Entire Frame [?]:
Backend [?]:
Failure Exception:
<class 'torch._dynamo.exc.UserError'>
Failure Reason:
Tried to use data-dependent value in the subsequent computation. This can happen when we encounter unbounded dynamic value that is unknown during tracing time. You will need to explicitly give hint to the compiler. Please take a look at torch._check OR torch._check_is_size APIs. Could not guard on data-dependent expression Eq((u0//7), u0) (unhinted: Eq((u0//7), u0)). (Size-like symbols: u0)
Potential framework code culprit (scroll up for full backtrace):
File "/opt/conda/lib/python3.11/site-packages/torch/_refs/__init__.py", line 3685, in _reshape_view_helper
if guard_size_oblivious(length == a_.shape[idx]):
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
User Stack (most recent call last):
(snipped, see stack below for prefix)
File "/workspace/networks/encoders/swin/swin_transformer.py", line 709, in forward
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
File "/opt/conda/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
return forward_call(*args, **kwargs)
File "/workspace/networks/encoders/swin/swin_transformer.py", line 456, in forward
mask_windows = window_partition(
File "/workspace/networks/encoders/swin/swin_transformer.py", line 75, in window_partition
x = x.view(B, H // window_size, window_size, W // window_size, window_size,
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#constrain-as-size-example
In file
/workspace/networks/encoders/swin/swin_transformer.py, line 75
No restarts!
Cache Size: 1
Accumulated Cache Size: 1
Guard Count:
Shape Env Guards:
Graph Ops:
Graph Nodes:
Graph Inputs:
Compliant Custom Ops:
Non-Compliant Custom Ops:
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