llama/ggml: add LLM training support
more compact progress bar refactor: llama_prepare_sbatch/ubatch llama_save_model_to_file gqa_mode arg for repeat_back llama_opt_param_filter ggml_graph_dup force_grads refactor ggml_opt, fix test-opt
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26 changed files with 1294 additions and 339 deletions
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@ -2177,3 +2177,19 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
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return result;
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}
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ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
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const int64_t ne_datapoint = llama_n_ctx(ctx);
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const int64_t ndata = (tokens.size() - ne_datapoint - 1) / stride;
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ggml_opt_dataset_t result = ggml_opt_dataset_init(
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GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);
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llama_token * data = (llama_token *) ggml_opt_dataset_data(result)->data;
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llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;
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for (int64_t idata = 0; idata < ndata; ++idata) {
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memcpy(data + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
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memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
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}
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return result;
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}
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