Johannes Gäßler
424c5d00a9
ggml/examples: add backend support for numerical optimization (ggml/949)
...
* CUDA eval works
* stochastic gradient descent op
* Adam except decay
* CUDA CROSS_ENTROPY_LOSS_BACK
* CUDA mnist-fc training works
* backend CLI arg
* refactor gguf load
* remove sched from opt_step_adam
* implement l1 regularization (weight decay)
* extra call to add optimizer
* initialize gradients with ggml_graph_reset
* gradient accumulation
* increment iter per eval instead of epoch
* adjust backend interfaces
* fix ggml_graph_reset without backend
* fix ggml graph export/import
* fixup
* rename
* revert ggml_opt changes
* more general CUDA repeat_back
* update documentation, fix CNN
* validation split
* add clarifying comment
* optimize PyTorch training
* adjust buffer size, thread count
* fix 0.0f validation split
* Update examples/mnist/mnist-common.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix gradient accumulation
* tensor flag for accumulators -> tensor hash set
* Update include/ggml.h
Co-authored-by: slaren <slarengh@gmail.com>
* Update tests/test-backend-ops.cpp
Co-authored-by: slaren <slarengh@gmail.com>
* Update tests/test-backend-ops.cpp
Co-authored-by: slaren <slarengh@gmail.com>
* fix test prints
* Update src/ggml-backend.c
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* better CUDA support for noncontiguous out_prod
* add comment
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-09-20 21:15:05 +03:00