code there!

Please add the inference code and how the output looks like for singe input.

@loretoparisi  are you able to do inferencing on the trained model?

thanks for the question, it was time ago, but I think so with some trick, I have to check the notebook for the code...

Ok. Thanks. Could you please attach your notebook code and data please.  @loretoparisi

I have something here I will add the inference code there!

@loretoparisi  Have you uploaded the inference script?

@loretoparisi  are you able to do inferencing on the trained model?

thanks for the question, it was time ago, but I think so with some trick, I have to check the notebook for the code...

Ok. Thanks. Could you please attach your notebook code and data please.  @loretoparisi

I have something here I will add the inference code there!

@loretoparisi  Have you uploaded the inference script?

not at this time, I will put it in the repo check updates, thanks.


Empty message in incident / recovery notification

Describe the bug
In the incident / recovery email sent via SMTP and in Slack, the message is empty which makes it confusing what the incident or recovery was.
To Reproduce
Steps to reproduce the behavior:

Use config below.
Run monika -c monika.yml
See the email notification.

# monika.yml
notifications:
  - id: unique-id-smtp
    type: smtp
    data:
      recipients:
        - <someemail>
      hostname: smtp.gmail.com
      port: 587
      username: <gmailusername>
      password:  <gmailpassword>
probes:
  - id: 'monika-localhost-2'
    incidentThreshold: 5
    recoveryThreshold: 5
    requests:
      - url: http://localhost:3000
    alerts:
      - query: response.status < 200 or response.status > 299
      - query: response.time > 2000

Expected behavior
The message should indicates which alert causes the incident and recoveries.
Screenshots

Desktop (please complete the following information):

OS: macOS
Version 1.6.3 and 1.6.2

Notes
Haven't checked in other notification channels, but probably it's the same case?

In the config above, no message is defined for the alerts. You might want to add message to alert like:
    alerts:
      - query: response.status < 200 or response.status > 299
        message: Response is not OK
      - query: response.time > 2000
        message: Response time too slow

Can you suggest the message format for when the configuration is empty? @nicnocquee


Fail installation on make

Hi
I was trying to install the new release compiling from the source.
It always worked in my machine but in this new release, something fails on the make step.
$ make
[  1%] Built target microtar
[  3%] Built target cacode
[  8%] Built target alp
[  8%] Building CXX object lib/ksw2/CMakeFiles/ksw2.dir/ksw2_extz2_sse.cpp.o
In file included from /home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse4.1.h:27:0,
                 from /home/linuxbrew/stow/tmp/MMseqs2/lib/ksw2/ksw2_extz2_sse.cpp:35:
/home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse.h: In function 'simde__m128 simde_x_mm_abs_ps(simde__m128)':
/home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse.h:926:74: error: '_mm512_abs_ps' was not declared in this scope
     return _mm512_castps512_ps128(_mm512_abs_ps(_mm512_castps128_ps512(a)));
                                                                          ^
In file included from /home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse3.h:30:0,
                 from /home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/ssse3.h:30,
                 from /home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse4.1.h:31,
                 from /home/linuxbrew/stow/tmp/MMseqs2/lib/ksw2/ksw2_extz2_sse.cpp:35:
/home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse2.h: In function 'simde__m128d simde_x_mm_abs_pd(simde__m128d)':
/home/linuxbrew/stow/tmp/MMseqs2/lib/simde/simde/x86/sse2.h:408:74: error: '_mm512_abs_pd' was not declared in this scope
     return _mm512_castpd512_pd128(_mm512_abs_pd(_mm512_castpd128_pd512(a)));
                                                                          ^
lib/ksw2/CMakeFiles/ksw2.dir/build.make:81: recipe for target 'lib/ksw2/CMakeFiles/ksw2.dir/ksw2_extz2_sse.cpp.o' failed
make[2]: *** [lib/ksw2/CMakeFiles/ksw2.dir/ksw2_extz2_sse.cpp.o] Error 1
CMakeFiles/Makefile2:720: recipe for target 'lib/ksw2/CMakeFiles/ksw2.dir/all' failed
make[1]: *** [lib/ksw2/CMakeFiles/ksw2.dir/all] Error 2
Makefile:148: recipe for target 'all' failed
make: *** [all] Error 2

Thanks

MMseqs2 HEAD is currently broken on GCC < 7.4 (https://github.com/simd-everywhere/simde/pull/612).
Please use 113e3212c137d026e297c7540e1fcd039f6812b1 for now.

@milot-mirdita thanks, Then I'll wait for your fix. I already have a working version.
I'd be pleased if you report me when it is ready.

Forgot to update you, df69c26e1c9aaeaa3f5d72fd6e782d02742b2b0c fixed the issue. You can use git head again.


detect and propagate edits on mastodon to bluesky

I can't believe this is a new issue / feature request, but I can't find it here yet. The main reason I love mastodon is I can actually edit the posts. But everyone is on bluesky. Sometimes I make dumb mistakes and want to edit them. Would it be possible to detect the changes, delete the bluesky post and post the new version as a new post?

Definitely the right idea! We've done this since the beginning. Sadly we're still waiting on Bluesky and bsky.app to surface edits visibly to users: #947. It's on their roadmap at least!

Duplicate of #947


Added 'notes' tab to order management for admins.

Related to #242. Really, I think this should close #242. Adding notes field to attendees requires a much more invasive change, since we don't even have a way to view them in the admin right now.
Part of me wants to prioritize this, since we have a request from an event for notes functionality - but they might be looking for notes on attendee, not on order. Not sure.
This also needs to be added to the model table display, but I didn't want to much up the merge of the #409 branch by creating conflicts... Maybe I shouldn't worry about that, though.

Looks cool to me but I'll flip it to Harry B for the python. I agree about putting it in the table, so let's close #242 and make a new issue for showing notes in the table

I'm going to look over this and (probably) merge it anyway, but I have a lot of concerns about attaching increasing amounts of data (between this and the surveys) to Order objects.
I think an Attendee is the fundamental object of dance event management and an Order is an implementation detail that should recede into the background as much as possible. I have trouble thinking of too many circumstances in which attaching data to an order is more useful than attaching data to the attendee instead and I think we invite confusion about where data belongs by putting it in both places (or just on the Order, which I think of as the wrong place for this sort of data).

I agree about this. Problem is that minimum option is typically order?

I mean, not if the order is not part of the feature at all.

What I mean to say is that if I were designing Dancerfly on my own, for instance, all data would be associated with the attendee and the Order would be exclusively a financial record that was associated with certain bought items. There would be no data beyond that associated with the order.

Fair.

This is also to say that I don't think this closes #242, because I think it's way less useful and covers way fewer use cases IMO than 242.

This seems good though and bug free. Merging.

(Also, acknowledging that I did apparently include order notes in the ticket, so this is my personal change of heart that's driving this conversation.)

I am also now in a place where I would also probably have designed it somewhat differently if I were doing it now. We can move in that direction, slowly.


Add phietala to protecode sc plugin contributors

Description
Adding phietala to devs for protecode-sc-plugin:
https://github.com/jenkinsci/protecode-sc-plugin
@mattisii Ping! Please approve

Submitter checklist for adding or changing permissions

Always

[x] Add link to plugin/component Git repository in description above

When adding new uploaders (this includes newly created permissions files)

[x] Make sure to @mention an existing maintainer to confirm the permissions request, if applicable
[x] Use the Jenkins community (LDAP) account name in the YAML file, not the GitHub account name
[x] Make sure to @mention the users being added so their GitHub account names are known if they require GitHub merge access (see below).
[x] All newly added users have logged in to Artifactory at least once

Reviewer checklist (not for requesters!)

[ ] Check this if newly added person also needs to be given merge permission to the GitHub repo (please @ the people/person with their GitHub username in this issue as well). If needed, it can be done using an IRC Bot command
[ ] Check that the $pluginId Developers team has Admin permissions while granting the access.
[ ] In the case of plugin adoption, ensure that the Jenkins Jira default assignee is either removed or changed to the new maintainer.
[ ] If security contacts are changed (this includes add/remove), ping the security officer (currently @daniel-beck) in this pull request. If an email contact is changed, wait for approval from the security officer.

There are IRC Bot commands for it

Yes, approved. Let's check later if the artifactory login issue is gone.


Create codacy-analysis.yml

Dropping this since it seems to be working without the yml now anyway.


SAA-2041: Deallocate sessions running later today

Codecov Report
Attention: Patch coverage is 92.50000% with 3 lines in your changes missing coverage. Please review.

Project coverage is 84.44%. Comparing base (74d6f86) to head (0e5b582).

Files with missing lines
Patch %
Lines

...smanagementapi/service/ManageAttendancesService.kt
81.25%
3 Missing :warning:

Additional details and impacted files
@@             Coverage Diff              @@
##               main    #1051      +/-   ##
============================================
+ Coverage     84.33%   84.44%   +0.10%
- Complexity     3700     3710      +10
============================================
  Files           381      381
  Lines         12414    12436      +22
  Branches       1070     1074       +4
============================================
+ Hits          10469    10501      +32
+ Misses         1461     1451      -10
  Partials        484      484

Flag
Coverage Δ

84.44% <92.50%> (?)

Flags with carried forward coverage won't be shown. Click here to find out more.

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:loudspeaker: Have feedback on the report? Share it here.


Increase threshold for steer override

I've seen this issue on many Toyota's, and also on my Honda Accord.  The steer threshold is too low, which often results in the integral value unwinding unnecessarily.

nice plot. So the blue line wasn't corresponding to actual steer override?

Thanks!  The blue "blocks" just show when the steer_override flag is set.  The hope would be that the flag is only set when the driver actually overrides, but it seems that the thresholds need to be raised enough to reduce false positives.

The yellow and grean lines at the bottom of the plot show the actual steer angle (yellow) and desired angle (green).  If the driver was actually overriding the steering, the actual and desired angles would not be parallel.

Here is a great example from my 2018 Honda Accord today.  There were 2 points in the turn when a steer override was flagged.

But it's likely that the 2nd override was actually me helping the steering.  In either case, I think that the "override" should not cause integration to unwind.  The integration should NOT unwind unless the driver opposes the direction of the integration.

unless the driver opposes the direction of the integration <-- makes sense. This would be a separate and good fix.
So I'm not sure that raising the steer override threshold is the fix we want. Are you sure it false triggers?

I agree that would be a separate PR, and I'm glad you like the idea.  However, it seems that raising the threshold is needed, even for safety reasons.  False positives on steer override probably prevent the driver monitoring from working consistently.  I would guess that it also introduces error in your disengagement statistics.

I need to perform more testing and datalog stock OP, which I can do today. My observations from last night is that when a sudden correction is needed, there's a bit of weird behavior which recovers a few seconds later. It almost feels like there's a "stumble". Changing the value to 200 resolved this behavior -- with in mind all of this testing was on a custom fork.
I will need to replicate/datalog with stock OP 0.6 for sanity. I will do that and report back.
Not too sure as to the full implementation of Steer Override, but I did notice some possible discrepancies when reviewing my Cabana data last night between torque EPS and Driver in some instances (they seem to match each other until after 730 timestamp):

@zorrobyte , can you please send me the route that you used to collect this data?

@rbiasini sure. Be warned, this isn't stock 0.6 and I have a custom 0.005 degree precise angle sensor (didn't modify the factory torque sensors, though)
5e7462b4437e3372|2019-07-03--00-31-37--7
https://github.com/zorrobyte/openpilot/blob/devel_justmysteeringsensor/opendbc/toyota_prius_2017_pt_generated.dbc
I also have a solid EPS motor actuator damper:

You may want to duplicate on comma's Prius for sanity checking.


Quickstart docs missing make bundle-build step in workspace services

The docs at https://microsoft.github.io/AzureTRE/tre-admins/setup-instructions/ui-install-ws-and-ur/ are missing the critical bundle-build step for building the bundles before publishing and registering. Instead of just fixing this, it should be amended to use the new single command for each bundle type:
make workspace_bundle BUNDLE=base
make workspace_service_bundle BUNDLE=guacamole
make shared_service_bundle BUNDLE=sonatype_nexus_vm
make user_resource_bundle WORKSPACE_SERVICE=guacamole BUNDLE=tre-service-guacamole-windowsvm

Also nexus_version needs to be removed from the user resource step as this is no longer in the schema

Some duplication of #2807