Comments (1)
Since TORCHDYNAMO_INLINE_INBUILT_NN_MODULES
we will probably just close this issue when @anijain2305 and @laithsakka get this turned on by default
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Related Issues (20)
- The doc of `column_stack()` should say `tuple` or `list` of tensors for `tensors` argument HOT 1
- The doc of `dstack()` should say `tuple` or `list` of tensors for `tensors` argument HOT 1
- fx.wrap() doesn't really work for things in torch/*
- Converting a numpy array of size larger than 32,768 to a tensor causes a segmentation fault HOT 7
- tree_map_only_ doesn't seem work as expected HOT 5
- [TensorDict - compile] dynamo unaware of `set().union()` HOT 4
- [TensorDict - compile] dynamo doesn't know about `callable(smth)` HOT 2
- [dynamo] Add support for torch.cuda.FloatTensor() HOT 1
- Module _dynamo init failed with triton folder in workdir using torch 2.4 HOT 1
- Mask not materialized for embedding backward HOT 7
- TorchDispatchMode fails on jit.trace
- Encountering HIP OOM error for the baddbmm operator when auto-tune is enabled HOT 3
- torch.cuda.empty_cache() doesn't work correctly for the first instance of nn.Module set to cuda (and a potential method to deal with that). HOT 7
- [Profiler] It is incorrect that "Self CUDA time total" contain duration of gpu_user_annotation HOT 1
- Calling saved_tensors_hooks.__exit__ inside unpack hook can lead to segfault HOT 3
- Problem with combination of jacref, vjp, tanh in torchscript
- autograd.Function x torch.compile backward stride access HOT 2
- FileNotFoundError: Could not find module when trying to load C++ extension HOT 4
- [PREEMPTIVE] Experimenting with AMAZN 2023 for `linux.4xlarge.nvidia.gpu` instances
- Ineffective Buffer Size Adjustment in Multi-Node NCCL Communication
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