llama : adjust default context size + print warnings (#10136)
* llama : adjust default context size + print warnings ggml-ci * ggml-ci : add missing gpu-layers + adjust context sizes
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3 changed files with 103 additions and 89 deletions
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@ -19440,12 +19440,26 @@ struct llama_context * llama_new_context_with_model(
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cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
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}
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LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
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LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
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LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
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LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
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LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
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LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
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const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max;
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LLAMA_LOG_INFO("%s: n_seq_max = %u\n", __func__, cparams.n_seq_max);
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LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
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LLAMA_LOG_INFO("%s: n_ctx_per_seq = %u\n", __func__, n_ctx_per_seq);
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LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
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LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
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LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
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LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
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LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
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if (n_ctx_per_seq < hparams.n_ctx_train) {
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LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
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__func__, n_ctx_per_seq, hparams.n_ctx_train);
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}
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if (n_ctx_per_seq > hparams.n_ctx_train) {
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LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
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__func__, n_ctx_per_seq, hparams.n_ctx_train);
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}
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ctx->abort_callback = params.abort_callback;
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ctx->abort_callback_data = params.abort_callback_data;
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