llama.cpp/ggml/src/ggml-cuda/cross-entropy-loss.cuh

#include "common.cuh"

#define CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE 256

void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

void ggml_cuda_cross_entropy_loss_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
sync : ggml 2024-08-27 22:01:45 +03:00			`#include "common.cuh"`

			`#define CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE 256`

			`void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor * dst);`
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 --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com> 2024-09-20 19:04:44 +03:00
			`void ggml_cuda_cross_entropy_loss_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);`