llama-fit-params: keep explicit --ctx-size 0 (#19070)
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@ -1231,6 +1231,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
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[](common_params & params, int value) {
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params.n_ctx = value;
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if (value == 0) {
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// disable context reduction in llama_params_fit if the user explicitly requests the full context size:
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params.fit_params_min_ctx = UINT32_MAX;
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}
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}
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).set_env("LLAMA_ARG_CTX_SIZE"));
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add_opt(common_arg(
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@ -489,6 +489,7 @@ extern "C" {
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// - returns true if the parameters could be successfully modified to fit device memory
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// - this function is NOT thread safe because it modifies the global llama logger state
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// - only parameters that have the same value as in llama_default_model_params are modified
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// with the exception of the context size which is modified if and only if equal to 0
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LLAMA_API enum llama_params_fit_status llama_params_fit(
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const char * path_model,
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struct llama_model_params * mparams,
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@ -311,8 +311,12 @@ static void llama_params_fit_impl(
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__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
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}
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} else {
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LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
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__func__, hp_nct, n_ctx_min);
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if (n_ctx_min == UINT32_MAX) {
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LLAMA_LOG_INFO("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
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} else {
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LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
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__func__, hp_nct, n_ctx_min);
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}
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}
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} else {
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LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
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@ -36,7 +36,7 @@ int main(int argc, char ** argv) {
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LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__);
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common_log_flush(common_log_main());
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printf("-c %" PRIu32 " -ngl %" PRIu32, cparams.n_ctx, mparams.n_gpu_layers);
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printf("-c %" PRIu32 " -ngl %" PRIi32, cparams.n_ctx, mparams.n_gpu_layers);
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size_t nd = llama_max_devices();
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while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) {
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