Comments (1)
Yes. Running RETURNN with the task 'forward' will produce log-posteriors, i.e. the logarithm of the final softmax layer.
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Related Issues (20)
- RF CausalAttention get_sequence_mask_broadcast bug HOT 3
- PT potential CUDA mem leak? HOT 2
- `psutil` `_read_smaps_file` takes lots of time HOT 4
- Hang in `uvm_ioctl` in kernel HOT 2
- PyTorch CUDA OOM in distributed training HOT 7
- PyTorch distributed training, could not unlink the shared memory file
- PyTorch distributed training, hang in `all_reduce(_has_data ...`, after exception, Timed out waiting 1800000ms for send operation to complete HOT 4
- PyTorch training, some epochs very slow HOT 5
- PyTorch training RuntimeError: cuFFT error: CUFFT_INTERNAL_ERROR HOT 2
- PyTorch collect model statistics
- PyTorch recover after CUDA OOM with restart does not work with CUDA HOT 3
- PyTorch distributed training CPU OOM with sync_on_cpu HOT 1
- Support `torch.compile` for RF
- RF backend: PyTorch code
- Different effective learning rate reported over gpus HOT 11
- CUDA error: initialization error HOT 3
- MultiProcDataset inside PyTorch DataLoader with num_workers>0, multiple issues HOT 4
- RuntimeError: CUDA error: unspecified launch failure HOT 2
- NonDaemonicSpawnProcess hangs at exit HOT 2
- High memory usage with datasets (specifically when multi procs are used)
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