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examples : replace fprintf to stdout with printf (#3017)
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Cebtenzzre
GitHub
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@ -584,109 +584,109 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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}
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void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stdout, "usage: %s [options]\n", argv[0]);
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fprintf(stdout, "\n");
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fprintf(stdout, "options:\n");
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fprintf(stdout, " -h, --help show this help message and exit\n");
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fprintf(stdout, " -i, --interactive run in interactive mode\n");
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fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n");
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fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
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fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
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fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n");
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fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n");
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fprintf(stdout, " (can be specified more than once for multiple prompts).\n");
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fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n");
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fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
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fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stdout, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stdout, " prompt to start generation with (default: empty)\n");
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fprintf(stdout, " -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
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fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
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fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
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fprintf(stdout, " not supported with --interactive or other interactive options\n");
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fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
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fprintf(stdout, " --random-prompt start with a randomized prompt.\n");
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fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n");
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fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n");
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fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
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fprintf(stdout, " -f FNAME, --file FNAME\n");
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fprintf(stdout, " prompt file to start generation.\n");
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fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
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fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
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fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
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fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
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fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
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fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
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fprintf(stdout, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
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fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
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fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
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fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
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fprintf(stdout, " --mirostat N use Mirostat sampling.\n");
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fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
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fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
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fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
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fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
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fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
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fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n");
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fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
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fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
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fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
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fprintf(stdout, " --grammar-file FNAME file to read grammar from\n");
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fprintf(stdout, " --cfg-negative-prompt PROMPT\n");
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fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n");
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fprintf(stdout, " --cfg-negative-prompt-file FNAME\n");
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fprintf(stdout, " negative prompt file to use for guidance. (default: empty)\n");
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fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
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fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
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fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
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fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
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fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
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fprintf(stdout, " --no-penalize-nl do not penalize newline token\n");
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fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
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fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
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fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp);
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fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n");
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fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
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fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
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fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
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fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
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fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
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printf("usage: %s [options]\n", argv[0]);
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printf("\n");
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printf("options:\n");
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printf(" -h, --help show this help message and exit\n");
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printf(" -i, --interactive run in interactive mode\n");
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printf(" --interactive-first run in interactive mode and wait for input right away\n");
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printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n");
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printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
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printf(" -r PROMPT, --reverse-prompt PROMPT\n");
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printf(" halt generation at PROMPT, return control in interactive mode\n");
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printf(" (can be specified more than once for multiple prompts).\n");
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printf(" --color colorise output to distinguish prompt and user input from generations\n");
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printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
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printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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printf(" -p PROMPT, --prompt PROMPT\n");
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printf(" prompt to start generation with (default: empty)\n");
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printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
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printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
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printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
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printf(" not supported with --interactive or other interactive options\n");
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printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
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printf(" --random-prompt start with a randomized prompt.\n");
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printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n");
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printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n");
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printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
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printf(" -f FNAME, --file FNAME\n");
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printf(" prompt file to start generation.\n");
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printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
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printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
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printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
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printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
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printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
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printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
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printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
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printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
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printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
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printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
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printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
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printf(" --mirostat N use Mirostat sampling.\n");
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printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
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printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
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printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
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printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
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printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
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printf(" modifies the likelihood of token appearing in the completion,\n");
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printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
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printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
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printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
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printf(" --grammar-file FNAME file to read grammar from\n");
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printf(" --cfg-negative-prompt PROMPT\n");
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printf(" negative prompt to use for guidance. (default: empty)\n");
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printf(" --cfg-negative-prompt-file FNAME\n");
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printf(" negative prompt file to use for guidance. (default: empty)\n");
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printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
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printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
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printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
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printf(" --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
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printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
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printf(" --no-penalize-nl do not penalize newline token\n");
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printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
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printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
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printf(" --temp N temperature (default: %.1f)\n", (double)params.temp);
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printf(" --perplexity compute perplexity over each ctx window of the prompt\n");
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printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
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printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
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printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
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printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
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printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
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if (llama_mlock_supported()) {
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fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
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printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
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}
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if (llama_mmap_supported()) {
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fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
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printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
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}
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fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n");
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fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n");
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fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n");
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printf(" --numa attempt optimizations that help on some NUMA systems\n");
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printf(" if run without this previously, it is recommended to drop the system page cache before using this\n");
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printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n");
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#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
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fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
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fprintf(stdout, " number of layers to store in VRAM\n");
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fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
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fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
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fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
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fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
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printf(" -ngl N, --n-gpu-layers N\n");
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printf(" number of layers to store in VRAM\n");
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printf(" -ts SPLIT --tensor-split SPLIT\n");
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printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
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printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
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printf(" -lv, --low-vram don't allocate VRAM scratch buffer\n");
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#ifdef GGML_USE_CUBLAS
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fprintf(stdout, " -nommq, --no-mul-mat-q\n");
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fprintf(stdout, " use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
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fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
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printf(" -nommq, --no-mul-mat-q\n");
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printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
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printf(" Not recommended since this is both slower and uses more VRAM.\n");
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#endif // GGML_USE_CUBLAS
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#endif
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fprintf(stdout, " --mtest compute maximum memory usage\n");
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fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
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fprintf(stdout, " --verbose-prompt print prompt before generation\n");
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printf(" --mtest compute maximum memory usage\n");
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printf(" --export export the computation graph to 'llama.ggml'\n");
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printf(" --verbose-prompt print prompt before generation\n");
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fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
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fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
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fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
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fprintf(stdout, " -m FNAME, --model FNAME\n");
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fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
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fprintf(stdout, " -md FNAME, --model-draft FNAME\n");
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fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str());
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fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n");
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fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n");
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fprintf(stdout, "\n");
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printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
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printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
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printf(" -m FNAME, --model FNAME\n");
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printf(" model path (default: %s)\n", params.model.c_str());
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printf(" -md FNAME, --model-draft FNAME\n");
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printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str());
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printf(" -ld LOGDIR, --logdir LOGDIR\n");
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printf(" path under which to save YAML logs (no logging if unset)\n");
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printf("\n");
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}
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std::string gpt_random_prompt(std::mt19937 & rng) {
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16
common/log.h
16
common/log.h
@ -513,16 +513,16 @@ inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string &
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inline void log_print_usage()
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{
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fprintf(stdout, "log options:\n");
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printf("log options:\n");
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/* format
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fprintf(stdout, " -h, --help show this help message and exit\n");*/
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printf(" -h, --help show this help message and exit\n");*/
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/* spacing
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fprintf(stdout, "__-param----------------Description\n");*/
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fprintf(stdout, " --log-test Run simple logging test\n");
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fprintf(stdout, " --log-disable Disable trace logs\n");
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fprintf(stdout, " --log-enable Enable trace logs\n");
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fprintf(stdout, " --log-file Specify a log filename (without extension)\n");
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fprintf(stdout, " Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /* */
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printf("__-param----------------Description\n");*/
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printf(" --log-test Run simple logging test\n");
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printf(" --log-disable Disable trace logs\n");
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printf(" --log-enable Enable trace logs\n");
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printf(" --log-file Specify a log filename (without extension)\n");
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printf(" Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /* */
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}
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#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
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@ -76,7 +76,7 @@ bool gguf_ex_write(const std::string & fname) {
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gguf_write_to_file(ctx, fname.c_str(), false);
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fprintf(stdout, "%s: wrote file '%s;\n", __func__, fname.c_str());
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printf("%s: wrote file '%s;\n", __func__, fname.c_str());
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ggml_free(ctx_data);
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gguf_free(ctx);
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@ -93,20 +93,20 @@ bool gguf_ex_read_0(const std::string & fname) {
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struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
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fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx));
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fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
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fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
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printf("%s: version: %d\n", __func__, gguf_get_version(ctx));
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printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
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printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
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// kv
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{
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const int n_kv = gguf_get_n_kv(ctx);
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fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
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printf("%s: n_kv: %d\n", __func__, n_kv);
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for (int i = 0; i < n_kv; ++i) {
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const char * key = gguf_get_key(ctx, i);
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|
||||
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
}
|
||||
}
|
||||
|
||||
@ -116,10 +116,10 @@ bool gguf_ex_read_0(const std::string & fname) {
|
||||
|
||||
const int keyidx = gguf_find_key(ctx, findkey);
|
||||
if (keyidx == -1) {
|
||||
fprintf(stdout, "%s: find key: %s not found.\n", __func__, findkey);
|
||||
printf("%s: find key: %s not found.\n", __func__, findkey);
|
||||
} else {
|
||||
const char * key_value = gguf_get_val_str(ctx, keyidx);
|
||||
fprintf(stdout, "%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value);
|
||||
printf("%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value);
|
||||
}
|
||||
}
|
||||
|
||||
@ -127,13 +127,13 @@ bool gguf_ex_read_0(const std::string & fname) {
|
||||
{
|
||||
const int n_tensors = gguf_get_n_tensors(ctx);
|
||||
|
||||
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
|
||||
for (int i = 0; i < n_tensors; ++i) {
|
||||
const char * name = gguf_get_tensor_name (ctx, i);
|
||||
const size_t offset = gguf_get_tensor_offset(ctx, i);
|
||||
|
||||
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
}
|
||||
}
|
||||
|
||||
@ -153,20 +153,20 @@ bool gguf_ex_read_1(const std::string & fname) {
|
||||
|
||||
struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
|
||||
|
||||
fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx));
|
||||
fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
||||
fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
|
||||
printf("%s: version: %d\n", __func__, gguf_get_version(ctx));
|
||||
printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
||||
printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
|
||||
|
||||
// kv
|
||||
{
|
||||
const int n_kv = gguf_get_n_kv(ctx);
|
||||
|
||||
fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
|
||||
printf("%s: n_kv: %d\n", __func__, n_kv);
|
||||
|
||||
for (int i = 0; i < n_kv; ++i) {
|
||||
const char * key = gguf_get_key(ctx, i);
|
||||
|
||||
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
}
|
||||
}
|
||||
|
||||
@ -174,13 +174,13 @@ bool gguf_ex_read_1(const std::string & fname) {
|
||||
{
|
||||
const int n_tensors = gguf_get_n_tensors(ctx);
|
||||
|
||||
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
|
||||
for (int i = 0; i < n_tensors; ++i) {
|
||||
const char * name = gguf_get_tensor_name (ctx, i);
|
||||
const size_t offset = gguf_get_tensor_offset(ctx, i);
|
||||
|
||||
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
}
|
||||
}
|
||||
|
||||
@ -189,13 +189,13 @@ bool gguf_ex_read_1(const std::string & fname) {
|
||||
const int n_tensors = gguf_get_n_tensors(ctx);
|
||||
|
||||
for (int i = 0; i < n_tensors; ++i) {
|
||||
fprintf(stdout, "%s: reading tensor %d data\n", __func__, i);
|
||||
printf("%s: reading tensor %d data\n", __func__, i);
|
||||
|
||||
const char * name = gguf_get_tensor_name(ctx, i);
|
||||
|
||||
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
|
||||
|
||||
fprintf(stdout, "%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data);
|
||||
printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data);
|
||||
|
||||
// print first 10 elements
|
||||
const float * data = (const float *) cur->data;
|
||||
@ -219,7 +219,7 @@ bool gguf_ex_read_1(const std::string & fname) {
|
||||
}
|
||||
}
|
||||
|
||||
fprintf(stdout, "%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data));
|
||||
printf("%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data));
|
||||
|
||||
ggml_free(ctx_data);
|
||||
gguf_free(ctx);
|
||||
@ -229,7 +229,7 @@ bool gguf_ex_read_1(const std::string & fname) {
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
if (argc < 3) {
|
||||
fprintf(stdout, "usage: %s data.gguf r|w\n", argv[0]);
|
||||
printf("usage: %s data.gguf r|w\n", argv[0]);
|
||||
return -1;
|
||||
}
|
||||
|
||||
|
||||
@ -305,9 +305,9 @@ struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name)
|
||||
|
||||
struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
|
||||
if( cur == NULL ) {
|
||||
fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str());
|
||||
printf("%s: tensor '%s' not found!\n", __func__, name.c_str());
|
||||
} else {
|
||||
// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
|
||||
// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
|
||||
}
|
||||
|
||||
return cur;
|
||||
@ -333,21 +333,21 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
return false;
|
||||
}
|
||||
|
||||
fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
|
||||
fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
|
||||
fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
|
||||
printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
|
||||
printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
|
||||
printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
|
||||
|
||||
// print all kv
|
||||
#if 0
|
||||
{
|
||||
const int n_kv = gguf_get_n_kv(ggufctx);
|
||||
|
||||
fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
|
||||
printf("%s: n_kv: %d\n", __func__, n_kv);
|
||||
|
||||
for (int i = 0; i < n_kv; ++i) {
|
||||
const char * key = gguf_get_key(ggufctx, i);
|
||||
|
||||
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@ -357,21 +357,21 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
int keyidx;
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "general.name");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.description");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.author");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.license");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.file_type");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
}
|
||||
|
||||
// check required metadata
|
||||
@ -382,11 +382,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "falcon") != 0) {
|
||||
fprintf(stdout, "%s: model architecture not supported!\n", __func__);
|
||||
printf("%s: model architecture not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: gguf model architecture not found!\n", __func__);
|
||||
printf("%s: gguf model architecture not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -394,11 +394,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
keyidx = gguf_find_key(ggufctx, "falcon.tensor_data_layout");
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "jploski") != 0) {
|
||||
fprintf(stdout, "%s: model tensor data layout not supported!\n", __func__);
|
||||
printf("%s: model tensor data layout not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: gguf model tensor data layout not found!\n", __func__);
|
||||
printf("%s: gguf model tensor data layout not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -455,11 +455,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) {
|
||||
fprintf(stdout, "%s: tokenizer model not supported!\n", __func__);
|
||||
printf("%s: tokenizer model not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: tokenizer model not found!\n", __func__);
|
||||
printf("%s: tokenizer model not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -467,22 +467,22 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
|
||||
|
||||
if (tokens_keyidx == -1) {
|
||||
fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__);
|
||||
printf("%s: gpt2 tokenizer vocab not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges");
|
||||
|
||||
if (merges_keyidx == -1) {
|
||||
fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__);
|
||||
printf("%s: gpt2 tokenizer merges not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx);
|
||||
hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx);
|
||||
|
||||
fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
|
||||
fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
|
||||
printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
|
||||
printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
|
||||
|
||||
for (size_t i = 0; i < hparams.n_vocab; i++) {
|
||||
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
|
||||
@ -523,12 +523,12 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
|
||||
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
|
||||
|
||||
if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); }
|
||||
if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); }
|
||||
if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); }
|
||||
if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); }
|
||||
if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); }
|
||||
if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); }
|
||||
if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); }
|
||||
if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); }
|
||||
if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); }
|
||||
if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); }
|
||||
if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); }
|
||||
if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); }
|
||||
|
||||
}
|
||||
|
||||
@ -543,13 +543,13 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_
|
||||
{
|
||||
const int n_tensors = gguf_get_n_tensors(ggufctx);
|
||||
|
||||
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
|
||||
for (int i = 0; i < n_tensors; ++i) {
|
||||
const char * name = gguf_get_tensor_name (ggufctx, i);
|
||||
const size_t offset = gguf_get_tensor_offset(ggufctx, i);
|
||||
|
||||
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -318,9 +318,9 @@ struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name)
|
||||
|
||||
struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
|
||||
if( cur == NULL ) {
|
||||
fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str());
|
||||
printf("%s: tensor '%s' not found!\n", __func__, name.c_str());
|
||||
} else {
|
||||
// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
|
||||
// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
|
||||
}
|
||||
|
||||
return cur;
|
||||
@ -346,21 +346,21 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
return false;
|
||||
}
|
||||
|
||||
fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
|
||||
fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
|
||||
fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
|
||||
printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
|
||||
printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
|
||||
printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
|
||||
|
||||
// print all kv
|
||||
#if 0
|
||||
{
|
||||
const int n_kv = gguf_get_n_kv(ggufctx);
|
||||
|
||||
fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
|
||||
printf("%s: n_kv: %d\n", __func__, n_kv);
|
||||
|
||||
for (int i = 0; i < n_kv; ++i) {
|
||||
const char * key = gguf_get_key(ggufctx, i);
|
||||
|
||||
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@ -370,21 +370,21 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
int keyidx;
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "general.name");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.description");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.author");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.license");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.file_type");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
}
|
||||
|
||||
// check required metadata
|
||||
@ -395,11 +395,11 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gptneox") != 0) {
|
||||
fprintf(stdout, "%s: model architecture not supported!\n", __func__);
|
||||
printf("%s: model architecture not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: gguf model architecture not found!\n", __func__);
|
||||
printf("%s: gguf model architecture not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -456,11 +456,11 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) {
|
||||
fprintf(stdout, "%s: tokenizer model not supported!\n", __func__);
|
||||
printf("%s: tokenizer model not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: tokenizer model not found!\n", __func__);
|
||||
printf("%s: tokenizer model not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -468,22 +468,22 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
|
||||
|
||||
if (tokens_keyidx == -1) {
|
||||
fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__);
|
||||
printf("%s: gpt2 tokenizer vocab not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges");
|
||||
|
||||
if (merges_keyidx == -1) {
|
||||
fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__);
|
||||
printf("%s: gpt2 tokenizer merges not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx);
|
||||
hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx);
|
||||
|
||||
fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
|
||||
fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
|
||||
printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
|
||||
printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
|
||||
|
||||
for (size_t i = 0; i < hparams.n_vocab; i++) {
|
||||
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
|
||||
@ -524,12 +524,12 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
|
||||
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
|
||||
|
||||
if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); }
|
||||
if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); }
|
||||
if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); }
|
||||
if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); }
|
||||
if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); }
|
||||
if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); }
|
||||
if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); }
|
||||
if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); }
|
||||
if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); }
|
||||
if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); }
|
||||
if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); }
|
||||
if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); }
|
||||
}
|
||||
|
||||
|
||||
@ -543,13 +543,13 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
|
||||
{
|
||||
const int n_tensors = gguf_get_n_tensors(ggufctx);
|
||||
|
||||
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
|
||||
for (int i = 0; i < n_tensors; ++i) {
|
||||
const char * name = gguf_get_tensor_name (ggufctx, i);
|
||||
const size_t offset = gguf_get_tensor_offset(ggufctx, i);
|
||||
|
||||
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -165,26 +165,26 @@ static const cmd_params cmd_params_defaults = {
|
||||
};
|
||||
|
||||
static void print_usage(int /* argc */, char ** argv) {
|
||||
fprintf(stdout, "usage: %s [options]\n", argv[0]);
|
||||
fprintf(stdout, "\n");
|
||||
fprintf(stdout, "options:\n");
|
||||
fprintf(stdout, " -h, --help\n");
|
||||
fprintf(stdout, " -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
|
||||
fprintf(stdout, " -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
|
||||
fprintf(stdout, " -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
|
||||
fprintf(stdout, " -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
|
||||
fprintf(stdout, " --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
|
||||
fprintf(stdout, " -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
|
||||
fprintf(stdout, " -ngl N, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
|
||||
fprintf(stdout, " -mg i, --main-gpu <n> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
|
||||
fprintf(stdout, " -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
|
||||
fprintf(stdout, " -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
|
||||
fprintf(stdout, " -ts, --tensor_split <ts0/ts1/..> \n");
|
||||
fprintf(stdout, " -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
|
||||
fprintf(stdout, " -o, --output <csv|json|md|sql> (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql");
|
||||
fprintf(stdout, " -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
|
||||
fprintf(stdout, "\n");
|
||||
fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
|
||||
printf("usage: %s [options]\n", argv[0]);
|
||||
printf("\n");
|
||||
printf("options:\n");
|
||||
printf(" -h, --help\n");
|
||||
printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
|
||||
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
|
||||
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
|
||||
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
|
||||
printf(" --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
|
||||
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
|
||||
printf(" -ngl N, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
|
||||
printf(" -mg i, --main-gpu <n> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
|
||||
printf(" -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
|
||||
printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
|
||||
printf(" -ts, --tensor_split <ts0/ts1/..> \n");
|
||||
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
|
||||
printf(" -o, --output <csv|json|md|sql> (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql");
|
||||
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
|
||||
printf("\n");
|
||||
printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
|
||||
|
||||
}
|
||||
|
||||
|
||||
@ -118,7 +118,7 @@ static void server_log(const char *level, const char *function, int line,
|
||||
}
|
||||
|
||||
const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
|
||||
fprintf(stdout, "%.*s\n", (int)str.size(), str.data());
|
||||
printf("%.*s\n", (int)str.size(), str.data());
|
||||
fflush(stdout);
|
||||
}
|
||||
|
||||
@ -694,50 +694,50 @@ struct llama_server_context
|
||||
static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
||||
const server_params &sparams)
|
||||
{
|
||||
fprintf(stdout, "usage: %s [options]\n", argv0);
|
||||
fprintf(stdout, "\n");
|
||||
fprintf(stdout, "options:\n");
|
||||
fprintf(stdout, " -h, --help show this help message and exit\n");
|
||||
fprintf(stdout, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
|
||||
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
||||
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
||||
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
|
||||
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
|
||||
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
||||
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
||||
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
printf("usage: %s [options]\n", argv0);
|
||||
printf("\n");
|
||||
printf("options:\n");
|
||||
printf(" -h, --help show this help message and exit\n");
|
||||
printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
|
||||
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
||||
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
||||
printf(" --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
|
||||
printf(" --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
|
||||
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
||||
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
||||
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
if (llama_mlock_supported())
|
||||
{
|
||||
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
||||
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
||||
}
|
||||
if (llama_mmap_supported())
|
||||
{
|
||||
fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
||||
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
||||
}
|
||||
fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n");
|
||||
printf(" --numa attempt optimizations that help on some NUMA systems\n");
|
||||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stdout, " number of layers to store in VRAM\n");
|
||||
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
|
||||
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
|
||||
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
|
||||
printf(" -ngl N, --n-gpu-layers N\n");
|
||||
printf(" number of layers to store in VRAM\n");
|
||||
printf(" -ts SPLIT --tensor-split SPLIT\n");
|
||||
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
printf(" -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
printf(" -nommq, --no-mul-mat-q\n");
|
||||
printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
|
||||
printf(" Not recommended since this is both slower and uses more VRAM.\n");
|
||||
#endif
|
||||
fprintf(stdout, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
|
||||
fprintf(stdout, " -a ALIAS, --alias ALIAS\n");
|
||||
fprintf(stdout, " set an alias for the model, will be added as `model` field in completion response\n");
|
||||
fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
||||
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
||||
fprintf(stdout, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
|
||||
fprintf(stdout, " --port PORT port to listen (default (default: %d)\n", sparams.port);
|
||||
fprintf(stdout, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
|
||||
fprintf(stdout, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
|
||||
fprintf(stdout, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
||||
fprintf(stdout, "\n");
|
||||
printf(" -m FNAME, --model FNAME\n");
|
||||
printf(" model path (default: %s)\n", params.model.c_str());
|
||||
printf(" -a ALIAS, --alias ALIAS\n");
|
||||
printf(" set an alias for the model, will be added as `model` field in completion response\n");
|
||||
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
||||
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
||||
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
|
||||
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
|
||||
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
|
||||
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
|
||||
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
@ -1595,7 +1595,7 @@ int main(int argc, char **argv)
|
||||
svr.set_base_dir(sparams.public_path);
|
||||
|
||||
// to make it ctrl+clickable:
|
||||
fprintf(stdout, "\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
|
||||
printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
|
||||
|
||||
LOG_INFO("HTTP server listening", {
|
||||
{"hostname", sparams.hostname},
|
||||
|
||||
Loading…
Reference in New Issue
Block a user