CUDA full GPU acceleration, KV cache in VRAM (#1827)
* Fixed CUDA RoPE * ggml_cuda_mul_mat_vec_p021 * ggml_cuda_scale * ggml_cuda_diag_mask_inf * ggml_is_permuted * ggml_cuda_cpy * flatten rows for ggml_cuda_op * Added a --low-vram option * Fixed Windows performance * Fixed LLAMA_CUDA_DMMV_Y > 1 for WizardLM
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11 changed files with 853 additions and 149 deletions
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@ -21,15 +21,16 @@
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int32_t get_num_physical_cores();
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_gpu_layers = 0; // number of layers to store in VRAM
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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int32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_gpu_layers = 0; // number of layers to store in VRAM
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
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bool low_vram = 0; // if true, reduce VRAM usage at the cost of performance
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// sampling parameters
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std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
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