Related Issues (20)
- dtype inconsistencies when dividing/rounding tensors
- Implement GroupNorm to invoke APEX GroupNorm for NeMo Stable Diffusion AutoEncoder performance HOT 14
- Dynamic shape needs to be modeled in trace
- OOM errors for Gemma-7, pythia-12b, Llama-2-13b-hf and Nous-Hermes-13b with FSDP zero3 and 2x8 H100 HOT 5
- Refine recording of source locations HOT 6
- Nous-Hermes-13b on 1x8 H100 FSDP zero2 with thunder_cudnn is 23% slower than with inductor HOT 5
- fsdp(jit(...)) transform can use more memory compared to jit(fsdp(...)) HOT 4
- nvfuserex has problems taking getitem. HOT 4
- load/save_state_dict hooks for early transforms HOT 3
- Training Llama-2-13b-hf on 2x8 H100 with Thunder inductor is 47% slower than with Inductor HOT 4
- FP8 Linear and conv with cudnn HOT 1
- Support RN50 BatchNorm fusions with cudnn
- CI : PyTorch nightly CI failing with `FutureWarning: is_compiling is deprecated. Use torch.compiler.is_compiling() instead.`
- Distill API for module transformations from distributed / quantization uses of ThunderModule attributes
- TransformerEngine API changed and caused test failure `AttributeError: 'TELinear' object has no attribute 'fp8_weight_shapes'`
- FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. HOT 2
- [RFC] Option to make a trace easier to interpret HOT 5
- Thunder object's `__repr__` should indicate what object they are (TensorProxy and others)
- nvfuser failure HOT 3
- NVFuser error adding thunder.jit to UNet model of NeMo Stable Diffusion HOT 6
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