[Bug] AWQ Marlin not work with Torch Compile

Checklist

[X] 1. I have searched related issues but cannot get the expected help.
[X] 2. The bug has not been fixed in the latest version.
[X] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug
cc @merrymercy @Ying1123 @liangsheng
server_args=ServerArgs(model_path='casperhansen/llama-3-8b-instruct-awq', tokenizer_path='casperhansen/llama-3-8b-instruct-awq', tokenizer_mode='auto', load_format='auto', dtype='auto', trust_remote_code=False, context_length=None, quantization=None, chat_template=None, host='<IP_ADDRESS>', port=30000, additional_ports=[30001, 30002, 30003, 30004], mem_fraction_static=0.88, max_prefill_tokens=None, max_running_requests=None, max_num_reqs=None, max_total_tokens=None, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=413516221, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key='', file_storage_pth='SGlang_storage', dp_size=1, load_balance_method='round_robin', chunked_prefill_size=None, disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=True, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_disk_cache=False, enable_torch_compile=True, enable_p2p_check=False, attention_reduce_in_fp32=False, efficient_weight_load=False, nccl_init_addr=None, nnodes=1, node_rank=None)
[gpu=0] Init nccl begin.
[gpu=0] Load weight begin. avail mem=78.73 GB
INFO 08-01 10:39:41 awq_marlin.py:77] The model is convertible to awq_marlin during runtime. Using awq_marlin kernel.
INFO 08-01 10:39:43 weight_utils.py:223] Using model weights format ['*.safetensors']
Loading safetensors checkpoint shards:   0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  50% Completed | 1/2 [00:00<00:00,  7.23it/s]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:00<00:00,  2.03it/s]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:00<00:00,  2.27it/s]

[gpu=0] Load weight end. type=LlamaForCausalLM, dtype=torch.float16, avail mem=73.30 GB
[gpu=0] Memory pool end. avail mem=9.27 GB
[gpu=0] Capture cuda graph begin. This can take up to several minutes.
Initialization failed. controller_init_state: Traceback (most recent call last):
  File "/sglang/python/sglang/srt/model_executor/model_runner.py", line 312, in init_cuda_graphs
    self.cuda_graph_runner.capture(batch_size_list)
  File "/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 133, in capture
    ) = self.capture_one_batch_size(bs, forward)
  File "/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 196, in capture_one_batch_size
    run_once()
  File "/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 193, in run_once
    return forward(input_ids, input_metadata.positions, input_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state, skip=1)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 786, in _convert_frame
    result = inner_convert(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
    return _compile(
  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/convert_frame.py", line 676, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
    transformations(instructions, code_options)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 500, in transform
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
    super().run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/lazy.py", line 94, in realize_and_forward
    return getattr(self.realize(), name)(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 335, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/nn_module.py", line 336, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 335, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/nn_module.py", line 336, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 335, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1272, in CALL_FUNCTION_KW
    self.call_function(fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/nn_module.py", line 336, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 335, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/nn_module.py", line 336, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 335, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 335, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1272, in CALL_FUNCTION_KW
    self.call_function(fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1272, in CALL_FUNCTION_KW
    self.call_function(fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 289, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 680, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2285, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2399, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/torch.py", line 679, in call_function
    tensor_variable = wrap_fx_proxy(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builder.py", line 1330, in wrap_fx_proxy
    return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builder.py", line 1415, in wrap_fx_proxy_cls
    example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1714, in get_fake_value
    raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1656, in get_fake_value
    ret_val = wrap_fake_exception(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1190, in wrap_fake_exception
    return fn()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1657, in <lambda>
    lambda: run_node(tx.output, node, args, kwargs, nnmodule)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1782, in run_node
    raise RuntimeError(make_error_message(e)).with_traceback(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1764, in run_node
    return node.target(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_ops.py", line 854, in __call__
    return self_._op(*args, **(kwargs or {}))
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 896, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 1241, in dispatch
    return self._cached_dispatch_impl(func, types, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 974, in _cached_dispatch_impl
    output = self._dispatch_impl(func, types, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 1397, in _dispatch_impl
    r = func.decompose(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_ops.py", line 631, in decompose
    return self._op_dk(dk, *args, **kwargs)
torch._dynamo.exc.TorchRuntimeError: Failed running call_function _C.gptq_marlin_gemm(*(FakeTensor(..., device='cuda:0', size=(1, 4096), dtype=torch.float16), Parameter(FakeTensor(..., device='cuda:0', size=(256, 12288), dtype=torch.int32)), Parameter(FakeTensor(..., device='cuda:0', size=(32, 6144), dtype=torch.float16)), Parameter(FakeTensor(..., device='cuda:0', size=(32, 768), dtype=torch.int32)), Parameter(FakeTensor(..., device='cuda:0', size=(0,), dtype=torch.int32)), Parameter(FakeTensor(..., device='cuda:0', size=(0,), dtype=torch.int32)), FakeTensor(..., device='cuda:0', size=(1536,), dtype=torch.int32), 4, 1, 6144, 4096, True, True), **{}):
The tensor has a non-zero number of elements, but its data is not allocated yet. Caffe2 uses a lazy allocation, so you will need to call mutable_data() orraw_mutable_data() to actually allocate memory.

from user code:
   File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/external_utils.py", line 36, 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 "/sglang/python/sglang/srt/models/llama2.py", line 314, in forward
    hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/sglang/python/sglang/srt/models/llama2.py", line 281, in forward
    hidden_states, residual = layer(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/sglang/python/sglang/srt/models/llama2.py", line 231, in forward
    hidden_states = self.self_attn(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/sglang/python/sglang/srt/models/llama2.py", line 165, in forward
    qkv, _ = self.qkv_proj(hidden_states)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/linear.py", line 330, in forward
    output_parallel = self.quant_method.apply(self, input_, bias)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/quantization/awq_marlin.py", line 257, in apply
    return apply_awq_marlin_linear(
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/quantization/utils/marlin_utils.py", line 286, in apply_awq_marlin_linear
    output = ops.gptq_marlin_gemm(reshaped_x,
  File "/usr/local/lib/python3.10/dist-packages/vllm/_custom_ops.py", line 34, in wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/_custom_ops.py", line 291, in gptq_marlin_gemm
    return torch.ops._C.gptq_marlin_gemm(a, b_q_weight, b_scales, b_zeros,

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

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/sglang/python/sglang/srt/managers/controller_single.py", line 150, in start_controller_process
    controller = ControllerSingle(
  File "/sglang/python/sglang/srt/managers/controller_single.py", line 84, in __init__
    self.tp_server = ModelTpServer(
  File "/sglang/python/sglang/srt/managers/tp_worker.py", line 92, in __init__
    self.model_runner = ModelRunner(
  File "/sglang/python/sglang/srt/model_executor/model_runner.py", line 134, in __init__
    self.init_cuda_graphs()
  File "/sglang/python/sglang/srt/model_executor/model_runner.py", line 314, in init_cuda_graphs
    raise Exception(
Exception: Capture cuda graph failed: Failed running call_function _C.gptq_marlin_gemm(*(FakeTensor(..., device='cuda:0', size=(1, 4096), dtype=torch.float16), Parameter(FakeTensor(..., device='cuda:0', size=(256, 12288), dtype=torch.int32)), Parameter(FakeTensor(..., device='cuda:0', size=(32, 6144), dtype=torch.float16)), Parameter(FakeTensor(..., device='cuda:0', size=(32, 768), dtype=torch.int32)), Parameter(FakeTensor(..., device='cuda:0', size=(0,), dtype=torch.int32)), Parameter(FakeTensor(..., device='cuda:0', size=(0,), dtype=torch.int32)), FakeTensor(..., device='cuda:0', size=(1536,), dtype=torch.int32), 4, 1, 6144, 4096, True, True), **{}):
The tensor has a non-zero number of elements, but its data is not allocated yet. Caffe2 uses a lazy allocation, so you will need to call mutable_data() orraw_mutable_data() to actually allocate memory.

from user code:
   File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/external_utils.py", line 36, 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 "/sglang/python/sglang/srt/models/llama2.py", line 314, in forward
    hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/sglang/python/sglang/srt/models/llama2.py", line 281, in forward
    hidden_states, residual = layer(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/sglang/python/sglang/srt/models/llama2.py", line 231, in forward
    hidden_states = self.self_attn(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/sglang/python/sglang/srt/models/llama2.py", line 165, in forward
    qkv, _ = self.qkv_proj(hidden_states)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/linear.py", line 330, in forward
    output_parallel = self.quant_method.apply(self, input_, bias)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/quantization/awq_marlin.py", line 257, in apply
    return apply_awq_marlin_linear(
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/quantization/utils/marlin_utils.py", line 286, in apply_awq_marlin_linear
    output = ops.gptq_marlin_gemm(reshaped_x,
  File "/usr/local/lib/python3.10/dist-packages/vllm/_custom_ops.py", line 34, in wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/_custom_ops.py", line 291, in gptq_marlin_gemm
    return torch.ops._C.gptq_marlin_gemm(a, b_q_weight, b_scales, b_zeros,

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
. Possible solutions:
1. disable cuda graph by --disable-cuda-graph
2. set --mem-fraction-static to a smaller value
Open an issue on GitHub with reproducible scripts if you need help.

Initialization failed. detoken_init_state: init ok

Reproduction
python -m sglang.launch_server --model-path  casperhansen/llama-3-8b-instruct-awq --enable-torch-compile --disable-radix-cache

Environment
Python: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
CUDA available: True
GPU 0: NVIDIA A100-SXM4-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.1, V12.1.105
CUDA Driver Version: 535.129.03
PyTorch: 2.3.1+cu121
sglang: 0.2.7
flashinfer: 0.1.3+cu121torch2.3
requests: 2.32.3
tqdm: 4.66.4
numpy: 1.26.3
aiohttp: 3.10.0
fastapi: 0.111.1
hf_transfer: 0.1.8
huggingface_hub: 0.24.5
interegular: 0.3.3
packaging: 23.2
PIL: 10.2.0
psutil: 5.9.8
pydantic: 2.8.2
uvicorn: 0.30.4
uvloop: 0.19.0
zmq: 24.0.1
vllm: 0.5.3.post1
openai: 1.37.1
anthropic: 0.32.0
NVIDIA Topology:
        GPU0    NIC0    NIC1    NIC2    NIC3    CPU Affinity    NUMA Affinity       GPU NUMA ID
GPU0     X      SYS     SYS     SYS     SYS     64-127,192-254  1          N/A
NIC0    SYS      X      PIX     SYS     SYS
NIC1    SYS     PIX      X      SYS     SYS
NIC2    SYS     SYS     SYS      X      PIX
NIC3    SYS     SYS     SYS     PIX      X

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect betweenNUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3

ulimit soft: 65535

--disable-cuda-graph is ok

Hi, zhyncs @zhyncs
I found you have successed inference model quantized by AWQ. Recently, I also do the work(quantizing LLaVA model and inference it), then I have some questions for you as below.

Did you use llm-compressor to quantize llama-3-8b-instruct model ?
You quantized it into INT8 or FP8?
When inference llama-3-8b-instruct-awq, SGLang needs quantization config file for awq, so how do you get it?
And where should place the awq quantization config file?
Compared with original precision, how about the inference performance of quantized, especially for latency?

Looking forward your reply, thanks a lot.

Hi @caojinpei You can directly download the converted ones from HuggingFace, or use libraries like AutoAWQ, AutoFP8.
ref
https://huggingface.co/collections/neuralmagic/llama-31-quantization-66a3f907f48d07feabb8f300
https://huggingface.co/casperhansen
https://github.com/casper-hansen/AutoAWQ
https://github.com/neuralmagic/AutoFP8
I tested the Llama3 8B Instruct AWQ, which was downloaded directly from HuggingFace. When I tested it, I only focused on throughput and didn't pay special attention to latency. You can test it yourself. Thanks.

CUDA Graph is ok and we just need to disable torch compile for now.
python -m sglang.launch_server --model-path  casperhansen/llama-3-8b-instruct-awq  --disable-radix-cache

I will continue to look into the reason why torch compile cannot be used and fix it.

Currently, there are some accuracy issues with Marlin AWQ in vLLM 0.5.3.post1, so it is not recommended for online use at the moment.

Due to the poorer accuracy of AWQ Marlin in vLLM 0.5.4, we will not upgrade to vLLM 0.5.4 for now.

@zhyncs Thanks a lot for your reply.
How about the throughput before and after quantization for Llama3 8B Instruct model?
I am wonderring what's the bit of weight and activation in Llama3 8B Instruct(AWQ), W8A8?

@caojinpei You can benchmark the throughput on your own. The performance is nearly identical whether SGLang or LMDeploy activates AWQ on Llama 3.1 8B Instruct. AWQ is W4A16. In theory, W8A8's accuracy should be lower than W4A16's since activation is more sensitive to precision.

@zhyncs Hi, zhyncs
I have downloaded Meta-Llama-3.1-8B-Instruct-quantized.w8a16 model from the below link.
https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16/blob/main/README.md
But when I inference the model using SGLang, there is one such issue:
"ValueError: Quantization method specified in the model config (compressed-tensors) does not match the quantization method specified in the quantization argument (gptq)."
Have you encounterred the issue before? If you did, how to fix it?
Thanks you~

@caojinpei

@zhyncs Hi, zhyncs
I have downloaded Meta-Llama-3.1-8B-Instruct-quantized.w8a16 model from the below link. And wanna to compare inference performance on A100 before and after quantization. https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16/blob/main/README.md
But when I inference the model using SGLang, there is one such issue: "ValueError: Quantization method specified in the model config (compressed-tensors) does not match the quantization method specified in the quantization argument (gptq)."
Have you encounterred the similar issue before? If you did, how to fix it?
Thanks you~

I have solved this issue, by adding "compressed-tensors" option in server_args.py. Thank you~

Currently, it seems to meet expectations. The implementation of gptq_marlin_gemm in vLLM neither inherits from CustomOp nor has a PyTorch Native implementation.

We will soon replace AWQ Marlin, so it won't be fixed as planned.


Adjust unitary tests to it's correct path as the golang best practices.

Address issue #260.

@daisy-ycguo ,please run make test.

en..OK.
go test ./... -tags=unit works.

Please see if commands_test.go should continue to stay at integration test folder. If it is a unit test, please change it to follow the unit test best practise.

ah, sorry, I forgot this one, will update it.

yes, update as per your comments.


Evaluation error

I have encountered an error while trying to val.py on coco val2017 :
1/ I run this :
python val.py --labels /content/lightweight-human-pose-estimation.pytorch/data/coco/annotations/person_keypoints_val2017.json --images-folder /content/lightweight-human-pose-estimation.pytorch/data/coco/val2017 --checkpoint-path /content/lightweight-human-pose-estimation.pytorch/checkpoint_iter_370000.pth
2/ after execution, I obtained this
TypeError: 'numpy.float64' object cannot be interpreted as an integer

Hi! Do you need any help with this?

Yes.
I didn't find the cause of this error ?

It works fine now after reimplementing the evaluation method.
Thanks

Cool!


[enhancement] Support source file browsing

Many debuggers offer an option to browse to a specific source file.  As an executable may include source files outside the project (e.g., in shared or static libraries), parsing the output of "info sources" and displaying the results for a user to open a specific source file to set a breakpoint could be very helpful.

Example situation for clarification:
A project exists where User A is developing libhis_thing.a, and User B is developing libher_thing.a, and User C links both libraries into their own independent executable.  When debugging, they may not know which particular source path Users A and B were using when they built their libraries, so the simple "File->Open File" may be cumbersome in this situation.  Since the file paths to all sources used in the executable are already stored in the DWARF info in the executable, parsing this info out from gdb (via "-file-list-exec-source-files") and making it available to a targeted file browser would be very beneficial to this workflow.

Just to check if I got that idea correctly:
That would be similar to entering one of -file-list-exec-source-files or info sources into the debugging console (that is possible already today), but present it in a way that you don't need to copy the interesting source path [CTRL]-[C], file-open [CTRL]+[O], insert [CTRL]+[A], [DEL], [CTRL]+[V], right?
It is a pity that you can nearly click on a path "anywhere" and have vscode using it a a file link, but not in the debugging console.
If we could just output the data to a new "pseudo-terminal" then users could click on those... - but then, having file links working in the debug console seems like a reasonable FR for vscode in general as debuggers are not unlikely to output relevant file paths in there console - @ereisch could you please check if that exists/otherwise create it and link it here?
If that exists there's no need for the workaround to create a pseudo-terminal.
For the FR in this repo:

How do you suggest to present the list to the user?
How should the entries be filtered/grouped?

Workaround for now: get the list in the debugging console, then copy+paste-to-open; a useful way to get the list for all files that have source information available for me is:
pipe info sources | grep "usr/include" | awk -F ', ' '{for(i=1; i<=NF; i++) print $i}' | grep -v "\\.h$" | sort -u:

get all source information "as text"
limit to the lines that have any system include in (it is very likely that a compiled source has those)
split those lines (separated by , )
drop the headers from the list (because it is unlikely you want to have breakpoints in there; but you can drop/adjust the filter as necessary)
sort with dropping duplicates.

A console listing may be onerous, as some projects may contain thousands of files.  Perhaps a dialog using window.showQuickPick() (if I'm understanding the API correctly)?

That could be a way: add a new command in the extension, that would then have to "connect" to the current debugging session (there is an example in the single command currently available in the extension: "examine memory"), execute -file-list-exec-source-files there, place every fullname attribute contained in the response to a (Hash?)Map (to drop duplicates) then get an String[] out of it and, then pass that to window.showQuickPick and open the chosen option in the editor.
That could be a contributed command code-debug.openSourceFile.
I guess that fullname´ is only available if the debugger can resolve the path, so if you need to setup source name translation ( set substitute-path) / directoryfirst that won't help - and for that another command could be created, something likecode-debug.listUnavailableSourceFiles`.
Do I get the idea right?

Correct; I don't know that the debugger attempts to resolve paths on its own; I believe those paths returned are simply as they are placed in the DWARF info (for ELF files).  So if you compiled a file with "gcc  ./src/my_file.c", all you would get in the DWARF info would be "./src/my_file.c".  Courteous programmers should be using absolute paths if working in a shared development environment on centrally-mapped drives, or relative paths if working out of a common repo, which means Code should be able to resolve them if working in the same environment or in a clone of the same repo, respectively.
Overall, the file resolution logic should be identical to whatever code-debug is presently using to resolve a path presented by gdb when you 'step' or 'break' into a routine that is in a different file than is currently open.


[Playground] The "COPY AS FRIENDLY DSL" button in the playground doesn't give me the newly saved DSL

OpenFGA v0.2.4
The "COPY AS FRIENDLY DSL" button in the playground doesn't give me the newly saved DSL

Create a new Store
Copy the DSL using the "COPY AS FRIENDLY DSL" button

type group
  relations
    define member as self
type resource
  relations
    define writer as self
    define reader as self or writer

Added and SAVE a new type "dog"

type group
  relations
    define member as self
type resource
  relations
    define writer as self
    define reader as self or writer
type dog
  relations
    define owner as self

"COPY AS FRIENDLY DSL" returns the following, dog is missing;

type group
  relations
    define member as self
type resource
  relations
    define writer as self
    define reader as self or writer

Copy as JSON works
{
  "type_definitions": [
    {
      "type": "group",
      "relations": {
        "member": {
          "this": {}
        }
      }
    },
    {
      "type": "resource",
      "relations": {
        "writer": {
          "this": {}
        },
        "reader": {
          "union": {
            "child": [
              {
                "this": {}
              },
              {
                "computedUserset": {
                  "object": "",
                  "relation": "writer"
                }
              }
            ]
          }
        }
      }
    },
    {
      "type": "dog",
      "relations": {
        "owner": {
          "this": {}
        }
      }
    }
  ]
}

Thanks for raising this issue @ghstahl we will take a look at it before the end of the day

Hello @ghstahl , sorry for the delay.  The issue is now fixed.  Please let us know if you still see any issues.


Ensure that challenge response contains body

Description
Included Unauthorized in the body of the challenge response to be consistent with the behavior from 2.10 and before.

Category

Bug fix
Issues Resolved

https://github.com/opensearch-project/security/issues/3803
https://github.com/opensearch-project/security/issues/4214

Check List

[X] New functionality includes testing
[X] New functionality has been documented
[X] Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

@peternied I pushed a fix for the resource-focused tests. For each request in the test it was holding the response in memory which became larger since the response body was now populated. Instead of returning the entire request object, I changed it to only return the status code. This better isolates the resources being tested to the resources of the cluster and not of the test case.

@cwperks Could you look into that test failure from SecurityInterceptorTests, doesn't look related - could you create an issue for follow up?

@peternied I am seeing AddressAlreadyInUse errors:
com.amazon.dlic.auth.http.saml.HTTPSamlAuthenticatorTest > shouldUnescapeSamlEntitiesTest2 FAILED
    java.net.BindException: Address already in use

I will file an issue to see if these errors can be systematically addressed. iirc it does port randomization, but I wonder if there can be port randomization with memory to not assign previously allocated ports.

@cwperks Are we only focusing on BasicAuth for this one?

Yes, this was the instance where the response changed from 2.10 -> 2.11


Add feature for async_trait support

This suggests adding a feature to ensure that the trait is generated with an async_trait invocation

Feature async_trait disabled -> nothing changed
Feature async_trait enabled -> by default, #[async_trait] will be added; can be disabled by using #[extension_trait(async_trait = false)]

Please don't use features for this, features have to be additive. The problem with a feature like this is that if some other crate uses this crate as well it will have this feature too, which will break if it doesn't have async_trait in its dependencies.
Anyway, it seems like async_trait does work with extension_trait if you provide #[async_trait] first. For instance, the following program works for me. I'll be later fixing this crate to work when #[async_trait] is provided after #[extension_trait].
use async_trait::async_trait;
use extension_trait::extension_trait;

#[async_trait]
#[extension_trait]
impl Hello for () {
    async fn x(&self) -> i32 {
        42
    }
}

#[tokio::main]
async fn main() {
    println!("{}", ().x().await);
}

Wow, the provided example indeed works!
I was pretty confused because I thought I tried exactly the same. Also tried both orders of the macros.
There is one difference though: I do a pub impl Hello for x() - And this indeed gives me compiler errors!
Nevertheless, you're right about the problem lying somewhere different and that this PR can be closed.
Should I open a new issue for this?

Hi @xfix, can you reproduce the described issue? If so, I can try having a look into an appropriate fix for the problem at hand


Upgrade to buildroot 2015.02 and remove nginx

Upgrades to buildroot 2015.02. Lots of fixes (the current version didn't even build for me).
Removes the nginx package. Looks like nginx was added in 2014.11 per changelog at http://git.buildroot.net/buildroot/plain/CHANGES?id=2014.11-rc1.
Changes base to debian to save a little space. Why not ubuntu-debootstrap? I don't know...

Before merging:

[x] Someone other than me take a peek at the auto-updated example/config.
[x] I'll test the builder on EC2 with my latest edits to make sure it actually works.

example/config looks fine. mostly added commented lines.

Tested locally and was able to build a Docker image with the resulting rootfs.tar.


SuSo-B-005 Not possible to enter values larger than 9

Problem description
It is not possible to enter values larger than 9 because

every keyup event submits the value and the selected cell looses its focus
when selecting the cell again, the entire text is selected and the next entered value overwrites the existing one

Reproduction steps

start entering a Sudoku to be solved
try entering the value 10 or higher

Explected behavior

any value can be entered and is evaluated whether it is valid
only bluring the cell submits the value

if it is valid, it will be accepted
if it is invalid, it will be reset to the previous value

New behavior:


Unregistered stuff - Blame report.

****************** Blame Report 1.16.4-2.4.1 ******************
This is an experimental report. It is suppose to automatically read
the JSON of all the unregistered ConfiguredFeatures, ConfiguredStructures,
and ConfiguredCarvers. Then does its best to collect the terms that seem to
state whose mod the unregistered stuff belongs to.
Possible mods responsible for unregistered stuff:
astromine:crude_oil
astromine:meteor

Duplicate of #329


refactor(prost): optimize some Debug representation

I hereby agree to the terms of the RisingWave Labs, Inc. Contributor License Agreement.
What's changed and what's your intention?

Checklist

[ ] I have written necessary rustdoc comments
[ ] I have added necessary unit tests and integration tests
[ ] I have added test labels as necessary. See details.
[ ] I have added fuzzing tests or opened an issue to track them. (Optional, recommended for new SQL features #7934).
[ ] My PR contains breaking changes. (If it deprecates some features, please create a tracking issue to remove them in the future).
[ ] All checks passed in ./risedev check (or alias, ./risedev c)
[ ] My PR changes performance-critical code. (Please run macro/micro-benchmarks and show the results.)

[ ] My PR contains critical fixes that are necessary to be merged into the latest release. (Please check out the details)

Documentation

[ ] My PR needs documentation updates. (Please use the Release note section below to summarize the impact on users)

Release note
If this PR includes changes that directly affect users or other significant modifications relevant to the community, kindly draft a release note to provide a concise summary of these changes. Please prioritize highlighting the impact these changes will have on users.

[!WARNING]
This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

#18112
#18111  👈
#18110
main

This stack of pull requests is managed by Graphite. Learn more about stacking.

Join @xxchan and the rest of your teammates on  Graphite


Could not run 'aten::_softmax'

Hello I am getting an error using the latest PyTorch-DirectML as it seems the softmax function is not working. I am getting the error I attached below.
I am aware that not all operators are implemented yet, but this operator is not listed anywhere on here: https://github.com/microsoft/DirectML/wiki/PyTorch-DirectML-Operator-Roadmap and other variants of softmax appear to already be implemented. Is this intentional?
Traceback (most recent call last):
  File "E:/Users/austi/PycharmProjects/CS5787/hw2/Problem2.py", line 65, in <module>
    run_peppers()
  File "E:/Users/austi/PycharmProjects/CS5787/hw2/Problem2.py", line 49, in run_peppers
    probabilities = softmax(output[0], dim=0)
  File "C:\Users\austi\anaconda3\envs\CS57872\lib\site-packages\torch\nn\functional.py", line 1583, in softmax
    ret = input.softmax(dim)
RuntimeError: Could not run 'aten::_softmax' with arguments from the 'UNKNOWN_TENSOR_TYPE_ID' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_softmax' is only available for these backends: [CPU, MkldnnCPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradNestedTensor, UNKNOWN_TENSOR_TYPE_ID, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].

CPU: registered at D:\a\_work\1\s\pytorch-directml\build\aten\src\ATen\RegisterCPU.cpp:5926 [kernel]
MkldnnCPU: registered at D:\a\_work\1\s\pytorch-directml\build\aten\src\ATen\RegisterMkldnnCPU.cpp:285 [kernel]
BackendSelect: fallthrough registered at D:\a\_work\1\s\pytorch-directml\aten\src\ATen\core\BackendSelectFallbackKernel.cpp:3 [backend fallback]
Named: registered at D:\a\_work\1\s\pytorch-directml\aten\src\ATen\core\NamedRegistrations.cpp:7 [backend fallback]
AutogradOther: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradCPU: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradCUDA: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradXLA: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradNestedTensor: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
UNKNOWN_TENSOR_TYPE_ID: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradPrivateUse1: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradPrivateUse2: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
AutogradPrivateUse3: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\VariableType_0.cpp:9273 [autograd kernel]
Tracer: registered at D:\a\_work\1\s\pytorch-directml\torch\csrc\autograd\generated\TraceType_0.cpp:10499 [kernel]
Autocast: fallthrough registered at D:\a\_work\1\s\pytorch-directml\aten\src\ATen\autocast_mode.cpp:250 [backend fallback]
Batched: registered at D:\a\_work\1\s\pytorch-directml\aten\src\ATen\BatchingRegistrations.cpp:1016 [backend fallback]
VmapMode: fallthrough registered at D:\a\_work\1\s\pytorch-directml\aten\src\ATen\VmapModeRegistrations.cpp:33 [backend fallback]

@Adele101

Hi, thank you for bringing this to our attention.
Both of the errors seem to be occurring because the aten::_softmax_backward_data function is not implemented with DML as of yet.
We will update the roadmap and look into addressing this gap in the next release.

Hi @austinv11  and @darkar18,  aten::_softmax_backward_data should be implemented in the current version of torch-directml-0.1.13.dev221216. Give that a shot and let us know if you face any other issues.

Sure thank you, I ll get back to you
Get Outlook for Androidhttps://aka.ms/AAb9ysg

From: Linnea May @.>
Sent: Saturday, December 17, 2022 2:12:36 AM
To: microsoft/DirectML @.>
Cc: Alex v Ajith @.>; Mention @.>
Subject: Re: [microsoft/DirectML] Could not run 'aten::_softmax' (Issue #215)
Hi @austinv11https://github.com/austinv11 and @darkar18https://github.com/darkar18, aten::_softmax_backward_data should be implemented in the current version of torch-directml-0.1.13.dev221216https://pypi.org/project/torch-directml/. Give that a shot and let us know if you face any other issues.
—
Reply to this email directly, view it on GitHubhttps://github.com/microsoft/DirectML/issues/215#issuecomment-1355577705, or unsubscribehttps://github.com/notifications/unsubscribe-auth/APVGBEBDHNRR3LTBAVWTYVDWNTH3ZANCNFSM5QUHQMGQ.
You are receiving this because you were mentioned.Message ID: @.***>


support include as a field name

Changelog
support include as a field name
Docs
None
Description
We were treating include as a keyword in our nearly grammar even though it is not considered a keyword in the OMGIDL spec. This PR fixes this which allows to use include as a regular field name.

@snosenzo any idea if there was a reason we purposely treated include as a keyword before?


Fix git and hub versions to latest ones

Latest git version is 2.14.2, latest hub version is 2.2.9.

Thanks, but we won't be updating documentation every time any of these version numbers change. The output in the examples are supposed to be illustrating what the output will look like, not exactly what it will be.


#217 -- Update Textfield Props

Can we also rework the the docs page for this to match the updated one.

Re-worked docs page to better match the current way we've been doing things.


fs: realpath: fix string size before converting

fs__realpath_handle() removes the UNC prefixes \?\ and \?\UNC
from paths returned by GetFinalPathNameByHandle, but doesn't
update the string length before sending it to fs__wide_to_utf8.
Fix that by decrement the correct size from the string length.
Fixes the issue raised by @mscdex in nodejs/node#3594.

Can you add a regression test for this?

Unfortunately no, as it depends on the content of the memory after the allocated buffer in fs__realpath_handle().

Any chance to merge this?

Sorey Yuval, I hope to get back to some windows PRs this upcoming week.
On Mar 5, 2016 4:58 PM, "Yuval Brik"<EMAIL_ADDRESS>wrote:

Any chance to merge this?
—
Reply to this email directly or view it on GitHub
https://github.com/libuv/libuv/pull/733#issuecomment-192676975.

@saghul - I think it could be great to have that for the 6.0.0 node release. I would appreciate if you will have the time to review and merge this.
Thanks!

+1

@mscdex I think github created the reactions so you wouldn't have to write it out:
https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments

but other times you just want to :+1: someone else's comment. We're adding Reactions to conversations today to help people express their feelings more simply and effectively.

@mscdex I think github created the reactions so you wouldn't have to write it out:
https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments
but other times you just want to  someone else's comment. We're adding Reactions to conversations today to help people express their feelings more simply and effectively.

-1

LGTM, let's see if the CI is green: https://ci.nodejs.org/job/libuv+any-pr+multi/285/

Looks like unrelated failures and a possible CI infrastructure-related issue on the win2012r2 node?

Yep, it's the usual Jenkins brainfart.

Landed in 5dc15cc, thanks! And sorry for the delay!


Spirv-opt generates code which loads whole OpTypeRuntimeArray

This comes from CTS tests (https://github.com/KhronosGroup/VK-GL-CTS), with following command line:
deqp-vk.exe --deqp-optimize-spirv=disable --deqp-optimization-recipe=1 -n dEQP-VK.ssbo.layout.random.unsized_arrays.0
This runs the GLSL-based test, where shader is compiled using glslang and than optimized using SPIRV-Tools.
The code genarated by spirv-opt is not valid, as it contains OpLoad which loads whole runtime array, of unbounded size.
The SPIR-V specification says:

OpLoad
Load through a pointer.
Result Type is the type of the loaded object. It must be a type with fixed size; i.e., it cannot be, nor include, any OpTypeRuntimeArray types.

Consider following GLSL source:
layout(std430, row_major, binding = 4) buffer BlockE
{
	highp mat3 k;
	bvec2 l[3];
	lowp vec3 m;
	bool n;
	mediump mat3 o[];
} blockE;
...
allOk = allOk && compare_mat3(blockE.o[0], mat3(1.0, 2.0, 4.0, -5.0, 3.0, 1.0, -7.0, 9.0, -2.0));

All accesses to blockE members are compiled by glslang as loading from pointers to the members:
%363 = OpAccessChain %362 %353 %188
%364 = OpLoad %35 %363
...
%383 = OpAccessChain %382 %353 %371 %178
%384 = OpLoad %42 %383
...
400 = OpAccessChain %382 %353 %371 %188
%401 = OpLoad %42 %400

spirv-opt translates this code to single OpLoad on entire structure:
%350 = OpTypeRuntimeArray %42
%351 = OpTypeStruct %349 %35 %35 %177 %350
%352 = OpTypePointer Uniform %351
%353 = OpVariable %352 Uniform
%3427 = OpLoad %351 %353

The problem is %351 contains %350 - which is of OpTypeRuntimeArray type.

My guess is that this is a problem with the common-uniform-elimination pass.  That is a pass that @greg-lunarg maintains.  Besides this, we know it causes problems (#946).  Greg, can you take a look?
In the meantime, I would recommend using  --deqp-optimization-recipe=7.  This includes the latest optimizations, and removes common-uniform-elimination.

See my comments for #2521

Given the comment in #2521, I am going to propose removal of 'common-uniform-elimination' from CTS.

@greg-lunarg Are you working on this?

@greg-lunarg Any update?

I will try to get this done in the next day or so. Thanks!


⚠ Bofrost.at has degraded performance

In c8cce1b, Bofrost.at (https://www.bofrost.at) experienced degraded performance:

HTTP code: 200
Response time: 1158 ms

Resolved: Bofrost.at performance has improved in 6d4b710 after 6 minutes.


Metaticket - TODO list for initial release

TODO list for initial release...

[x] LICENSE file
[x] LICENSE: Add Name of copyright owner
[x] Rails Engine scaffold
[x] Standard.rb / Linter
[x] Solr: via SolrWrapper or Lando?
[x] EngineCart
[x] Test fixtures: GBL > Allmaps georeferenced
[x] Test fixtures: GBL > Allmaps NOT georeferenced
[x] Test fixtures: BL (Marc) > Allmaps georeferenced
[x] Test fixtures: BL (Marc) > Allmaps NOT georeferenced
[x] GitHub Actions / CI
[x] Dependabot
[x] RSpec test suite
[x] Show page / Toolbar button(s)
[x] RDBMS: SidecarAllmaps table
[x] Rake tasks for indexing / maintenance
[x] Open source / Public repo
[x] Install script(s) accept args for BL vs. GBL
[x] Install script(s) accept args for Bootstrap v4 and Bootstrap v5
[x] Test matrix: Ruby v3.2, v3.3
[ ] Test matrix: Rails v7.0, v.71
[ ] a11y: Accessibility review
[ ] Test matrix: Blacklight v7 and v8
[x] Test matrix: GeoBlacklight v4
[x] Index page / Georeferenced facet
[x] i18n: locales en file, others?

Closing as no longer relevant.


Add RelayMessage + RelayMessageOption

Hello,
Could you merge this PR for RelayMessage + RelayMessageOption?
Thx
Jérôme

Thx for review.
Plz, find all fix.

Hello,
Did you planed to merge or reject this pull request?
Thx

My apologies, I totally dropped the ball on this one.  The PR looks good to me.  Would you mind squashing into one commit, and then I will merge?
Thanks for your contribution.

Hello,
No pb. Could you squash this PR by yourself?
That seems more easy for you as describe here:
https://help.github.com/articles/about-pull-request-merge-squashing/
Many thx.
2016-07-02 17:33 GMT+02:00 Matt Layher<EMAIL_ADDRESS>
My apologies, I totally dropped the ball on this one. The PR looks good to
me. Would you mind squashing into one commit, and then I will merge?
Thanks for your contribution.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/mdlayher/dhcp6/pull/4#issuecomment-230107488, or mute
the thread
https://github.com/notifications/unsubscribe/AJhF7-H05UdIuIOM_r8PNQyj7UASJZp3ks5qRoTfgaJpZM4H0oNs
.

--
"The box said 'Requires Windows 95, NT, or better,' so I installed Linux."

Merging as #5 since the web UI wasn't cooperating.  Thanks for your contribution.


"PanGestureHandler" not working with "ImageBackground"

When PanGestureHandler is used with ImageBackground, the methods onGestureEvent and onHandlerStateChange do not trigger.
This is an example code:
import React, { Component } from 'react';
import {
  View,
  ImageBackground,
} from 'react-native';
import { State, PanGestureHandler } from 'react-native-gesture-handler';

export default class Test extends Component {

  _onGestureEvent = event => {
    console.log(event);
  };

  _onHandlerStateChange = event => {
    console.log(event);
  };

  render(){
      return (
        <PanGestureHandler
          onGestureEvent={this._onGestureEvent}
          onHandlerStateChange={this._onHandlerStateChange}>
            <ImageBackground style={{width: '100%', height: '100%'}} source={{uri: 'http://images.cinedigm.com/CEG10022532_312x472_0.jpg'}}/>
        </PanGestureHandler>
      );
    }
}

Hi, thank you for this issue.
Well, I'm afraid it should not work, because Gesture Handler could be attached only to animated views. Please wrap it ion Animated.View of use const AninamatedImageBackground = Animated.createAnimatedComponent(ImageBackground) in  order to handle your case. Please mind that the second solution is not officially supported


syntax error: operand expected (error token is ".")

sbt version: 1.3.6
My heroku build reports this on build start, but somehow this does not fail the build
-----> Play 2.x - Scala app detected

-----> Installing JDK 11... done

-----> Running: sbt clean compile stage

.sbt_home/bin/sbt-extras: line 202: [[: .: syntax error: operand expected (error token is ".")

OpenJDK 64-Bit Server VM warning: Ignoring option MaxPermSize; support was removed in 8.0

this is the project:
https://github.com/francisdb/belgiantv

hmm, after doing some more research I guess this is a dupe of https://github.com/paulp/sbt-extras/issues/212


gitmoji vs. conventional commits -- auto-generated changelog

Re: this change of the title in the corresponding PR

At Lamin, we're auto-creating the changelog and bucketing changes by UX (🚸), bug fixes (🐛), new features (✨) using the gitmoji pre-commit hook (https://gitmoji.dev/).
The idea is to get a friendly changelog without much effort, e.g., here: https://docs.lamin.ai/changelog/2024#db-0-76-16
So, if we add an emoji to the PR title, this here will be easy to read & re-organize: https://github.com/laminlabs/lamin-docs/blob/main/docs/changelog/soon/laminr.md
Similar to this: https://github.com/laminlabs/lamin-docs/blob/main/docs/changelog/soon/lamindb.md
Does this make sense -- can we do this also for laminr @lazappi @rcannood ? I know that most people use conventional commit instead of gitmoji but fastapi etc. are also following this alternative approach.

I would be happy to do that. We might have to play around with setting up pre-commit but after that it should be ok. Not sure what @rcannood thinks though.
Is adding them to PRs also automated or do you have to do that manually?

We might have to play around with setting up pre-commit but after that it should be ok.

Some on our team don't use it for every commit. And I also think this is not sooooo helpful. So, I don't think it's a requirement. All our other repos have pre-commit anyway and now that I think of it gitmoji -i might even be independently hooking into git. 🤔 So, you might not even need pre-commit.

Is adding them to PRs also automated or do you have to do that manually?

It depends on when you create the PR. If you create it after making the first commit on a new branch, the PR title will have the commit message as title.
But, to be honest, spending 1 min on making sure the PR title is well chosen and has an emoji that categorizes it well is well spent. I don't think automation is needed as it happens so rarely and should be done consciously.

I see -- sure thing! We can switch to using this approach from the next release onwards :)

Cool! :)


Add optional email field for feedback, record feedback in db

What does this change?

This improves the feedback form, adding an optional email field so we can get in touch with individuals who wish to be contacted back

This has been covered by changes to our privacy policy in: #1715

We also record the route so we know which page the user sent the feedback from
Some small improvements to accessibility and width on mobile

Whoopsy, I've double-reviewed you - definitely not meant to replace yours Max, just had the page open too long before hitting go :smile:

Whoopsy, I've double-reviewed you - definitely not meant to replace yours Max, just had the page open too long before hitting go 😄

thanks for the extra comments, all good points - i'll get back soon 🚗

@Hoolean I've had a look and made some changes based on your feedback :)


[FIX] All index integer

All index must be integer not string or number

Merged at version 1.0.0-rc13


Apache Crash triggered in test_Minify_CSS.php

Are you sure this is not a problem with your configuration? (ask on the
Google Group)

Minify commit/version:
PHP version: 5.3.5 running under XAMPP on Windows 7

What steps will reproduce the problem?
1.load test_Minify_CSS.php
2.
3.

Expected output:
Any output at all

Actual output:
Nothing, page does not load and Apache Crashes

Commenting out either line 81 or line 86 runs just fine, just not with the
correct output. Either line without the other runs fine, but both lines
together and Apache just crashes.

Original issue reported on code.google.com by<EMAIL_ADDRESS>on 14 Aug 2012 at 7:53

I forgot to say which file the line numbers were from. They are from
Minify/CSS/Compressor.php

Original comment by<EMAIL_ADDRESS>on 14 Aug 2012 at 7:54

hey, i just found a solution here... check this topic

https://code.google.com/p/minify/issues/detail?id=62#c9

Regards,
Carlos

Original comment by<EMAIL_ADDRESS>on 25 Jun 2013 at 5:27

We keep running into limits and bugs in PCRE. You can try raising PCRE's
backtrack limit and other settings. Not much I can do about it and no one
crashes on the same line :/

Original comment by<EMAIL_ADDRESS>on 15 Aug 2012 at 3:39

the same here... crashes with non error... i raised PCRE limit, script
execution time... and nothing happen... just a connection reset by apache... :(

Original comment by<EMAIL_ADDRESS>on 25 Jun 2013 at 5:22


Fix fused cpdef default arguments

A few things were going wrong:

they're creating CloneNodes (but not requiring the contents of the clone of the clone node to be temp)
Assignment from a clone node generates cleanup code (which is against the general rules of a clone node), and also loses a reference via giveref
cpdef functions cause a small memory leak (#4412) by assigning to their constants twice. This is unfortunately difficult to test for. With this patch we no longer leak, but still duplicate a little bit of work.

Fixes #4409 and #4412

Nice. Thanks a bunch for digging this up.


feat(ffe-searchable-dropdown-react): added darkmode styling

Added darkmode styling to searchable dropdown list.

Note that this PR does not solve the styling issues described in #814 and #815.

Coverage remained the same at 84.949% when pulling a5bdcaa2cf6ac2e4901e5bb0e4f5eb80221fe39c on searchable-dropdown-darkmode-fix into 54a5da6f90103f5544c6569c0c8e1b96ba454faa on develop.

Only issue I saw was that you need repeat the styling, but if thats not fixable this is ok for me.
.ffe-searchable-dropdown__list {
        background: @ffe-grey-dark-blue--dark;
        .native & {
            @media (prefers-color-scheme: dark) {
                background: @ffe-grey-dark-blue--dark;
            }
        }
    }

I know it looks redundant, but I have to do this, or else the styling will be overwritten by the dark mode styling when in dark mode :/

Husk og squash commitesen bare.

Closing and opening a new PR to clean up commits.


Expecting  client eval warnings on Last and LastOrDefault query extensions

Steps to reproduce
A simple Last or LastOrDefault query.
db.MyEntities.LastOrDefault();
I'm using the SQL Server provider.
Provider is configured to throw on client eval.
The issue
Actual query against database is not filtered:
I was seeing this in the ILoggerProvider logging and then verified in SQL Profiler:
SELECT [s].[Id], [s].[Name]
FROM [Samurais] AS [s]
Therefore I can only assume the full set is returned and the Last evaluation is happening in memory.
I can see that the [throw on client eval] config is working because an Average method does cause an exception to throw.
No exception thrown for Last or LastOrDefault,
system
EF Core version: 1.1.0-preview1-final
Operating system: windows 10
Visual Studio version: VS2015

We should also double-check if/when we can translate Last.

If the query has order by, we could reverse it (asc->desc, desc->asc) and use First/FirstOrDefault instead

yeah ...translating last would be very subjective, TOP 1 is also subjective I suppose since it's possible (in linq and in TSQL) to use it without an order by. Would be easier to blame the database if it just had a BOTTOM expression ;)


Documentation tweak

Status: Ready for Review
Reviewers: @jeffkamo @avelinet @ry5n @tedtate
Changes

Documentation updated with TODO instructions when you committed directly to gh-pages

Todos:

[ ] Designer +1
[ ] Designer +1

@nastiatikk well let's not be too hasty in closing PRs like this. I was still hoping to check with @ry5n and @avelinet that this change makes sense.

@jeffkamo it was closed automatically after I renamed the branch, sorry for confusion

Seems to make sense, if this is the right fix I wouldn’t mind having it.


Example for how to add tanh distortion to triangle wave?

Trying to smooth out a triangle wave to make it look more like a sine wave (but don't want a sine wave).

However, when I try using the tanh distortion node on a triangleWave operation generator it just modifies the amplitude of the wave or widens it a bit.
Using the code:
import XCPlayground
import AudioKit

//make triangle wave operation @ 86Hz
var triangleWave = AKOperation.triangleWave(frequency: 86.0, amplitude: 0.25)
var waveGen = AKOperationGenerator(operation: triangleWave)

//Tanh Distortion of triangle wave so make it look a little more like a sine wave
var distortion = AKTanhDistortion(waveGen, pregain: 0.5, postgain: 0.5, postiveShapeParameter: 0.5, negativeShapeParameter: 0.5)

AudioKit.output = distortion
AudioKit.start()

waveGen.start()
distortion.start()

let plotView = AKOutputWaveformPlot.createView()
XCPlaygroundPage.currentPage.liveView = plotView
XCPlaygroundPage.currentPage.needsIndefiniteExecution = true

This is what I see from the above code

Is this the correct way to tanh distort a triangleWave ?

This must be a bug and I'll look at it, but if you really want that other curve, why don't you make it?  See the playground for general purpose oscillator where the custom curve is defined:  http://audiokit.io/playgrounds/General Purpose Oscillator/

Hi Aure,
Thank you for that example. I made a custom wave that was pretty close to what I wanted.
However, I like using the AKOperation.triangleWave since I can use the triggerWithAttack operation that ends the waveform after the hold param is complete:
var boom = AKOperation.triangleWave(frequency: 80, amplitude: 1.0)
var boomTrigger = boom.triggeredWithEnvelope(AKOperation.trigger, attack: 0.01, hold: 0.2, release: 0.1)

I know I can do this with the AKOscillator using the amplitude envelop but it requires me to manually call the start and stop methods of the amplitude envelope which makes what I'm trying to do a little more complicated.
I'll wait to see what you found for the other issues I mentioned (clipper and tanh distortion) .....

@Joshua-Thompson Hey Joshua!
This one is a "bug" that I think I fixed. It turns out that the sp_dist module was set up by default to distort a signal in the range [-32767, 32767] when really it should have been set up by default to process normalized signals in the range [-1, 1]. This problem is really just an artifact of the original Csound code it was ported from.
I have put the new changes in soundpipe dev branch, so Aure can put those changes in.

Okay, this produces errors on valgrind. Now we're getting somewhere. I think there's some uninitialized variable somewhere:
#include <stdio.h>
#include "soundpipe.h"

typedef struct {
    sp_bltriangle *tri;
    sp_clip *clp;
} UserData;

void write_clip(sp_data *sp, void *ud) {
    UserData *udp = ud;
    SPFLOAT out, tri;
    sp_bltriangle_compute(sp, udp->tri, NULL, &tri);
    sp_clip_compute(sp, udp->clp, &tri, &out);
    sp->out[0] = out;
}

int main() {
    UserData ud;
    sp_data *sp;
    sp_create(&sp);

    sp_bltriangle_create(&ud.tri);
    sp_clip_create(&ud.clp);

    sp_clip_init(sp, ud.clp);
    ud.clp->lim = 0.4;
    ud.clp->meth = 0;
    sp_bltriangle_init(sp, ud.tri);
    *ud.tri->amp = 1.0;
    sp_process(sp, &ud, write_clip);

    sp_clip_destroy(&ud.clp);
    sp_bltriangle_destroy(&ud.tri);

    sp_destroy(&sp);
    return 0;
}

@PaulBatchelor I think you posted that message to the wrong issue :)

@Joshua-Thompson I believe this is fixed in the develop branch. I ran your playground and the parameters now shape the sound a lot.  Thanks a lot!

I see, ok I'll download the new code from the dev branch.
Thank you both!

Just wanted to say that it works great now, thanks for resolving that bug :)


Since 0.10.5 the KeepAlive value in the default MongoConnectionOptions differs from the default of StandardSocketOptions

Since version 0.10.5 we ran into a lot of disconnected socket exceptions. When drilling into this problem we came to the following analysis:
Since the last version of ReactiveMongo the MongoConnectionOptions are used to configure the connection settings.
In the default values of KeepAlive setting is set to true: https://github.com/ReactiveMongo/ReactiveMongo/blob/master/driver/src/main/scala/api/api.scala#L481
In the previous version this settings has no default value set by ReactiveMongo. Then the connection falls back on the settings in the StandardSocketOptions: here the KeepAlive is set to false: http://docs.oracle.com/javase/7/docs/api/java/net/StandardSocketOptions.html#SO_KEEPALIVE
This setting is used for (from the documentation): When the SO_KEEPALIVE option is enabled the operating system may use a keep-alive mechanism to periodically probe the other end of a connection when the connection is otherwise idle.
The Mongo manual has this entry about KeepAlive: http://docs.mongodb.org/v2.6/faq/diagnostics/#does-tcp-keepalive-time-affect-sharded-clusters-and-replica-sets
This "new" behavior caused by giving a different default gave us a lot of weird socket disconnected errors; we are not using the OS keep-alive mechanism and therefore we haven't used the information from the Mongo documentation. When we override the KeepAlive default that is used in ReactiveMongo (true, where false should be the default) to the false, all our problems are gone.
The fix is easy; the default of KeepAlive should be the same as underlying frameworks that is uses (in this instance the standard StandardSocketOptions which is used by Netty for setting up SocketConnections.
Maybe this issues have something to do with the behavior described above? #162 #213

A fix has been pushed to master


doesn't appear active link

active link not shown on parent menu, but the child has shown active. i tried to looking for active link on parent that has set to active.
/* application/views/admin/adminlte/admin_sidebar.php */
/* application/libraries/Template.php */
/* application/modules/users/User_admin.php */

fixed


Request for a stable release

Hi, I'm maintaining a foundry buildpacks which requires a download address and sha256, and the current version of Nightly I'm using is frequently updated, and what's even more strange is that sometimes this nightly version disappears, resulting in a CI error over here. For example:
  [[metadata.dependencies]]
    id = "foundry"
    name = "Foundry"
    purl =<EMAIL_ADDRESS>    sha256 = "6d493a985e0c8c666a9d6ded4c662d2db530d91ea2679dd941fd4b2820fab023"
    uri = "https://raw.githubusercontent.com/foundry-rs/foundry/nightly-23700c9233314e06ba65be4522daf3aa89130dc0/foundryup/install"
    stacks = ["*"]
    version = "0.2.0"

https://github.com/amp-buildpacks/foundry/actions/runs/9248668764/job/25439315306
So, I'm requesting if the Foundry team can release a stable version that might help us, and the various package managers.

+1. This has been a large issue for our team. We've resulted to utilizing the foundryup --commit option and building our own containers so we could maintain a stable development environment.

Big +1. Seems like releases are pruned by github for some reason (unless this is one of your configs to save money?). Docker images are not pruned however and are all available: https://github.com/foundry-rs/foundry/pkgs/container/foundry/versions?filters[version_type]=tagged
But for some reason forge script from inside docker image takes forever to deploy our large stuff, so is unusable for us. Then for any recent release that we need, we need to wait until the next monthly release before being able to update, which is annoying.
Is there a way to change your release config to keep all releases between the monthly releases? You can prune them after a month.

I found a temporary solution: foundry will keep one release per month on the 2nd, and the others will be removed, so I'm now specifying this in my dependencies.

I found a temporary solution: foundry will keep one release per month on the 2nd, and the others will be removed, so I'm now specifying this in my dependencies.

Yes but my point is they should keep all of the nightly releases until around until the next monthly release. Otherwise I need to wait 1 month before the next monthly release to get the bug fixes in.


Missing lidar intensity signal

Lidar signal intensity seems to be missing.
In the following code, intensity is filled with -1 no matter what (checked on the first frame of validation and testing sequences from release 1.3.2):
range_images, _, _, _ = frame_utils.parse_range_image_and_camera_projection(frame)
intensity = np.asarray(range_images[open_dataset.LaserName.TOP][0].data) \
    .reshape(range_images[open_dataset.LaserName.TOP][0].shape.dims)[:, :, 3]

Never mind, I misread the file and intensity is the second channel after range.


Fix the finetune directory link in README.md

The link to finetune at the top of the readme points at blob/finetune, fixed to the sft directory

looks like its been fixed


Use exit codes when failing start

Is your feature request related to a problem? Please describe.
When trying to start dbeaver I get this error
WARNING: Using incubator modules: jdk.incubator.jpackage, jdk.incubator.foreign

(java:27): Gdk-ERROR **: 09:23:20.548: The program 'java' received an X Window System error.
This probably reflects a bug in the program.
The error was 'BadAccess (attempt to access private resource denied)'.
  (Details: serial 194 error_code 10 request_code 130 (MIT-SHM) minor_code 1)
  (Note to programmers: normally, X errors are reported asynchronously;
   that is, you will receive the error a while after causing it.
   To debug your program, run it with the GDK_SYNCHRONIZE environment
   variable to change this behavior. You can then get a meaningful
   backtrace from your debugger if you break on the gdk_x_error() function.)

I have done as the message tells me and done export GDK_SYNCHRONIZE which seems to have done nothing.
I have also done export NO_AT_BRIDGE=1 as this error seems to relate to accessibility settings in Gnome (according to https://unix.stackexchange.com/a/230442 ).
If I try to start dbeaver again, it works. The number of retries varies. Max number of retries I have had before successfully starting is 7. Sometimes it works on the second run. Never on the first.
Describe the solution you'd like
As a simple solution I thought I would just script around this. Do n retries until it starts and stop the retries when I have a successful launch. This was not as easy as it seemed.
Basically, my idea was the following
#!/bin/bash
n=0
until [ "$n" -ge 5 ]
do
   dbeaver
   exitCode=$?
   if [[ $exitCode -eq 0 ]]; then
           break
   fi
   n=$((n+1))
   sleep 5
done

The problem with this is that even when dbeaver has a failed start, it gives an exit code of 0. What I would like is for dbeaver to return a non-zero exit code if it actually didn't come up.
Describe alternatives you've considered
Well, if dbeaver would launch without trouble that'd be great. I do think this is more gnome relate though so not really in the hands of dbeaver.
I could just manually retry, but a script is less work from the user which is nice.
Additional context
Not really. If you want I could probably whip up a docker-container that attaches to the X session so you can try the problem yourself and see when you have a working solution.

For anyone facing the same problem and want a script for it I did some more testing.
#!/bin/bash
n=0
until [ "$n" -ge 5 ]
do
   dbeaver 2>&1 | tee beaver.log
   bad=$(grep "BadAccess" beaver.log | wc -l)
   if [ "$bad" -gt 0 ]; then
           echo "Failed to actually start. Retry $n/5"
   else
           break
   fi
   rm beaver.log
   n=$((n+1))
   sleep 1
done
exit 1

Since we can't rely on the exit code, redirect all output to a log and check if that log contains the error.
If it contains the error, retry. If it does not then we will hopefully have had a successful start and we can break from the loop.

Please tell your Java and Linux versions.

ArchLinux, kernel 5.9.2
OpenJDK 14.0.2


[image_picker] add requestFullMetadata for iOS (optional permissions)

Description
Originally #3264 made by @cpboyd
Creating a new PR as I've closed the previous one (#4638) by a mistake
Make iOS 11+ permissions requests optional per https://developer.apple.com/forums/thread/653414.
PHAssets aren't actually required and there was already some fallback code in place.
Related issues
Fixes flutter/flutter#65995
Pre-launch Checklist

[x] I read the Contributor Guide and followed the process outlined there for submitting PRs.
[x] I read the Tree Hygiene wiki page, which explains my responsibilities.
[x] I read and followed the relevant style guides and ran the auto-formatter. (Unlike the flutter/flutter repo, the flutter/plugins repo does use dart format.)
[x] I signed the CLA.
[x] The title of the PR starts with the name of the plugin surrounded by square brackets, e.g. [shared_preferences]
[x] I listed at least one issue that this PR fixes in the description above.
[x] I updated pubspec.yaml with an appropriate new version according to the pub versioning philosophy, or this PR is exempt from version changes.
[x] I updated CHANGELOG.md to add a description of the change, following repository CHANGELOG style.
[x] I updated/added relevant documentation (doc comments with ///).
[x] I added new tests to check the change I am making, or this PR is test-exempt.
[x] All existing and new tests are passing.

@PiotrMitkowski Will you be able to continue working on this? It's totally fine if not; we can take it over (though it might take longer). I'm just asking because I'm trying to clean out our review queue.

@Hixie definitely yes, it's just blocked by another PR for iOS changes: https://github.com/flutter/plugins/pull/5713. I'll get back here as soon as those iOS changes are reviewed and merged.

Sorry about that review delay; the iOS changes have now been published!

Sorry about that review delay; the iOS changes have now been published!

No problem, thanks!

This looks great! Just one thing I overlooked in the original review: the README section about iOS permissions needs to be updated to discuss optionality of the library permission.

@stuartmorgan I've added a paragraph about new permissions handling. Let me know if that's what you had in mind.

@idish thanks for your feedback! I'll take an extra look to make sure, that it works correctly.

@idish I've found a bug and fixed it in #6429. You can take a look there, if you want. I'll put this PR in draft while waiting on the other one to be merged.

@idish I've found a bug and fixed it in #6429. You can take a look there, if you want. I'll put this PR in draft while waiting on the other one to be merged.

Thanks! I can confirm that your bugfix PR works.

@cyanglaz Ping from triage on this review.

Hello, have you fixed the problem of 'Select More Photos'? I have also reproduced the problem here

@liumengchun this is more like a workaround to not use the gallery permissions at all. They are needed only to access some specific image metadata, which isn't needed in most of the cases.

@PiotrMitkowski thank you, great work 👏