Open Yanhan-cmd opened 6 months ago
I have the same problem as you, but the difference is that: When I use Ansor tuning vit_b_32.onnx, the tvm.auto_scheduler.extract_tasks
is crushed. I print some messages in tvm.auto_scheduler.relay_integration.auto_schedule_topi
:
This information shows that the problem seems to occur when fusing
layout_transform
, reshape_transpose
and concatenate_add
.
Maybe the underlying reason for the problem you encountered is also this.
@Yanhan-cmd @Jocx-H
I have also been testing I-VIT recently, and you can switch the TVM version to v0.9.0. However, I found that its speed is far from as fast as claimed in the paper, and FasterTransformer is not as slow as the paper suggests
One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details. Traceback (most recent call last): File "D:/TVM/I-ViT/TVM_benchmark/evaluate_accuracy.py", line 97, in
main()
File "D:/TVM/I-ViT/TVM_benchmark/evaluate_accuracy.py", line 81, in main
lib = relay.build(func, target=target, params=pretrained_params)
File "D:\Anaconda3\envs\tvm-build\lib\site-packages\tvm-0.15.dev103+g506eff23b-py3.8-win-amd64.egg\tvm\relay\build_module.py", line 364, in build
graph_json, runtime_mod, params = bld_mod.build(
File "D:\Anaconda3\envs\tvm-build\lib\site-packages\tvm-0.15.dev103+g506eff23b-py3.8-win-amd64.egg\tvm\relay\build_module.py", line 161, in build
self._build(
File "D:\Anaconda3\envs\tvm-build\lib\site-packages\tvm-0.15.dev103+g506eff23b-py3.8-win-amd64.egg\tvm_ffi_ctypes\packed_func.py", line 229, in call
_LIB.TVMFuncCall(
OSError: exception: stack overflow
tvm.version :0.15.dev0
Platform: LAPTOP
Operating system: Windows
Device: PC+RTX 3070
Python version: 3.8
GPU driver version (if applicable):12.0
CUDA/cuDNN version (if applicable):12.0