Related Issues (20)
- erroneous symbols in initial trace
- Fusion pass should drop unused arg from parent symbols. HOT 1
- missing slice check in prologue HOT 4
- Add `post_optimization_transform` to `thunder.jit` HOT 2
- Applying Thunder on torch.fx.GraphModule from Dynamo fails
- [distributed][Tensor Parallelism] Support meta device params
- grad transform `forward_and_backward_from_trace` is not handling NumberProxy properly in saved_for_backward HOT 12
- TypeError: Missing a required argument with thunder.jit in NeMo SD ResBlock HOT 3
- thunder/tests/test_nvfuser_remat.py::test_find_cut_dropout_nvfuser_cuda_None is over sensitive. HOT 7
- CSE should always modify subsymbols
- jit : if torch.device is an input to jitted fn, it's value is not checked in prologue.
- TransformerEngine: Communication and Computation is not overlapped in backward pass with FSDP Zero3 HOT 5
- [distributed] Enable transformed modules to load state dicts of the originals HOT 2
- reenable testing cudnn SDPA with PyTorch dev version / 2.4.0a0+ HOT 3
- Returning `dtype` or `device` from jitted function returns thunder's dtype or device (not torch.{dtype/device}). HOT 4
- Find a way to properly sort the communication operators for zero2/zero3
- `column_parallel` / `row_parallel` fail to transform `litgpt.model.CausalSelfAttention` with `TypeError: unshable type: 'TensorProxy'`
- nvFuser executor doesn't support prims.sum with symbolic dimensions HOT 3
- NotImplementedError: requires_grad=True is not yet supported within thunder.compile HOT 5
- CUDA error: CUDA_ERROR_ILLEGAL_ADDRESS failed when training falcon-7b HOT 11
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