This PR introduces small fixes around fp16 training and batch fitting:
* Multi-loss casts type to first loss-type before accumulation (aborted before due to missing cast)
* Throw `ShapeSizeException` if total expanded shape size exceeds numeric capacity of the maximum int value (2^31-1)
* During mini-batch-fitting, catch `ShapeSizeException` and use another sizing hint. Aborts outside mini-batch-fitting.
* Negative `--workspace -N` value allocates workspace as total available GPU memory minus N megabytes.
This PR sets default parameters for cost-scaling to 8.f 10000 1.f 8.f, i.e. when scaling scale by 8 and do not try to automatically scale up or down. This seems most stable than variable cost-scaling with larger numbers that was the default before.
This PR fixes case augmentation with multi-threaded reading. The solution is to not look at iterator::pos_ in lazy processing, rather pass it as an argument to the lazy function.
This replaces dense alignment storage and training with a sparse representation. Training speed with guided alignment matches now nearly normal training speed, regaining about 25% speed.
This is no. 1 of 2 PRs. The next one will introduce a new guided-alignment training scheme with better alignment accuracy.
* Add GCC 11 support
Some C++ Standard Library headers have been changed to no longer include other headers that they do need to depend on. As such, C++ programs that used standard library components without including the right headers will no longer compile.
The following headers are used less widely in libstdc++ and may need to be included explicitly when compiled with GCC 11:
<limits> (for std::numeric_limits)
<memory> (for std::unique_ptr, std::shared_ptr etc.)
<utility> (for std::pair, std::tuple_size, std::index_sequence etc.)
<thread> (for members of namespace std::this_thread.)
Co-authored-by: Roman Grundkiewicz <rgrundkiewicz@gmail.com>
Support for RMSNorm as drop-in replace for LayerNorm from _Biao Zhang; Rico Sennrich (2019). Root Mean Square Layer Normalization_. Enabled in Transformer model via `--transformer-postprocess dar` instead of `dan`.
This PR adds cleaner suppression of unwanted output words. We identified a situation where SPM with byte-fallback can generate random bytes with output-sampling.
That is particularly harmful when that random bytes happens to be a newline symbol. Here we suppress newline in output unless explicitly wanted.
