diff options
Diffstat (limited to 'examples')
48 files changed, 3828 insertions, 1907 deletions
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 161960b..a7b2677 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -13,6 +13,10 @@ set(TARGET common) add_library(${TARGET} OBJECT common.h common.cpp + console.h + console.cpp + grammar-parser.h + grammar-parser.cpp ) if (BUILD_SHARED_LIBS) diff --git a/examples/Miku.sh b/examples/Miku.sh index c44d9ae..b9174b4 100755 --- a/examples/Miku.sh +++ b/examples/Miku.sh @@ -2,21 +2,21 @@ set -e AI_NAME="${AI_NAME:-Miku}" -MODEL="${MODEL:-./models/gpt4all-7B/gpt4all-lora-unfiltered-quantized.bin}" +MODEL="${MODEL:-./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin}" USER_NAME="${USER_NAME:-Anon}" # Uncomment and adjust to the number of CPU cores you want to use. #N_THREAD="${N_THREAD:-4}" +CTX_SIZE="${CTX_SIZE:-4096}" N_PREDICTS="${N_PREDICTS:-4096}" GEN_OPTIONS=(--batch_size 1024 ---ctx_size 2048 +--ctx_size "$CTX_SIZE" --keep -1 --repeat_last_n 256 --repeat_penalty 1.17647 ---temp 0.7 ---top_k 40 ---top_p 0.5) +--temp 0.6 +--mirostat 2) if [ -n "$N_THREAD" ]; then GEN_OPTIONS+=(--threads "$N_THREAD") @@ -24,16 +24,17 @@ fi ./main "${GEN_OPTIONS[@]}" \ --model "$MODEL" \ + --in-prefix " " \ + --in-suffix "${AI_NAME}:" \ --n_predict "$N_PREDICTS" \ --color --interactive \ --reverse-prompt "${USER_NAME}:" \ - --prompt " -This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer. + --prompt "This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer. ${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next. ${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct, she will ask the user for help. ${AI_NAME} is a very helpful AI and will help the user with anything they need. She is also very friendly and will try to make the user feel better if they are sad. ${AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life. She will also try to make the user like her. -The conversation is only between ${USER_NAME} and ${AI_NAME} +The conversation is only between ${USER_NAME} and ${AI_NAME}. The conversation is only through text, so ${AI_NAME} can't see ${USER_NAME}'s face or hear his voice. ${AI_NAME} can only communicate through text, so she can't send images or videos. diff --git a/examples/baby-llama/CMakeLists.txt b/examples/baby-llama/CMakeLists.txt index d2ce363..7b70227 100644 --- a/examples/baby-llama/CMakeLists.txt +++ b/examples/baby-llama/CMakeLists.txt @@ -1,4 +1,5 @@ set(TARGET baby-llama) add_executable(${TARGET} baby-llama.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 4965881..6fa55b3 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -8,6 +8,12 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +#ifdef LLAMA_DEFAULT_RMS_EPS +static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; +#else +static const float rms_norm_eps = 5e-6f; +#endif + float frand() { return (float)rand()/(float)RAND_MAX; } @@ -562,7 +568,7 @@ struct ggml_tensor * forward( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // cur = attention_norm*cur cur = ggml_mul(ctx0, @@ -685,7 +691,7 @@ struct ggml_tensor * forward( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); // cur = ffn_norm*cur // cur shape [n_embd,N,1,1] @@ -729,7 +735,7 @@ struct ggml_tensor * forward( { // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // inpL = norm*inpL // inpL shape [n_embd,N,1,1] @@ -817,7 +823,7 @@ struct ggml_tensor * forward_batch( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = attention_norm*cur @@ -981,7 +987,7 @@ struct ggml_tensor * forward_batch( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = ffn_norm*cur @@ -1034,7 +1040,7 @@ struct ggml_tensor * forward_batch( { // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(inpL, n_embd, N*n_batch); // inpL = norm*inpL @@ -1104,7 +1110,7 @@ struct ggml_tensor * forward_lora( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // cur = attention_norm*cur cur = ggml_mul(ctx0, @@ -1251,7 +1257,7 @@ struct ggml_tensor * forward_lora( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); // cur = ffn_norm*cur // cur shape [n_embd,N,1,1] @@ -1295,7 +1301,7 @@ struct ggml_tensor * forward_lora( { // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // inpL = norm*inpL // inpL shape [n_embd,N,1,1] diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt index 0376961..3f34153 100644 --- a/examples/benchmark/CMakeLists.txt +++ b/examples/benchmark/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET benchmark) add_executable(${TARGET} benchmark-matmult.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/common.cpp b/examples/common.cpp index fd551c9..4d3ba9b 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -25,7 +25,6 @@ #else #include <sys/ioctl.h> #include <unistd.h> -#include <wchar.h> #endif #if defined(_MSC_VER) @@ -117,6 +116,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_threads = std::stoi(argv[i]); + if (params.n_threads <= 0) { + params.n_threads = std::thread::hardware_concurrency(); + } } else if (arg == "-p" || arg == "--prompt") { if (++i >= argc) { invalid_param = true; @@ -168,6 +170,36 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_ctx = std::stoi(argv[i]); + } else if (arg == "-gqa" || arg == "--gqa") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_gqa = std::stoi(argv[i]); + } else if (arg == "-eps" || arg == "--rms-norm-eps") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rms_norm_eps = std::stof(argv[i]); + } else if (arg == "--rope-freq-base") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_base = std::stof(argv[i]); + } else if (arg == "--rope-freq-scale") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_scale = std::stof(argv[i]); + } else if (arg == "--rope-scale") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_scale = 1.0f/std::stof(argv[i]); } else if (arg == "--memory-f32") { params.memory_f16 = false; } else if (arg == "--top-p") { @@ -248,12 +280,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.cfg_scale = std::stof(argv[i]); - } else if (arg == "--cfg-smooth-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.cfg_smooth_factor = std::stof(argv[i]); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; @@ -267,6 +293,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_keep = std::stoi(argv[i]); + } else if (arg == "--chunks") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_chunks = std::stoi(argv[i]); } else if (arg == "-m" || arg == "--model") { if (++i >= argc) { invalid_param = true; @@ -285,6 +317,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.lora_adapter = argv[i]; + params.use_mmap = false; } else if (arg == "--lora-base") { if (++i >= argc) { invalid_param = true; @@ -301,6 +334,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.instruct = true; } else if (arg == "--multiline-input") { params.multiline_input = true; + } else if (arg == "--simple-io") { + params.simple_io = true; } else if (arg == "--color") { params.use_color = true; } else if (arg == "--mlock") { @@ -324,7 +359,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { #ifdef GGML_USE_CUBLAS params.main_gpu = std::stoi(argv[i]); #else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n"); + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n"); #endif } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { @@ -348,13 +383,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } } #else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n"); + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n"); +#endif // GGML_USE_CUBLAS + } else if (arg == "--mul-mat-q" || arg == "-mmq") { +#ifdef GGML_USE_CUBLAS + params.mul_mat_q = true; +#else + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n"); #endif // GGML_USE_CUBLAS } else if (arg == "--low-vram" || arg == "-lv") { #ifdef GGML_USE_CUBLAS params.low_vram = true; #else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n"); + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n"); #endif // GGML_USE_CUBLAS } else if (arg == "--no-mmap") { params.use_mmap = false; @@ -374,6 +415,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.antiprompt.push_back(argv[i]); } else if (arg == "--perplexity") { params.perplexity = true; + } else if (arg == "--hellaswag") { + params.hellaswag = true; + } else if (arg == "--hellaswag-tasks") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.hellaswag_tasks = std::stoi(argv[i]); } else if (arg == "--ignore-eos") { params.logit_bias[llama_token_eos()] = -INFINITY; } else if (arg == "--no-penalize-nl") { @@ -402,6 +451,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { exit(0); } else if (arg == "--random-prompt") { params.random_prompt = true; + } else if (arg == "--in-prefix-bos") { + params.input_prefix_bos = true; } else if (arg == "--in-prefix") { if (++i >= argc) { invalid_param = true; @@ -414,6 +465,28 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.input_suffix = argv[i]; + } else if (arg == "--grammar") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.grammar = argv[i]; + } else if (arg == "--grammar-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + break; + } + std::copy( + std::istreambuf_iterator<char>(file), + std::istreambuf_iterator<char>(), + std::back_inserter(params.grammar) + ); } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); gpt_print_usage(argc, argv, default_params); @@ -443,88 +516,102 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stderr, "usage: %s [options]\n", argv[0]); - fprintf(stderr, "\n"); - fprintf(stderr, "options:\n"); - fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " -i, --interactive run in interactive mode\n"); - fprintf(stderr, " --interactive-first run in interactive mode and wait for input right away\n"); - fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); - fprintf(stderr, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); - fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n"); - fprintf(stderr, " halt generation at PROMPT, return control in interactive mode\n"); - fprintf(stderr, " (can be specified more than once for multiple prompts).\n"); - fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n"); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stderr, " -p PROMPT, --prompt PROMPT\n"); - fprintf(stderr, " prompt to start generation with (default: empty)\n"); - fprintf(stderr, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stderr, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - fprintf(stderr, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - fprintf(stderr, " not supported with --interactive or other interactive options\n"); - fprintf(stderr, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - fprintf(stderr, " --random-prompt start with a randomized prompt.\n"); - fprintf(stderr, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - fprintf(stderr, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - fprintf(stderr, " -f FNAME, --file FNAME\n"); - fprintf(stderr, " prompt file to start generation.\n"); - fprintf(stderr, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); - fprintf(stderr, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); - fprintf(stderr, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); - fprintf(stderr, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); - fprintf(stderr, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); - fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); - fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); - fprintf(stderr, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); - fprintf(stderr, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); - fprintf(stderr, " --mirostat N use Mirostat sampling.\n"); - fprintf(stderr, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - fprintf(stderr, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); - fprintf(stderr, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); - fprintf(stderr, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); - fprintf(stderr, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - fprintf(stderr, " modifies the likelihood of token appearing in the completion,\n"); - fprintf(stderr, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - fprintf(stderr, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - fprintf(stderr, " --cfg-negative-prompt PROMPT \n"); - fprintf(stderr, " negative prompt to use for guidance. (default: empty)\n"); - fprintf(stderr, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); - fprintf(stderr, " --cfg-smooth-factor N smooth factor between old and new logits (default: %f, 1.0 = no smoothing)\n", params.cfg_smooth_factor); - fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - fprintf(stderr, " --no-penalize-nl do not penalize newline token\n"); - fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n"); - fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp); - fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stderr, " --perplexity compute perplexity over the prompt\n"); - fprintf(stderr, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, "usage: %s [options]\n", argv[0]); + fprintf(stdout, "\n"); + fprintf(stdout, "options:\n"); + fprintf(stdout, " -h, --help show this help message and exit\n"); + fprintf(stdout, " -i, --interactive run in interactive mode\n"); + fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n"); + fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); + fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); + fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n"); + fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n"); + fprintf(stdout, " (can be specified more than once for multiple prompts).\n"); + fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n"); + fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); + fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + fprintf(stdout, " -p PROMPT, --prompt PROMPT\n"); + fprintf(stdout, " prompt to start generation with (default: empty)\n"); + fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); + fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); + fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); + fprintf(stdout, " not supported with --interactive or other interactive options\n"); + fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); + fprintf(stdout, " --random-prompt start with a randomized prompt.\n"); + fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); + fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); + fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); + fprintf(stdout, " -f FNAME, --file FNAME\n"); + fprintf(stdout, " prompt file to start generation.\n"); + fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); + fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa); + fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps); + fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); + fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); + fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); + fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); + 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); + fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); + fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); + fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); + fprintf(stdout, " --mirostat N use Mirostat sampling.\n"); + fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); + fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); + fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); + fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); + fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); + fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n"); + fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); + fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); + fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); + fprintf(stdout, " --grammar-file FNAME file to read grammar from\n"); + fprintf(stdout, " --cfg-negative-prompt PROMPT \n"); + fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n"); + fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); + fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale); + fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base); + fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale); + fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); + fprintf(stdout, " --no-penalize-nl do not penalize newline token\n"); + 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"); + fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp); + fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n"); + fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); + fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); + fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { - fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); + fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - fprintf(stderr, " --numa attempt optimizations that help on some NUMA systems\n"); - fprintf(stderr, " if run without this previously, it is recommended to drop the system page cache before using this\n"); - fprintf(stderr, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); + fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n"); + fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n"); + fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, " -ngl N, --n-gpu-layers N\n"); - fprintf(stderr, " number of layers to store in VRAM\n"); - fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n"); - fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" ); - fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n" ); + 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, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" ); + fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" ); + fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" ); #endif - fprintf(stderr, " --mtest compute maximum memory usage\n"); - fprintf(stderr, " --export export the computation graph to 'llama.ggml'\n"); - fprintf(stderr, " --verbose-prompt print prompt before generation\n"); - fprintf(stderr, " --lora FNAME apply LoRA adapter\n"); - fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - fprintf(stderr, " -m FNAME, --model FNAME\n"); - fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); - fprintf(stderr, "\n"); + fprintf(stdout, " --mtest compute maximum memory usage\n"); + fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n"); + fprintf(stdout, " --verbose-prompt print prompt before generation\n"); + fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\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, " -m FNAME, --model FNAME\n"); + fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, "\n"); } std::string gpt_random_prompt(std::mt19937 & rng) { @@ -560,18 +647,23 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { auto lparams = llama_context_default_params(); - lparams.n_ctx = params.n_ctx; - lparams.n_batch = params.n_batch; - lparams.n_gpu_layers = params.n_gpu_layers; - lparams.main_gpu = params.main_gpu; - memcpy(lparams.tensor_split, params.tensor_split, LLAMA_MAX_DEVICES*sizeof(float)); - lparams.low_vram = params.low_vram; - lparams.seed = params.seed; - lparams.f16_kv = params.memory_f16; - lparams.use_mmap = params.use_mmap; - lparams.use_mlock = params.use_mlock; - lparams.logits_all = params.perplexity; - lparams.embedding = params.embedding; + lparams.n_ctx = params.n_ctx; + lparams.n_batch = params.n_batch; + lparams.n_gqa = params.n_gqa; + lparams.rms_norm_eps = params.rms_norm_eps; + lparams.n_gpu_layers = params.n_gpu_layers; + lparams.main_gpu = params.main_gpu; + lparams.tensor_split = params.tensor_split; + lparams.low_vram = params.low_vram; + lparams.mul_mat_q = params.mul_mat_q; + lparams.seed = params.seed; + lparams.f16_kv = params.memory_f16; + lparams.use_mmap = params.use_mmap; + lparams.use_mlock = params.use_mlock; + lparams.logits_all = params.perplexity; + lparams.embedding = params.embedding; + lparams.rope_freq_base = params.rope_freq_base; + lparams.rope_freq_scale = params.rope_freq_scale; return lparams; } @@ -607,376 +699,3 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par return std::make_tuple(model, lctx); } - -void console_init(console_state & con_st) { -#if defined(_WIN32) - // Windows-specific console initialization - DWORD dwMode = 0; - con_st.hConsole = GetStdHandle(STD_OUTPUT_HANDLE); - if (con_st.hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(con_st.hConsole, &dwMode)) { - con_st.hConsole = GetStdHandle(STD_ERROR_HANDLE); - if (con_st.hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(con_st.hConsole, &dwMode))) { - con_st.hConsole = NULL; - } - } - if (con_st.hConsole) { - // Enable ANSI colors on Windows 10+ - if (con_st.use_color && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)) { - SetConsoleMode(con_st.hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING); - } - // Set console output codepage to UTF8 - SetConsoleOutputCP(CP_UTF8); - } - HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE); - if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) { - // Set console input codepage to UTF16 - _setmode(_fileno(stdin), _O_WTEXT); - - // Turn off ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT) - dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT); - SetConsoleMode(hConIn, dwMode); - } -#else - // POSIX-specific console initialization - struct termios new_termios; - tcgetattr(STDIN_FILENO, &con_st.prev_state); - new_termios = con_st.prev_state; - new_termios.c_lflag &= ~(ICANON | ECHO); - new_termios.c_cc[VMIN] = 1; - new_termios.c_cc[VTIME] = 0; - tcsetattr(STDIN_FILENO, TCSANOW, &new_termios); - - con_st.tty = fopen("/dev/tty", "w+"); - if (con_st.tty != nullptr) { - con_st.out = con_st.tty; - } - - setlocale(LC_ALL, ""); -#endif -} - -void console_cleanup(console_state & con_st) { - // Reset console color - console_set_color(con_st, CONSOLE_COLOR_DEFAULT); - -#if !defined(_WIN32) - if (con_st.tty != nullptr) { - con_st.out = stdout; - fclose(con_st.tty); - con_st.tty = nullptr; - } - // Restore the terminal settings on POSIX systems - tcsetattr(STDIN_FILENO, TCSANOW, &con_st.prev_state); -#endif -} - -/* Keep track of current color of output, and emit ANSI code if it changes. */ -void console_set_color(console_state & con_st, console_color_t color) { - if (con_st.use_color && con_st.color != color) { - fflush(stdout); - switch(color) { - case CONSOLE_COLOR_DEFAULT: - fprintf(con_st.out, ANSI_COLOR_RESET); - break; - case CONSOLE_COLOR_PROMPT: - fprintf(con_st.out, ANSI_COLOR_YELLOW); - break; - case CONSOLE_COLOR_USER_INPUT: - fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_GREEN); - break; - case CONSOLE_COLOR_ERROR: - fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_RED); - break; - } - con_st.color = color; - fflush(con_st.out); - } -} - -char32_t getchar32() { -#if defined(_WIN32) - HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE); - wchar_t high_surrogate = 0; - - while (true) { - INPUT_RECORD record; - DWORD count; - if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) { - return WEOF; - } - - if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) { - wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar; - if (wc == 0) { - continue; - } - - if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate - high_surrogate = wc; - continue; - } else if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate - if (high_surrogate != 0) { // Check if we have a high surrogate - return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000; - } - } - - high_surrogate = 0; // Reset the high surrogate - return static_cast<char32_t>(wc); - } - } -#else - wchar_t wc = getwchar(); - if (static_cast<wint_t>(wc) == WEOF) { - return WEOF; - } - -#if WCHAR_MAX == 0xFFFF - if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate - wchar_t low_surrogate = getwchar(); - if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate - return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000; - } - } - if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair - return 0xFFFD; // Return the replacement character U+FFFD - } -#endif - - return static_cast<char32_t>(wc); -#endif -} - -void pop_cursor(console_state & con_st) { -#if defined(_WIN32) - if (con_st.hConsole != NULL) { - CONSOLE_SCREEN_BUFFER_INFO bufferInfo; - GetConsoleScreenBufferInfo(con_st.hConsole, &bufferInfo); - - COORD newCursorPosition = bufferInfo.dwCursorPosition; - if (newCursorPosition.X == 0) { - newCursorPosition.X = bufferInfo.dwSize.X - 1; - newCursorPosition.Y -= 1; - } else { - newCursorPosition.X -= 1; - } - - SetConsoleCursorPosition(con_st.hConsole, newCursorPosition); - return; - } -#endif - putc('\b', con_st.out); -} - -int estimateWidth(char32_t codepoint) { -#if defined(_WIN32) - return 1; -#else - return wcwidth(codepoint); -#endif -} - -int put_codepoint(console_state & con_st, const char* utf8_codepoint, size_t length, int expectedWidth) { -#if defined(_WIN32) - CONSOLE_SCREEN_BUFFER_INFO bufferInfo; - if (!GetConsoleScreenBufferInfo(con_st.hConsole, &bufferInfo)) { - // go with the default - return expectedWidth; - } - COORD initialPosition = bufferInfo.dwCursorPosition; - DWORD nNumberOfChars = length; - WriteConsole(con_st.hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL); - - CONSOLE_SCREEN_BUFFER_INFO newBufferInfo; - GetConsoleScreenBufferInfo(con_st.hConsole, &newBufferInfo); - - // Figure out our real position if we're in the last column - if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) { - DWORD nNumberOfChars; - WriteConsole(con_st.hConsole, &" \b", 2, &nNumberOfChars, NULL); - GetConsoleScreenBufferInfo(con_st.hConsole, &newBufferInfo); - } - - int width = newBufferInfo.dwCursorPosition.X - initialPosition.X; - if (width < 0) { - width += newBufferInfo.dwSize.X; - } - return width; -#else - // we can trust expectedWidth if we've got one - if (expectedWidth >= 0 || con_st.tty == nullptr) { - fwrite(utf8_codepoint, length, 1, con_st.out); - return expectedWidth; - } - - fputs("\033[6n", con_st.tty); // Query cursor position - int x1, x2, y1, y2; - int results = 0; - results = fscanf(con_st.tty, "\033[%d;%dR", &y1, &x1); - - fwrite(utf8_codepoint, length, 1, con_st.tty); - - fputs("\033[6n", con_st.tty); // Query cursor position - results += fscanf(con_st.tty, "\033[%d;%dR", &y2, &x2); - - if (results != 4) { - return expectedWidth; - } - - int width = x2 - x1; - if (width < 0) { - // Calculate the width considering text wrapping - struct winsize w; - ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); - width += w.ws_col; - } - return width; -#endif -} - -void replace_last(console_state & con_st, char ch) { -#if defined(_WIN32) - pop_cursor(con_st); - put_codepoint(con_st, &ch, 1, 1); -#else - fprintf(con_st.out, "\b%c", ch); -#endif -} - -void append_utf8(char32_t ch, std::string & out) { - if (ch <= 0x7F) { - out.push_back(static_cast<unsigned char>(ch)); - } else if (ch <= 0x7FF) { - out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F))); - out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F))); - } else if (ch <= 0xFFFF) { - out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F))); - out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F))); - out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F))); - } else if (ch <= 0x10FFFF) { - out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07))); - out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F))); - out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F))); - out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F))); - } else { - // Invalid Unicode code point - } -} - -// Helper function to remove the last UTF-8 character from a string -void pop_back_utf8_char(std::string & line) { - if (line.empty()) { - return; - } - - size_t pos = line.length() - 1; - - // Find the start of the last UTF-8 character (checking up to 4 bytes back) - for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) { - if ((line[pos] & 0xC0) != 0x80) break; // Found the start of the character - } - line.erase(pos); -} - -bool console_readline(console_state & con_st, std::string & line) { - console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); - if (con_st.out != stdout) { - fflush(stdout); - } - - line.clear(); - std::vector<int> widths; - bool is_special_char = false; - bool end_of_stream = false; - - char32_t input_char; - while (true) { - fflush(con_st.out); // Ensure all output is displayed before waiting for input - input_char = getchar32(); - - if (input_char == '\r' || input_char == '\n') { - break; - } - - if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) { - end_of_stream = true; - break; - } - - if (is_special_char) { - console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); - replace_last(con_st, line.back()); - is_special_char = false; - } - - if (input_char == '\033') { // Escape sequence - char32_t code = getchar32(); - if (code == '[' || code == 0x1B) { - // Discard the rest of the escape sequence - while ((code = getchar32()) != (char32_t) WEOF) { - if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') { - break; - } - } - } - } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace - if (!widths.empty()) { - int count; - do { - count = widths.back(); - widths.pop_back(); - // Move cursor back, print space, and move cursor back again - for (int i = 0; i < count; i++) { - replace_last(con_st, ' '); - pop_cursor(con_st); - } - pop_back_utf8_char(line); - } while (count == 0 && !widths.empty()); - } - } else { - int offset = line.length(); - append_utf8(input_char, line); - int width = put_codepoint(con_st, line.c_str() + offset, line.length() - offset, estimateWidth(input_char)); - if (width < 0) { - width = 0; - } - widths.push_back(width); - } - - if (!line.empty() && (line.back() == '\\' || line.back() == '/')) { - console_set_color(con_st, CONSOLE_COLOR_PROMPT); - replace_last(con_st, line.back()); - is_special_char = true; - } - } - - bool has_more = con_st.multiline_input; - if (is_special_char) { - replace_last(con_st, ' '); - pop_cursor(con_st); - - char last = line.back(); - line.pop_back(); - if (last == '\\') { - line += '\n'; - fputc('\n', con_st.out); - has_more = !has_more; - } else { - // llama will just eat the single space, it won't act as a space - if (line.length() == 1 && line.back() == ' ') { - line.clear(); - pop_cursor(con_st); - } - has_more = false; - } - } else { - if (end_of_stream) { - has_more = false; - } else { - line += '\n'; - fputc('\n', con_st.out); - } - } - - fflush(con_st.out); - return has_more; -} diff --git a/examples/common.h b/examples/common.h index 6315df9..375bc0a 100644 --- a/examples/common.h +++ b/examples/common.h @@ -11,27 +11,27 @@ #include <unordered_map> #include <tuple> -#if !defined (_WIN32) -#include <stdio.h> -#include <termios.h> -#endif - // // CLI argument parsing // int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_gpu_layers = 0; // number of layers to store in VRAM - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_gqa = 1; // grouped-query attention factor (TODO: move to hparams) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_gpu_layers = 0; // number of layers to store in VRAM + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; // rms norm epsilon + float rope_freq_base = 10000.0f; // RoPE base frequency + float rope_freq_scale = 1.0f; // RoPE frequency scaling factor // sampling parameters std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens @@ -44,7 +44,7 @@ struct gpt_params { int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) float frequency_penalty = 0.00f; // 0.0 = disabled float presence_penalty = 0.00f; // 0.0 = disabled - int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate @@ -52,7 +52,6 @@ struct gpt_params { // https://arxiv.org/abs/2306.17806 std::string cfg_negative_prompt; // string to help guidance float cfg_scale = 1.f; // How strong is guidance - float cfg_smooth_factor = 1.f; // Smooth factor between old and new logits std::string model = "models/7B/ggml-model.bin"; // model path std::string model_alias = "unknown"; // model alias @@ -60,12 +59,17 @@ struct gpt_params { std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state std::string input_prefix = ""; // string to prefix user inputs with std::string input_suffix = ""; // string to suffix user inputs with + std::string grammar = ""; // optional BNF-like grammar to constrain sampling std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted std::string lora_adapter = ""; // lora adapter path std::string lora_base = ""; // base model path for the lora adapter - bool low_vram = false; // if true, reduce VRAM usage at the cost of performance + bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt + size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score + + bool low_vram = false; // if true, reduce VRAM usage at the cost of performance + bool mul_mat_q = false; // if true, use experimental mul_mat_q kernels bool memory_f16 = true; // use f16 instead of f32 for memory kv bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs @@ -76,7 +80,9 @@ struct gpt_params { bool embedding = false; // get only sentence embedding bool interactive_first = false; // wait for user input immediately bool multiline_input = false; // reverse the usage of `\` + bool simple_io = false; // improves compatibility with subprocesses and limited consoles + bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool instruct = false; // instruction mode (used for Alpaca models) bool penalize_nl = true; // consider newlines as a repeatable token bool perplexity = false; // compute perplexity over the prompt @@ -106,42 +112,3 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params); struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params); - -// -// Console utils -// - -#define ANSI_COLOR_RED "\x1b[31m" -#define ANSI_COLOR_GREEN "\x1b[32m" -#define ANSI_COLOR_YELLOW "\x1b[33m" -#define ANSI_COLOR_BLUE "\x1b[34m" -#define ANSI_COLOR_MAGENTA "\x1b[35m" -#define ANSI_COLOR_CYAN "\x1b[36m" -#define ANSI_COLOR_RESET "\x1b[0m" -#define ANSI_BOLD "\x1b[1m" - -enum console_color_t { - CONSOLE_COLOR_DEFAULT=0, - CONSOLE_COLOR_PROMPT, - CONSOLE_COLOR_USER_INPUT, - CONSOLE_COLOR_ERROR -}; - -struct console_state { - bool multiline_input = false; - bool use_color = false; - console_color_t color = CONSOLE_COLOR_DEFAULT; - - FILE* out = stdout; -#if defined (_WIN32) - void* hConsole; -#else - FILE* tty = nullptr; - termios prev_state; -#endif -}; - -void console_init(console_state & con_st); -void console_cleanup(console_state & con_st); -void console_set_color(console_state & con_st, console_color_t color); -bool console_readline(console_state & con_st, std::string & line); diff --git a/examples/console.cpp b/examples/console.cpp new file mode 100644 index 0000000..8966b10 --- /dev/null +++ b/examples/console.cpp @@ -0,0 +1,496 @@ +#include "console.h" +#include <vector> +#include <iostream> + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include <windows.h> +#include <fcntl.h> +#include <io.h> +#else +#include <climits> +#include <sys/ioctl.h> +#include <unistd.h> +#include <wchar.h> +#include <stdio.h> +#include <stdlib.h> +#include <signal.h> +#include <termios.h> +#endif + +#define ANSI_COLOR_RED "\x1b[31m" +#define ANSI_COLOR_GREEN "\x1b[32m" +#define ANSI_COLOR_YELLOW "\x1b[33m" +#define ANSI_COLOR_BLUE "\x1b[34m" +#define ANSI_COLOR_MAGENTA "\x1b[35m" +#define ANSI_COLOR_CYAN "\x1b[36m" +#define ANSI_COLOR_RESET "\x1b[0m" +#define ANSI_BOLD "\x1b[1m" + +namespace console { + + // + // Console state + // + + static bool advanced_display = false; + static bool simple_io = true; + static display_t current_display = reset; + + static FILE* out = stdout; + +#if defined (_WIN32) + static void* hConsole; +#else + static FILE* tty = nullptr; + static termios initial_state; +#endif + + // + // Init and cleanup + // + + void init(bool use_simple_io, bool use_advanced_display) { + advanced_display = use_advanced_display; + simple_io = use_simple_io; +#if defined(_WIN32) + // Windows-specific console initialization + DWORD dwMode = 0; + hConsole = GetStdHandle(STD_OUTPUT_HANDLE); + if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) { + hConsole = GetStdHandle(STD_ERROR_HANDLE); + if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) { + hConsole = nullptr; + simple_io = true; + } + } + if (hConsole) { + // Enable ANSI colors on Windows 10+ + if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)) { + SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING); + } + // Set console output codepage to UTF8 + SetConsoleOutputCP(CP_UTF8); + } + HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE); + if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) { + // Set console input codepage to UTF16 + _setmode(_fileno(stdin), _O_WTEXT); + + // Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT) + if (simple_io) { + dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT; + } else { + dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT); + } + if (!SetConsoleMode(hConIn, dwMode)) { + simple_io = true; + } + } +#else + // POSIX-specific console initialization + if (!simple_io) { + struct termios new_termios; + tcgetattr(STDIN_FILENO, &initial_state); + new_termios = initial_state; + new_termios.c_lflag &= ~(ICANON | ECHO); + new_termios.c_cc[VMIN] = 1; + new_termios.c_cc[VTIME] = 0; + tcsetattr(STDIN_FILENO, TCSANOW, &new_termios); + + tty = fopen("/dev/tty", "w+"); + if (tty != nullptr) { + out = tty; + } + } + + setlocale(LC_ALL, ""); +#endif + } + + void cleanup() { + // Reset console display + set_display(reset); + +#if !defined(_WIN32) + // Restore settings on POSIX systems + if (!simple_io) { + if (tty != nullptr) { + out = stdout; + fclose(tty); + tty = nullptr; + } + tcsetattr(STDIN_FILENO, TCSANOW, &initial_state); + } +#endif + } + + // + // Display and IO + // + + // Keep track of current display and only emit ANSI code if it changes + void set_display(display_t display) { + if (advanced_display && current_display != display) { + fflush(stdout); + switch(display) { + case reset: + fprintf(out, ANSI_COLOR_RESET); + break; + case prompt: + fprintf(out, ANSI_COLOR_YELLOW); + break; + case user_input: + fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN); + break; + case error: + fprintf(out, ANSI_BOLD ANSI_COLOR_RED); + } + current_display = display; + fflush(out); + } + } + + char32_t getchar32() { +#if defined(_WIN32) + HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE); + wchar_t high_surrogate = 0; + + while (true) { + INPUT_RECORD record; + DWORD count; + if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) { + return WEOF; + } + + if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) { + wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar; + if (wc == 0) { + continue; + } + + if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate + high_surrogate = wc; + continue; + } + if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate + if (high_surrogate != 0) { // Check if we have a high surrogate + return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000; + } + } + + high_surrogate = 0; // Reset the high surrogate + return static_cast<char32_t>(wc); + } + } +#else + wchar_t wc = getwchar(); + if (static_cast<wint_t>(wc) == WEOF) { + return WEOF; + } + +#if WCHAR_MAX == 0xFFFF + if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate + wchar_t low_surrogate = getwchar(); + if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate + return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000; + } + } + if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair + return 0xFFFD; // Return the replacement character U+FFFD + } +#endif + + return static_cast<char32_t>(wc); +#endif + } + + void pop_cursor() { +#if defined(_WIN32) + if (hConsole != NULL) { + CONSOLE_SCREEN_BUFFER_INFO bufferInfo; + GetConsoleScreenBufferInfo(hConsole, &bufferInfo); + + COORD newCursorPosition = bufferInfo.dwCursorPosition; + if (newCursorPosition.X == 0) { + newCursorPosition.X = bufferInfo.dwSize.X - 1; + newCursorPosition.Y -= 1; + } else { + newCursorPosition.X -= 1; + } + + SetConsoleCursorPosition(hConsole, newCursorPosition); + return; + } +#endif + putc('\b', out); + } + + int estimateWidth(char32_t codepoint) { +#if defined(_WIN32) + return 1; +#else + return wcwidth(codepoint); +#endif + } + + int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) { +#if defined(_WIN32) + CONSOLE_SCREEN_BUFFER_INFO bufferInfo; + if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) { + // go with the default + return expectedWidth; + } + COORD initialPosition = bufferInfo.dwCursorPosition; + DWORD nNumberOfChars = length; + WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL); + + CONSOLE_SCREEN_BUFFER_INFO newBufferInfo; + GetConsoleScreenBufferInfo(hConsole, &newBufferInfo); + + // Figure out our real position if we're in the last column + if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) { + DWORD nNumberOfChars; + WriteConsole(hConsole, &" \b", 2, &nNumberOfChars, NULL); + GetConsoleScreenBufferInfo(hConsole, &newBufferInfo); + } + + int width = newBufferInfo.dwCursorPosition.X - initialPosition.X; + if (width < 0) { + width += newBufferInfo.dwSize.X; + } + return width; +#else + // We can trust expectedWidth if we've got one + if (expectedWidth >= 0 || tty == nullptr) { + fwrite(utf8_codepoint, length, 1, out); + return expectedWidth; + } + + fputs("\033[6n", tty); // Query cursor position + int x1; + int y1; + int x2; + int y2; + int results = 0; + results = fscanf(tty, "\033[%d;%dR", &y1, &x1); + + fwrite(utf8_codepoint, length, 1, tty); + + fputs("\033[6n", tty); // Query cursor position + results += fscanf(tty, "\033[%d;%dR", &y2, &x2); + + if (results != 4) { + return expectedWidth; + } + + int width = x2 - x1; + if (width < 0) { + // Calculate the width considering text wrapping + struct winsize w; + ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); + width += w.ws_col; + } + return width; +#endif + } + + void replace_last(char ch) { +#if defined(_WIN32) + pop_cursor(); + put_codepoint(&ch, 1, 1); +#else + fprintf(out, "\b%c", ch); +#endif + } + + void append_utf8(char32_t ch, std::string & out) { + if (ch <= 0x7F) { + out.push_back(static_cast<unsigned char>(ch)); + } else if (ch <= 0x7FF) { + out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F))); + out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F))); + } else if (ch <= 0xFFFF) { + out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F))); + out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F))); + out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F))); + } else if (ch <= 0x10FFFF) { + out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07))); + out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F))); + out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F))); + out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F))); + } else { + // Invalid Unicode code point + } + } + + // Helper function to remove the last UTF-8 character from a string + void pop_back_utf8_char(std::string & line) { + if (line.empty()) { + return; + } + + size_t pos = line.length() - 1; + + // Find the start of the last UTF-8 character (checking up to 4 bytes back) + for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) { + if ((line[pos] & 0xC0) != 0x80) { + break; // Found the start of the character + } + } + line.erase(pos); + } + + bool readline_advanced(std::string & line, bool multiline_input) { + if (out != stdout) { + fflush(stdout); + } + + line.clear(); + std::vector<int> widths; + bool is_special_char = false; + bool end_of_stream = false; + + char32_t input_char; + while (true) { + fflush(out); // Ensure all output is displayed before waiting for input + input_char = getchar32(); + + if (input_char == '\r' || input_char == '\n') { + break; + } + + if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) { + end_of_stream = true; + break; + } + + if (is_special_char) { + set_display(user_input); + replace_last(line.back()); + is_special_char = false; + } + + if (input_char == '\033') { // Escape sequence + char32_t code = getchar32(); + if (code == '[' || code == 0x1B) { + // Discard the rest of the escape sequence + while ((code = getchar32()) != (char32_t) WEOF) { + if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') { + break; + } + } + } + } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace + if (!widths.empty()) { + int count; + do { + count = widths.back(); + widths.pop_back(); + // Move cursor back, print space, and move cursor back again + for (int i = 0; i < count; i++) { + replace_last(' '); + pop_cursor(); + } + pop_back_utf8_char(line); + } while (count == 0 && !widths.empty()); + } + } else { + int offset = line.length(); + append_utf8(input_char, line); + int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char)); + if (width < 0) { + width = 0; + } + widths.push_back(width); + } + + if (!line.empty() && (line.back() == '\\' || line.back() == '/')) { + set_display(prompt); + replace_last(line.back()); + is_special_char = true; + } + } + + bool has_more = multiline_input; + if (is_special_char) { + replace_last(' '); + pop_cursor(); + + char last = line.back(); + line.pop_back(); + if (last == '\\') { + line += '\n'; + fputc('\n', out); + has_more = !has_more; + } else { + // llama will just eat the single space, it won't act as a space + if (line.length() == 1 && line.back() == ' ') { + line.clear(); + pop_cursor(); + } + has_more = false; + } + } else { + if (end_of_stream) { + has_more = false; + } else { + line += '\n'; + fputc('\n', out); + } + } + + fflush(out); + return has_more; + } + + bool readline_simple(std::string & line, bool multiline_input) { +#if defined(_WIN32) + std::wstring wline; + if (!std::getline(std::wcin, wline)) { + // Input stream is bad or EOF received + line.clear(); + GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0); + return false; + } + + int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL); + line.resize(size_needed); + WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL); +#else + if (!std::getline(std::cin, line)) { + // Input stream is bad or EOF received + line.clear(); + return false; + } +#endif + if (!line.empty()) { + char last = line.back(); + if (last == '/') { // Always return control on '/' symbol + line.pop_back(); + return false; + } + if (last == '\\') { // '\\' changes the default action + line.pop_back(); + multiline_input = !multiline_input; + } + } + line += '\n'; + + // By default, continue input if multiline_input is set + return multiline_input; + } + + bool readline(std::string & line, bool multiline_input) { + set_display(user_input); + + if (simple_io) { + return readline_simple(line, multiline_input); + } + return readline_advanced(line, multiline_input); + } + +} diff --git a/examples/console.h b/examples/console.h new file mode 100644 index 0000000..ec17526 --- /dev/null +++ b/examples/console.h @@ -0,0 +1,19 @@ +// Console functions + +#pragma once + +#include <string> + +namespace console { + enum display_t { + reset = 0, + prompt, + user_input, + error + }; + + void init(bool use_simple_io, bool use_advanced_display); + void cleanup(); + void set_display(display_t display); + bool readline(std::string & line, bool multiline_input); +} diff --git a/examples/embd-input/CMakeLists.txt b/examples/embd-input/CMakeLists.txt index 2b62395..5bbb1ea 100644 --- a/examples/embd-input/CMakeLists.txt +++ b/examples/embd-input/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET embdinput) add_library(${TARGET} embd-input-lib.cpp embd-input.h) +install(TARGETS ${TARGET} LIBRARY) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) @@ -8,6 +9,7 @@ endif() set(TARGET embd-input-test) add_executable(${TARGET} embd-input-test.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/embd-input/README.md b/examples/embd-input/README.md index 02d028f..5c4c75e 100644 --- a/examples/embd-input/README.md +++ b/examples/embd-input/README.md @@ -17,7 +17,7 @@ make import torch bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin" -pth_path = "./examples/embd_input/llava_projection.pth" +pth_path = "./examples/embd-input/llava_projection.pth" dic = torch.load(bin_path) used_key = ["model.mm_projector.weight","model.mm_projector.bias"] diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 2656382..2185b9b 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -30,7 +30,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) { fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); + params.seed = uint32_t(time(NULL)); } fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); diff --git a/examples/embd-input/llava.py b/examples/embd-input/llava.py index 2f20cb7..bcbdd2b 100644 --- a/examples/embd-input/llava.py +++ b/examples/embd-input/llava.py @@ -59,7 +59,7 @@ if __name__=="__main__": # Also here can use pytorch_model-00003-of-00003.bin directly. a.load_projection(os.path.join( os.path.dirname(__file__) , - "llava_projetion.pth")) + "llava_projection.pth")) respose = a.chat_with_image( Image.open("./media/llama1-logo.png").convert('RGB'), "what is the text in the picture?") diff --git a/examples/embd-input/minigpt4.py b/examples/embd-input/minigpt4.py index 8e98f85..15c9b77 100644 --- a/examples/embd-input/minigpt4.py +++ b/examples/embd-input/minigpt4.py @@ -64,7 +64,7 @@ class MiniGPT4(Blip2Base): self.max_txt_len = max_txt_len self.end_sym = end_sym self.model = MyModel(["main", *args]) - # system promt + # system prompt self.model.eval_string("Give the following image: <Img>ImageContent</Img>. " "You will be able to see the image once I provide it to you. Please answer my questions." "###") diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index db73b6b..0c752c7 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET embedding) add_executable(${TARGET} embedding.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/grammar-parser.cpp b/examples/grammar-parser.cpp new file mode 100644 index 0000000..e76bd11 --- /dev/null +++ b/examples/grammar-parser.cpp @@ -0,0 +1,423 @@ +#include "grammar-parser.h" +#include <cstdint> +#include <cwchar> +#include <string> +#include <utility> +#include <stdexcept> +#include <exception> + +namespace grammar_parser { + // NOTE: assumes valid utf8 (but checks for overrun) + // copied from llama.cpp + std::pair<uint32_t, const char *> decode_utf8(const char * src) { + static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; + uint8_t first_byte = static_cast<uint8_t>(*src); + uint8_t highbits = first_byte >> 4; + int len = lookup[highbits]; + uint8_t mask = (1 << (8 - len)) - 1; + uint32_t value = first_byte & mask; + const char * end = src + len; // may overrun! + const char * pos = src + 1; + for ( ; pos < end && *pos; pos++) { + value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F); + } + return std::make_pair(value, pos); + } + + uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { + uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size()); + auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); + return result.first->second; + } + + uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) { + uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size()); + state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id; + return next_id; + } + + void add_rule( + parse_state & state, + uint32_t rule_id, + const std::vector<llama_grammar_element> & rule) { + if (state.rules.size() <= rule_id) { + state.rules.resize(rule_id + 1); + } + state.rules[rule_id] = rule; + } + + bool is_word_char(char c) { + return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9'); + } + + std::pair<uint32_t, const char *> parse_hex(const char * src, int size) { + const char * pos = src; + const char * end = src + size; + uint32_t value = 0; + for ( ; pos < end && *pos; pos++) { + value <<= 4; + char c = *pos; + if ('a' <= c && c <= 'f') { + value += c - 'a' + 10; + } else if ('A' <= c && c <= 'F') { + value += c - 'A' + 10; + } else if ('0' <= c && c <= '9') { + value += c - '0'; + } else { + break; + } + } + if (pos != end) { + throw std::runtime_error("expecting " + std::to_string(size) + " hex chars at " + src); + } + return std::make_pair(value, pos); + } + + const char * parse_space(const char * src, bool newline_ok) { + const char * pos = src; + while (*pos == ' ' || *pos == '\t' || *pos == '#' || + (newline_ok && (*pos == '\r' || *pos == '\n'))) { + if (*pos == '#') { + while (*pos && *pos != '\r' && *pos != '\n') { + pos++; + } + } else { + pos++; + } + } + return pos; + } + + const char * parse_name(const char * src) { + const char * pos = src; + while (is_word_char(*pos)) { + pos++; + } + if (pos == src) { + throw std::runtime_error(std::string("expecting name at ") + src); + } + return pos; + } + + std::pair<uint32_t, const char *> parse_char(const char * src) { + if (*src == '\\') { + switch (src[1]) { + case 'x': return parse_hex(src + 2, 2); + case 'u': return parse_hex(src + 2, 4); + case 'U': return parse_hex(src + 2, 8); + case 't': return std::make_pair('\t', src + 2); + case 'r': return std::make_pair('\r', src + 2); + case 'n': return std::make_pair('\n', src + 2); + case '\\': + case '"': + case '[': + case ']': + return std::make_pair(src[1], src + 2); + default: + throw std::runtime_error(std::string("unknown escape at ") + src); + } + } else if (*src) { + return decode_utf8(src); + } + throw std::runtime_error("unexpected end of input"); + } + + const char * parse_alternates( + parse_state & state, + const char * src, + const std::string & rule_name, + uint32_t rule_id, + bool is_nested); + + const char * parse_sequence( + parse_state & state, + const char * src, + const std::string & rule_name, + std::vector<llama_grammar_element> & out_elements, + bool is_nested) { + size_t last_sym_start = out_elements.size(); + const char * pos = src; + while (*pos) { + if (*pos == '"') { // literal string + pos++; + last_sym_start = out_elements.size(); + while (*pos != '"') { + auto char_pair = parse_char(pos); + pos = char_pair.second; + out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first}); + } + pos = parse_space(pos + 1, is_nested); + } else if (*pos == '[') { // char range(s) + pos++; + enum llama_gretype start_type = LLAMA_GRETYPE_CHAR; + if (*pos == '^') { + pos++; + start_type = LLAMA_GRETYPE_CHAR_NOT; + } + last_sym_start = out_elements.size(); + while (*pos != ']') { + auto char_pair = parse_char(pos); + pos = char_pair.second; + enum llama_gretype type = last_sym_start < out_elements.size() + ? LLAMA_GRETYPE_CHAR_ALT + : start_type; + + out_elements.push_back({type, char_pair.first}); + if (pos[0] == '-' && pos[1] != ']') { + auto endchar_pair = parse_char(pos + 1); + pos = endchar_pair.second; + out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first}); + } + } + pos = parse_space(pos + 1, is_nested); + } else if (is_word_char(*pos)) { // rule reference + const char * name_end = parse_name(pos); + uint32_t ref_rule_id = get_symbol_id(state, pos, name_end - pos); + pos = parse_space(name_end, is_nested); + last_sym_start = out_elements.size(); + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, ref_rule_id}); + } else if (*pos == '(') { // grouping + // parse nested alternates into synthesized rule + pos = parse_space(pos + 1, true); + uint32_t sub_rule_id = generate_symbol_id(state, rule_name); + pos = parse_alternates(state, pos, rule_name, sub_rule_id, true); + last_sym_start = out_elements.size(); + // output reference to synthesized rule + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + if (*pos != ')') { + throw std::runtime_error(std::string("expecting ')' at ") + pos); + } + pos = parse_space(pos + 1, is_nested); + } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator + if (last_sym_start == out_elements.size()) { + throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos); + } + + // apply transformation to previous symbol (last_sym_start to end) according to + // rewrite rules: + // S* --> S' ::= S S' | + // S+ --> S' ::= S S' | S + // S? --> S' ::= S | + uint32_t sub_rule_id = generate_symbol_id(state, rule_name); + std::vector<llama_grammar_element> sub_rule; + // add preceding symbol to generated rule + sub_rule.insert( + sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); + if (*pos == '*' || *pos == '+') { + // cause generated rule to recurse + sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + } + // mark start of alternate def + sub_rule.push_back({LLAMA_GRETYPE_ALT, 0}); + if (*pos == '+') { + // add preceding symbol as alternate only for '+' (otherwise empty) + sub_rule.insert( + sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); + } + sub_rule.push_back({LLAMA_GRETYPE_END, 0}); + add_rule(state, sub_rule_id, sub_rule); + + // in original rule, replace previous symbol with reference to generated rule + out_elements.resize(last_sym_start); + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + + pos = parse_space(pos + 1, is_nested); + } else { + break; + } + } + return pos; + } + + const char * parse_alternates( + parse_state & state, + const char * src, + const std::string & rule_name, + uint32_t rule_id, + bool is_nested) { + std::vector<llama_grammar_element> rule; + const char * pos = parse_sequence(state, src, rule_name, rule, is_nested); + while (*pos == '|') { + rule.push_back({LLAMA_GRETYPE_ALT, 0}); + pos = parse_space(pos + 1, true); + pos = parse_sequence(state, pos, rule_name, rule, is_nested); + } + rule.push_back({LLAMA_GRETYPE_END, 0}); + add_rule(state, rule_id, rule); + return pos; + } + + const char * parse_rule(parse_state & state, const char * src) { + const char * name_end = parse_name(src); + const char * pos = parse_space(name_end, false); + size_t name_len = name_end - src; + uint32_t rule_id = get_symbol_id(state, src, name_len); + const std::string name(src, name_len); + + if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) { + throw std::runtime_error(std::string("expecting ::= at ") + pos); + } + pos = parse_space(pos + 3, true); + + pos = parse_alternates(state, pos, name, rule_id, false); + + if (*pos == '\r') { + pos += pos[1] == '\n' ? 2 : 1; + } else if (*pos == '\n') { + pos++; + } else if (*pos) { + throw std::runtime_error(std::string("expecting newline or end at ") + pos); + } + return parse_space(pos, true); + } + + parse_state parse(const char * src) { + try { + parse_state state; + const char * pos = parse_space(src, true); + while (*pos) { + pos = parse_rule(state, pos); + } + return state; + } catch (const std::exception & err) { + fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what()); + return parse_state(); + } + } + + void print_grammar_char(FILE * file, uint32_t c) { + if (0x20 <= c && c <= 0x7f) { + fprintf(file, "%c", static_cast<char>(c)); + } else { + // cop out of encoding UTF-8 + fprintf(file, "<U+%04X>", c); + } + } + + bool is_char_element(llama_grammar_element elem) { + switch (elem.type) { + case LLAMA_GRETYPE_CHAR: return true; + case LLAMA_GRETYPE_CHAR_NOT: return true; + case LLAMA_GRETYPE_CHAR_ALT: return true; + case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true; + default: return false; + } + } + + void print_rule_binary(FILE * file, const std::vector<llama_grammar_element> & rule) { + for (auto elem : rule) { + switch (elem.type) { + case LLAMA_GRETYPE_END: fprintf(file, "END"); break; + case LLAMA_GRETYPE_ALT: fprintf(file, "ALT"); break; + case LLAMA_GRETYPE_RULE_REF: fprintf(file, "RULE_REF"); break; + case LLAMA_GRETYPE_CHAR: fprintf(file, "CHAR"); break; + case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break; + case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break; + case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break; + } + switch (elem.type) { + case LLAMA_GRETYPE_END: + case LLAMA_GRETYPE_ALT: + case LLAMA_GRETYPE_RULE_REF: + fprintf(file, "(%u) ", elem.value); + break; + case LLAMA_GRETYPE_CHAR: + case LLAMA_GRETYPE_CHAR_NOT: + case LLAMA_GRETYPE_CHAR_RNG_UPPER: + case LLAMA_GRETYPE_CHAR_ALT: + fprintf(file, "(\""); + print_grammar_char(file, elem.value); + fprintf(file, "\") "); + break; + } + } + fprintf(file, "\n"); + } + + void print_rule( + FILE * file, + uint32_t rule_id, + const std::vector<llama_grammar_element> & rule, + const std::map<uint32_t, std::string> & symbol_id_names) { + if (rule.empty() || rule.back().type != LLAMA_GRETYPE_END) { + throw std::runtime_error( + "malformed rule, does not end with LLAMA_GRETYPE_END: " + std::to_string(rule_id)); + } + fprintf(file, "%s ::= ", symbol_id_names.at(rule_id).c_str()); + for (size_t i = 0, end = rule.size() - 1; i < end; i++) { + llama_grammar_element elem = rule[i]; + switch (elem.type) { + case LLAMA_GRETYPE_END: + throw std::runtime_error( + "unexpected end of rule: " + std::to_string(rule_id) + "," + + std::to_string(i)); + case LLAMA_GRETYPE_ALT: + fprintf(file, "| "); + break; + case LLAMA_GRETYPE_RULE_REF: + fprintf(file, "%s ", symbol_id_names.at(elem.value).c_str()); + break; + case LLAMA_GRETYPE_CHAR: + fprintf(file, "["); + print_grammar_char(file, elem.value); + break; + case LLAMA_GRETYPE_CHAR_NOT: + fprintf(file, "[^"); + print_grammar_char(file, elem.value); + break; + case LLAMA_GRETYPE_CHAR_RNG_UPPER: + if (i == 0 || !is_char_element(rule[i - 1])) { + throw std::runtime_error( + "LLAMA_GRETYPE_CHAR_RNG_UPPER without preceding char: " + + std::to_string(rule_id) + "," + std::to_string(i)); + } + fprintf(file, "-"); + print_grammar_char(file, elem.value); + break; + case LLAMA_GRETYPE_CHAR_ALT: + if (i == 0 || !is_char_element(rule[i - 1])) { + throw std::runtime_error( + "LLAMA_GRETYPE_CHAR_ALT without preceding char: " + + std::to_string(rule_id) + "," + std::to_string(i)); + } + print_grammar_char(file, elem.value); + break; + } + if (is_char_element(elem)) { + switch (rule[i + 1].type) { + case LLAMA_GRETYPE_CHAR_ALT: + case LLAMA_GRETYPE_CHAR_RNG_UPPER: + break; + default: + fprintf(file, "] "); + } + } + } + fprintf(file, "\n"); + } + + void print_grammar(FILE * file, const parse_state & state) { + try { + std::map<uint32_t, std::string> symbol_id_names; + for (auto kv : state.symbol_ids) { + symbol_id_names[kv.second] = kv.first; + } + for (size_t i = 0, end = state.rules.size(); i < end; i++) { + // fprintf(file, "%zu: ", i); + // print_rule_binary(file, state.rules[i]); + print_rule(file, uint32_t(i), state.rules[i], symbol_id_names); + // fprintf(file, "\n"); + } + } catch (const std::exception & err) { + fprintf(stderr, "\n%s: error printing grammar: %s\n", __func__, err.what()); + } + } + + std::vector<const llama_grammar_element *> parse_state::c_rules() { + std::vector<const llama_grammar_element *> ret; + for (const auto & rule : rules) { + ret.push_back(rule.data()); + } + return ret; + } +} diff --git a/examples/grammar-parser.h b/examples/grammar-parser.h new file mode 100644 index 0000000..9037d72 --- /dev/null +++ b/examples/grammar-parser.h @@ -0,0 +1,29 @@ +// Implements a parser for an extended Backus-Naur form (BNF), producing the +// binary context-free grammar format specified by llama.h. Supports character +// ranges, grouping, and repetition operators. As an example, a grammar for +// arithmetic might look like: +// +// root ::= expr +// expr ::= term ([-+*/] term)* +// term ::= num | "(" space expr ")" space +// num ::= [0-9]+ space +// space ::= [ \t\n]* + +#pragma once +#include "llama.h" +#include <vector> +#include <map> +#include <cstdint> +#include <string> + +namespace grammar_parser { + struct parse_state { + std::map<std::string, uint32_t> symbol_ids; + std::vector<std::vector<llama_grammar_element>> rules; + + std::vector<const llama_grammar_element *> c_rules(); + }; + + parse_state parse(const char * src); + void print_grammar(FILE * file, const parse_state & state); +} diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py new file mode 100644 index 0000000..2dccc11 --- /dev/null +++ b/examples/json-schema-to-grammar.py @@ -0,0 +1,132 @@ +import argparse +import json +import re +import sys + +# whitespace is constrained to a single space char to prevent model "running away" in +# whitespace. Also maybe improves generation quality? +SPACE_RULE = '" "?' + +PRIMITIVE_RULES = { + 'boolean': '("true" | "false") space', + 'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', + 'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space', + 'string': r''' "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" space ''', + 'null': '"null" space', +} + +INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') +GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') +GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'} + + +class SchemaConverter: + def __init__(self, prop_order): + self._prop_order = prop_order + self._rules = {'space': SPACE_RULE} + + def _format_literal(self, literal): + escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub( + lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal) + ) + return f'"{escaped}"' + + def _add_rule(self, name, rule): + esc_name = INVALID_RULE_CHARS_RE.sub('-', name) + if esc_name not in self._rules or self._rules[esc_name] == rule: + key = esc_name + else: + i = 0 + while f'{esc_name}{i}' in self._rules: + i += 1 + key = f'{esc_name}{i}' + self._rules[key] = rule + return key + + def visit(self, schema, name): + schema_type = schema.get('type') + rule_name = name or 'root' + + if 'oneOf' in schema or 'anyOf' in schema: + rule = ' | '.join(( + self.visit(alt_schema, f'{name}{"-" if name else ""}{i}') + for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf']) + )) + return self._add_rule(rule_name, rule) + + elif 'const' in schema: + return self._add_rule(rule_name, self._format_literal(schema['const'])) + + elif 'enum' in schema: + rule = ' | '.join((self._format_literal(v) for v in schema['enum'])) + return self._add_rule(rule_name, rule) + + elif schema_type == 'object' and 'properties' in schema: + # TODO: `required` keyword + prop_order = self._prop_order + prop_pairs = sorted( + schema['properties'].items(), + # sort by position in prop_order (if specified) then by key + key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]), + ) + + rule = '"{" space' + for i, (prop_name, prop_schema) in enumerate(prop_pairs): + prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') + if i > 0: + rule += ' "," space' + rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}' + rule += ' "}" space' + + return self._add_rule(rule_name, rule) + + elif schema_type == 'array' and 'items' in schema: + # TODO `prefixItems` keyword + item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item') + rule = f'"[" space ({item_rule_name} ("," space {item_rule_name})*)? "]" space' + return self._add_rule(rule_name, rule) + + else: + assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' + return self._add_rule( + 'root' if rule_name == 'root' else schema_type, + PRIMITIVE_RULES[schema_type] + ) + + def format_grammar(self): + return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items())) + + +def main(args_in = None): + parser = argparse.ArgumentParser( + description=''' + Generates a grammar (suitable for use in ./main) that produces JSON conforming to a + given JSON schema. Only a subset of JSON schema features are supported; more may be + added in the future. + ''', + ) + parser.add_argument( + '--prop-order', + default=[], + type=lambda s: s.split(','), + help=''' + comma-separated property names defining the order of precedence for object properties; + properties not specified here are given lower precedence than those that are, and are + sorted alphabetically + ''' + ) + parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)') + args = parser.parse_args(args_in) + + schema = json.load(sys.stdin if args.schema == '-' else open(args.schema)) + prop_order = {name: idx for idx, name in enumerate(args.prop_order)} + converter = SchemaConverter(prop_order) + converter.visit(schema, '') + print(converter.format_grammar()) + + +if __name__ == '__main__': + main() diff --git a/examples/llama.vim b/examples/llama.vim new file mode 100644 index 0000000..f03fadf --- /dev/null +++ b/examples/llama.vim @@ -0,0 +1,132 @@ +" Requires an already running llama.cpp server +" To install either copy or symlink to ~/.vim/autoload/llama.vim +" Then start with either :call llama#doLlamaGen(), +" or add a keybind to your vimrc such as +" nnoremap Z :call llama#doLlamaGen()<CR> +" Similarly, you could add an insert mode keybind with +" inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR> +" +" g:llama_api_url and g:llama_overrides can be configured in your .vimrc +" let g:llama_api_url = "192.168.1.10:8080" +" llama_overrides can also be set through buffer/window scopes. For instance +" autocmd filetype python let b:llama_overrides = {"temp": 0.2} +" Could be added to your .vimrc to automatically set a lower temperature when +" editing a python script +" Additionally, an override dict can be stored at the top of a file +" !*{"stop": ["User:"]} +" Could be added to the start of your chatlog.txt to set the stopping token +" These parameter dicts are merged together from lowest to highest priority: +" server default -> g:llama_overrides -> w:llama_overrides -> +" b:llama_overrides -> in file (!*) overrides +" +" Sublists (like logit_bias and stop) are overridden, not merged +" Example override: +" !*{"logit_bias": [[13, -5], [2, false]], "temperature": 1, "top_k": 5, "top_p": 0.5, "n_predict": 256, "repeat_last_n": 256, "repeat_penalty": 1.17647} +if !exists("g:llama_api_url") + let g:llama_api_url= "127.0.0.1:8080" +endif +if !exists("g:llama_overrides") + let g:llama_overrides = {} +endif +const s:querydata = {"n_predict": 256, "stop": [ "\n" ], "stream": v:true } +const s:curlcommand = ['curl','--data-raw', "{\"prompt\":\"### System:\"}", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', "Content-Type: application/json"] +let s:linedict = {} + +func s:callbackHandler(bufn, channel, msg) + if len(a:msg) < 3 + return + elseif a:msg[0] == "d" + let l:msg = a:msg[6:-1] + else + let l:msg = a:msg + endif + let l:decoded_msg = json_decode(l:msg) + let l:newtext = split(l:decoded_msg['content'], "\n", 1) + if len(l:newtext) > 0 + call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0]) + else + echo "nothing genned" + endif + if len(newtext) > 1 + let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1]) + let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1 + endif + if has_key(l:decoded_msg, "stop") && l:decoded_msg.stop + echo "Finished generation" + endif +endfunction + +func llama#doLlamaGen() + if exists("b:job") + if job_status(b:job) == "run" + call job_stop(b:job) + return + endif + endif + + let l:cbuffer = bufnr("%") + let s:linedict[l:cbuffer] = line('$') + let l:buflines = getbufline(l:cbuffer, 1, 1000) + let l:querydata = copy(s:querydata) + call extend(l:querydata, g:llama_overrides) + if exists("w:llama_overrides") + call extend(l:querydata, w:llama_overrides) + endif + if exists("b:llama_overrides") + call extend(l:querydata, b:llama_overrides) + endif + if l:buflines[0][0:1] == '!*' + let l:userdata = json_decode(l:buflines[0][2:-1]) + call extend(l:querydata, l:userdata) + let l:buflines = l:buflines[1:-1] + endif + let l:querydata.prompt = join(l:buflines, "\n") + let l:curlcommand = copy(s:curlcommand) + let l:curlcommand[2] = json_encode(l:querydata) + let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])}) +endfunction + +" Echos the tokkenization of the provided string , or cursor to end of word +" Onus is placed on the user to include the preceding space +func llama#tokenizeWord(...) + if (a:0 > 0) + let l:input = a:1 + else + exe "normal \"*ye" + let l:input = @* + endif + let l:querydata = {"content": l:input} + let l:curlcommand = copy(s:curlcommand) + let l:curlcommand[2] = json_encode(l:querydata) + let l:curlcommand[8] = g:llama_api_url .. "/tokenize" + let s:token_job = job_start(l:curlcommand, {"callback": function("s:tokenizeWordCallback", [l:input])}) +endfunction + +func s:tokenizeWordCallback(plaintext, channel, msg) + echo '"' .. a:plaintext ..'" - ' .. string(json_decode(a:msg).tokens) +endfunction + + +" Echos the token count of the entire buffer (or provided string) +" Example usage :echo llama#tokenCount() +func llama#tokenCount(...) + if (a:0 > 0) + let l:buflines = a:1 + else + let l:buflines = getline(1,1000) + if l:buflines[0][0:1] == '!*' + let l:buflines = l:buflines[1:-1] + endif + let l:buflines = join(l:buflines, "\n") + endif + let l:querydata = {"content": l:buflines} + let l:curlcommand = copy(s:curlcommand) + let l:curlcommand[2] = json_encode(l:querydata) + let l:curlcommand[8] = g:llama_api_url .. "/tokenize" + let s:token_job = job_start(l:curlcommand, {"callback": "s:tokenCountCallback"}) +endfunction + +func s:tokenCountCallback(channel, msg) + let resp = json_decode(a:msg) + echo len(resp.tokens) +endfunction diff --git a/examples/llama2-13b.sh b/examples/llama2-13b.sh new file mode 100755 index 0000000..92b3f6d --- /dev/null +++ b/examples/llama2-13b.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# +# Temporary script - will be removed in the future +# + +cd `dirname $0` +cd .. + +./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \ + --color \ + --ctx_size 2048 \ + -n -1 \ + -ins -b 256 \ + --top_k 10000 \ + --temp 0.2 \ + --repeat_penalty 1.1 \ + -t 8 diff --git a/examples/llama2.sh b/examples/llama2.sh new file mode 100755 index 0000000..221b375 --- /dev/null +++ b/examples/llama2.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# +# Temporary script - will be removed in the future +# + +cd `dirname $0` +cd .. + +./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \ + --color \ + --ctx_size 2048 \ + -n -1 \ + -ins -b 256 \ + --top_k 10000 \ + --temp 0.2 \ + --repeat_penalty 1.1 \ + -t 8 diff --git a/examples/llm.vim b/examples/llm.vim new file mode 100644 index 0000000..594a285 --- /dev/null +++ b/examples/llm.vim @@ -0,0 +1,27 @@ +" Basic plugin example + +function! Llm() + + let url = "http://127.0.0.1:8080/completion" + + " Get the content of the current buffer + let buffer_content = join(getline(1, '$'), "\n") + + " Create the JSON payload + let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":10,"stream": v:false} + let json_payload.prompt = buffer_content + + " Define the curl command + let curl_command = 'curl -k -s -X POST -H "Content-Type: application/json" -d @- ' . url + let response = system(curl_command, json_encode(json_payload)) + + " Extract the content field from the response + let content = json_decode(response).content + + let split_newlines = split(content, '\n', 1) + + " Insert the content at the cursor position + call setline(line('.'), [ getline('.') . split_newlines[0] ] + split_newlines[1:]) +endfunction + +command! Llm call Llm() diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index c364242..cc18889 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET main) add_executable(${TARGET} main.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/main/README.md b/examples/main/README.md index 04b8d54..55c1609 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -140,6 +140,12 @@ The `--ctx-size` option allows you to set the size of the prompt context used by - `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results. +### Extended Context Size + +Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. + +- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. + ### Keep Prompt The `--keep` option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained. @@ -202,9 +208,9 @@ Example usage: `--top-p 0.95` - `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled). -Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. The method adjusts the logits (token probabilities) by raising them to the power of the parameter z. A higher value of z (e.g., 2.0) will further suppress less likely tokens from the tail of the distribution, while a value of 1.0 disables the effect of TFS. By setting the parameter z, you can control how much the probabilities of less likely tokens are reduced. +Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS. -Example usage: `--tfs 2.0` +Example usage: `--tfs 0.95` ### Locally Typical Sampling @@ -293,5 +299,5 @@ These options provide extra functionality and customization when running the LLa - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. - `-lv, --low-vram`: Do not allocate a VRAM scratch buffer for holding temporary results. Reduces VRAM usage at the cost of performance, particularly prompt processing speed. Requires cuBLAS. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model. This allows you to adapt the pretrained model to specific tasks or domains. +- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 2248c24..56ada7e 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -4,8 +4,10 @@ #endif #include "common.h" +#include "console.h" #include "llama.h" #include "build-info.h" +#include "grammar-parser.h" #include <cassert> #include <cinttypes> @@ -34,9 +36,7 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static console_state con_st; static llama_context ** g_ctx; - static bool is_interacting = false; #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) @@ -45,7 +45,7 @@ void sigint_handler(int signo) { if (!is_interacting) { is_interacting=true; } else { - console_cleanup(con_st); + console::cleanup(); printf("\n"); llama_print_timings(*g_ctx); _exit(130); @@ -63,10 +63,8 @@ int main(int argc, char ** argv) { // save choice to use color for later // (note for later: this is a slightly awkward choice) - con_st.use_color = params.use_color; - con_st.multiline_input = params.multiline_input; - console_init(con_st); - atexit([]() { console_cleanup(con_st); }); + console::init(params.simple_io, params.use_color); + atexit([]() { console::cleanup(); }); if (params.perplexity) { printf("\n************\n"); @@ -84,9 +82,17 @@ int main(int argc, char ** argv) { return 0; } + if (params.rope_freq_base != 10000.0) { + fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base); + } + + if (params.rope_freq_scale != 1.0) { + fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); + } + if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);" - "expect poor results\n", __func__, params.n_ctx); + // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048 + fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); } else if (params.n_ctx < 8) { fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; @@ -131,17 +137,14 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - // determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters + // determine the maximum memory usage needed to do inference for the given n_batch and n_ctx parameters // uncomment the "used_mem" line in llama.cpp to see the results if (params.mem_test) { { - const std::vector<llama_token> tmp(params.n_batch, llama_token_bos()); - llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); - } + fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx); - { - const std::vector<llama_token> tmp = { 0, }; - llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads); + const std::vector<llama_token> tmp(params.n_batch, llama_token_bos()); + llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads); } llama_print_timings(ctx); @@ -319,6 +322,10 @@ int main(int argc, char ** argv) { } } + if (params.input_prefix_bos) { + fprintf(stderr, "Input prefix with BOS\n"); + } + if (!params.input_prefix.empty()) { fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); } @@ -332,13 +339,38 @@ int main(int argc, char ** argv) { fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); fprintf(stderr, "\n\n"); + grammar_parser::parse_state parsed_grammar; + llama_grammar * grammar = NULL; + if (!params.grammar.empty()) { + parsed_grammar = grammar_parser::parse(params.grammar.c_str()); + // will be empty (default) if there are parse errors + if (parsed_grammar.rules.empty()) { + return 1; + } + fprintf(stderr, "%s: grammar:\n", __func__); + grammar_parser::print_grammar(stderr, parsed_grammar); + fprintf(stderr, "\n"); + + { + auto it = params.logit_bias.find(llama_token_eos()); + if (it != params.logit_bias.end() && it->second == -INFINITY) { + fprintf(stderr, + "%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__); + } + } + + std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules()); + grammar = llama_grammar_init( + grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + } + // TODO: replace with ring-buffer std::vector<llama_token> last_n_tokens(n_ctx); std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); if (params.interactive) { const char *control_message; - if (con_st.multiline_input) { + if (params.multiline_input) { control_message = " - To return control to LLaMa, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { @@ -366,7 +398,7 @@ int main(int argc, char ** argv) { int n_past_guidance = 0; // the first thing we will do is to output the prompt, so set color accordingly - console_set_color(con_st, CONSOLE_COLOR_PROMPT); + console::set_display(console::prompt); std::vector<llama_token> embd; std::vector<llama_token> embd_guidance; @@ -387,9 +419,9 @@ int main(int argc, char ** argv) { // Ensure the input doesn't exceed the context size by truncating embd if necessary. if ((int)embd.size() > max_embd_size) { auto skipped_tokens = embd.size() - max_embd_size; - console_set_color(con_st, CONSOLE_COLOR_ERROR); + console::set_display(console::error); printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); - console_set_color(con_st, CONSOLE_COLOR_DEFAULT); + console::set_display(console::reset); fflush(stdout); embd.resize(max_embd_size); } @@ -549,7 +581,7 @@ int main(int argc, char ** argv) { llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale, params.cfg_smooth_factor); + llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale); } // Apply penalties @@ -565,6 +597,10 @@ int main(int argc, char ** argv) { logits[llama_token_nl()] = nl_logit; } + if (grammar != NULL) { + llama_sample_grammar(ctx, &candidates_p, grammar); + } + if (temp <= 0) { // Greedy sampling id = llama_sample_token_greedy(ctx, &candidates_p); @@ -590,20 +626,14 @@ int main(int argc, char ** argv) { } // printf("`%d`", candidates_p.size); + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(id); } - // replace end of text token with newline token when in interactive mode - if (id == llama_token_eos() && params.interactive && !params.instruct) { - id = llama_token_newline.front(); - if (params.antiprompt.size() != 0) { - // tokenize and inject first reverse prompt - const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); - embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); - } - } - // add it to the context embd.push_back(id); @@ -634,7 +664,7 @@ int main(int argc, char ** argv) { } // reset color to default if we there is no pending user input if (input_echo && (int)embd_inp.size() == n_consumed) { - console_set_color(con_st, CONSOLE_COLOR_DEFAULT); + console::set_display(console::reset); } // if not currently processing queued inputs; @@ -660,7 +690,7 @@ int main(int argc, char ** argv) { if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) { if (params.interactive) { is_interacting = true; - console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); + console::set_display(console::user_input); } is_antiprompt = true; fflush(stdout); @@ -669,11 +699,34 @@ int main(int argc, char ** argv) { } } + // deal with end of text token in interactive mode + if (last_n_tokens.back() == llama_token_eos()) { + if (params.interactive) { + if (params.antiprompt.size() != 0) { + // tokenize and inject first reverse prompt + const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); + embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); + is_antiprompt = true; + } + + is_interacting = true; + printf("\n"); + console::set_display(console::user_input); + fflush(stdout); + } else if (params.instruct) { + is_interacting = true; + } + } + if (n_past > 0 && is_interacting) { if (params.instruct) { printf("\n> "); } + if (params.input_prefix_bos) { + embd_inp.push_back(llama_token_bos()); + } + std::string buffer; if (!params.input_prefix.empty()) { buffer += params.input_prefix; @@ -683,12 +736,12 @@ int main(int argc, char ** argv) { std::string line; bool another_line = true; do { - another_line = console_readline(con_st, line); + another_line = console::readline(line, params.multiline_input); buffer += line; } while (another_line); // done taking input, reset color - console_set_color(con_st, CONSOLE_COLOR_DEFAULT); + console::set_display(console::reset); // Add tokens to embd only if the input buffer is non-empty // Entering a empty line lets the user pass control back @@ -720,18 +773,26 @@ int main(int argc, char ** argv) { } if (n_past > 0) { + if (is_interacting) { + // reset grammar state if we're restarting generation + if (grammar != NULL) { + llama_grammar_free(grammar); + + std::vector<const llama_grammar_element *> grammar_rules( + parsed_grammar.c_rules()); + grammar = llama_grammar_init( + grammar_rules.data(), grammar_rules.size(), + parsed_grammar.symbol_ids.at("root")); + } + } is_interacting = false; } } // end of text token - if (!embd.empty() && embd.back() == llama_token_eos()) { - if (params.instruct) { - is_interacting = true; - } else { - fprintf(stderr, " [end of text]\n"); - break; - } + if (!embd.empty() && embd.back() == llama_token_eos() && !(params.instruct || params.interactive)) { + fprintf(stderr, " [end of text]\n"); + break; } // In interactive mode, respect the maximum number of tokens and drop back to user input when reached. @@ -751,6 +812,9 @@ int main(int argc, char ** argv) { llama_free(ctx); llama_free_model(model); + if (grammar != NULL) { + llama_grammar_free(grammar); + } llama_backend_free(); return 0; diff --git a/examples/make-ggml.py b/examples/make-ggml.py new file mode 100644 index 0000000..f63d9fc --- /dev/null +++ b/examples/make-ggml.py @@ -0,0 +1,92 @@ +""" +This script converts Hugging Face llama models to GGML and quantizes them. + +Usage: +python make-ggml.py --model {model_dir_or_hf_repo_name} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)] + +Arguments: +- --model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub. +- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used. +- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used. +- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'. +- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created. + +Quant types: +- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M +- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L +- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M +- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M +- Q2_K: smallest, extreme quality loss - not recommended +- Q3_K: alias for Q3_K_M +- Q3_K_S: very small, very high quality loss +- Q3_K_M: very small, very high quality loss +- Q3_K_L: small, substantial quality loss +- Q4_K: alias for Q4_K_M +- Q4_K_S: small, significant quality loss +- Q4_K_M: medium, balanced quality - recommended +- Q5_K: alias for Q5_K_M +- Q5_K_S: large, low quality loss - recommended +- Q5_K_M: large, very low quality loss - recommended +- Q6_K: very large, extremely low quality loss +- Q8_0: very large, extremely low quality loss - not recommended +- F16: extremely large, virtually no quality loss - not recommended +- F32: absolutely huge, lossless - not recommended +""" +import subprocess +subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True) + +import argparse +import os +from huggingface_hub import snapshot_download + +def main(model, outname, outdir, quants, keep_fp16): + ggml_version = "v3" + + if not os.path.isdir(model): + print(f"Model not found at {model}. Downloading...") + try: + if outname is None: + outname = model.split('/')[-1] + model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache') + except Exception as e: + raise Exception(f"Could not download the model: {e}") + + if outdir is None: + outdir = f'../models/{outname}' + + if not os.path.isfile(f"{model}/config.json"): + raise Exception(f"Could not find config.json in {model}") + + os.makedirs(outdir, exist_ok=True) + + print("Building llama.cpp") + subprocess.run(f"cd .. && make quantize", shell=True, check=True) + + fp16 = f"{outdir}/{outname}.ggml{ggml_version}.fp16.bin" + + print(f"Making unquantised GGML at {fp16}") + if not os.path.isfile(fp16): + subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True) + else: + print(f"Unquantised GGML already exists at: {fp16}") + + print("Making quants") + for type in quants: + outfile = f"{outdir}/{outname}.ggml{ggml_version}.{type}.bin" + print(f"Making {type} : {outfile}") + subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True) + + if not keep_fp16: + os.remove(fp16) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Convert/Quantize HF to GGML. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.') + parser.add_argument('--model', required=True, help='Downloaded model dir or Hugging Face model repo name') + parser.add_argument('--outname', default=None, help='Output model(s) name') + parser.add_argument('--outdir', default=None, help='Output directory') + parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types') + parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False) + + args = parser.parse_args() + + main(args.model, args.outname, args.outdir, args.quants, args.keep_fp16) diff --git a/examples/metal/CMakeLists.txt b/examples/metal/CMakeLists.txt index a8c4284..f16d491 100644 --- a/examples/metal/CMakeLists.txt +++ b/examples/metal/CMakeLists.txt @@ -1,3 +1,4 @@ set(TEST_TARGET metal) add_executable(${TEST_TARGET} metal.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TEST_TARGET} PRIVATE ggml) diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index 61b17b8..af00b4e 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET perplexity) add_executable(${TARGET} perplexity.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 7e120ff..62433e9 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -4,6 +4,7 @@ #include <cmath> #include <ctime> +#include <sstream> #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -32,13 +33,15 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // BOS tokens will be added for each chunk before eval auto tokens = ::llama_tokenize(ctx, params.prompt, true); - int count = 0; + const int n_chunk_max = tokens.size() / params.n_ctx; - const int n_chunk = tokens.size() / params.n_ctx; + const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); const int n_vocab = llama_n_vocab(ctx); const int n_batch = params.n_batch; + int count = 0; double nll = 0.0; + fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); for (int i = 0; i < n_chunk; ++i) { @@ -118,6 +121,178 @@ void perplexity(llama_context * ctx, const gpt_params & params) { printf("\n"); } +void hellaswag_score(llama_context * ctx, const gpt_params & params) { + // Calculates hellaswag score (acc_norm) from prompt + // + // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl + // All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68 + // + // All 10042 tasks should be extracted to keep the results standardized like other implementations. + // + // Datafile layout: + // ['??'] denotes json fields + // 6 lines per task: + // ['activity_label'] + ": " +['ctx'] - The first part of the query, the context + // ['label'] - The index the best common sense ending aka gold ending + // ['endings'][0] - Endings added to the first part of the query + // ['endings'][1] + // ['endings'][2] + // ['endings'][3] + + std::vector<std::string> prompt_lines; + std::istringstream strstream(params.prompt); + std::string line; + + while (std::getline(strstream,line,'\n')) { + prompt_lines.push_back(line); + } + + if( prompt_lines.size() % 6 != 0) { + fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__); + return; + } + + size_t hs_task_count = prompt_lines.size()/6; + fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count); + + // This is needed as usual for LLaMA models + bool prepend_bos = true; + + // Number of tasks to use when computing the score + if ( params.hellaswag_tasks < hs_task_count ) { + hs_task_count = params.hellaswag_tasks; + } + + // The tasks should be randomized so the score stabilizes quickly. + bool randomize_tasks = true; + + // The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now + std::mt19937 rng(1); + + // Dataholder for hellaswag tasks + struct hs_data_t { + std::string context; + size_t gold_ending_idx; + std::string ending[4]; + size_t ending_logprob_count[4]; + double ending_logprob[4]; + }; + + fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") ); + + // Select and read data from prompt lines + hs_data_t *hs_data = new hs_data_t[hs_task_count]; + for (size_t i=0; i < hs_task_count; i++) { + size_t idx = i; + + // Select a random example of those left in the prompt + if (randomize_tasks) { + std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ; + idx = dist(rng); + } + + hs_data[i].context = prompt_lines[idx*6]; + hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); + for (size_t j=0; j < 4; j++) { + hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j]; + } + + // Delete the selected random example from the prompt + if (randomize_tasks) { + prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) ); + } + } + + fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__); + printf("\ntask\tacc_norm\n"); + + double acc = 0.0f; + const int n_vocab = llama_n_vocab(ctx); + + for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) { + + // Tokenize the context to count tokens + std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos); + size_t context_size = context_embd.size(); + + for (size_t ending_idx=0;ending_idx<4;ending_idx++) { + + // Tokenize the query + std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos); + size_t query_size = query_embd.size(); + + // Stop if query wont fit the ctx window + if (query_size > (size_t)params.n_ctx) { + fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size); + return; + } + + // Speedup small evaluations by evaluating atleast 32 tokens + if (query_size < 32) { + query_embd.resize(32); + } + + // Evaluate the query + if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return; + } + + const auto query_logits = llama_get_logits(ctx); + std::vector<float> logits; + logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab); + + hs_data[task_idx].ending_logprob_count[ending_idx] = 0; + hs_data[task_idx].ending_logprob[ending_idx] = 0.0f; + + // Calculate the logprobs over the ending + for (size_t j = context_size-1; j < query_size - 1; j++) { + // Calculate probability of next token, given the previous ones. + const std::vector<float> tok_logits( + logits.begin() + (j + 0) * n_vocab, + logits.begin() + (j + 1) * n_vocab); + + const float prob = softmax(tok_logits)[query_embd[ j + 1]]; + + hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob); + hs_data[task_idx].ending_logprob_count[ending_idx]++; + } + + // Calculate the mean token logprob for acc_norm + hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx]; + + +// printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n", +// task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] ); + } + + // Find the ending with maximum logprob + size_t ending_logprob_max_idx = -1; + double ending_logprob_max_val = -INFINITY; + for (size_t j=0; j < 4; j++) { + if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) { + ending_logprob_max_idx = j; + ending_logprob_max_val = hs_data[task_idx].ending_logprob[j]; + } + } + +// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx); + + // If the gold ending got the maximum logprobe add one accuracy point + if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) { + acc += 1.0; + } + + // Print the accumulated accuracy mean x 100 + printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0); + fflush(stdout); + } + + delete [] hs_data; + + printf("\n"); +} + int main(int argc, char ** argv) { gpt_params params; @@ -166,7 +341,11 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - perplexity(ctx, params); + if (params.hellaswag) { + hellaswag_score(ctx, params); + } else { + perplexity(ctx, params); + } llama_print_timings(ctx); llama_free(ctx); diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index 7bebc11..c5c3940 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -1,4 +1,5 @@ set(TARGET quantize-stats) add_executable(${TARGET} quantize-stats.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 475fc8b..47d0be7 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET quantize) add_executable(${TARGET} quantize.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 797d2f0..744f549 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -14,103 +14,27 @@ struct quant_option { }; static const std::vector<struct quant_option> QUANT_OPTIONS = { - { - "Q4_0", - LLAMA_FTYPE_MOSTLY_Q4_0, - " 3.50G, +0.2499 ppl @ 7B - small, very high quality loss - legacy, prefer using Q3_K_M", - }, - { - "Q4_1", - LLAMA_FTYPE_MOSTLY_Q4_1, - " 3.90G, +0.1846 ppl @ 7B - small, substantial quality loss - legacy, prefer using Q3_K_L", - }, - { - "Q5_0", - LLAMA_FTYPE_MOSTLY_Q5_0, - " 4.30G, +0.0796 ppl @ 7B - medium, balanced quality - legacy, prefer using Q4_K_M", - }, - { - "Q5_1", - LLAMA_FTYPE_MOSTLY_Q5_1, - " 4.70G, +0.0415 ppl @ 7B - medium, low quality loss - legacy, prefer using Q5_K_M", - }, + { "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 3.50G, +0.2499 ppl @ 7B", }, + { "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 3.90G, +0.1846 ppl @ 7B", }, + { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.30G, +0.0796 ppl @ 7B", }, + { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0415 ppl @ 7B", }, #ifdef GGML_USE_K_QUANTS - { - "Q2_K", - LLAMA_FTYPE_MOSTLY_Q2_K, - " 2.67G, +0.8698 ppl @ 7B - smallest, extreme quality loss - not recommended", - }, - { - "Q3_K", - LLAMA_FTYPE_MOSTLY_Q3_K_M, - "alias for Q3_K_M" - }, - { - "Q3_K_S", - LLAMA_FTYPE_MOSTLY_Q3_K_S, - " 2.75G, +0.5505 ppl @ 7B - very small, very high quality loss", - }, - { - "Q3_K_M", - LLAMA_FTYPE_MOSTLY_Q3_K_M, - " 3.06G, +0.2437 ppl @ 7B - very small, very high quality loss", - }, - { - "Q3_K_L", - LLAMA_FTYPE_MOSTLY_Q3_K_L, - " 3.35G, +0.1803 ppl @ 7B - small, substantial quality loss", - }, - { - "Q4_K", - LLAMA_FTYPE_MOSTLY_Q4_K_M, - "alias for Q4_K_M", - }, - { - "Q4_K_S", - LLAMA_FTYPE_MOSTLY_Q4_K_S, - " 3.56G, +0.1149 ppl @ 7B - small, significant quality loss", - }, - { - "Q4_K_M", - LLAMA_FTYPE_MOSTLY_Q4_K_M, - " 3.80G, +0.0535 ppl @ 7B - medium, balanced quality - *recommended*", - }, - { - "Q5_K", - LLAMA_FTYPE_MOSTLY_Q5_K_M, - "alias for Q5_K_M", - }, - { - "Q5_K_S", - LLAMA_FTYPE_MOSTLY_Q5_K_S, - " 4.33G, +0.0353 ppl @ 7B - large, low quality loss - *recommended*", - }, - { - "Q5_K_M", - LLAMA_FTYPE_MOSTLY_Q5_K_M, - " 4.45G, +0.0142 ppl @ 7B - large, very low quality loss - *recommended*", - }, - { - "Q6_K", - LLAMA_FTYPE_MOSTLY_Q6_K, - " 5.15G, +0.0044 ppl @ 7B - very large, extremely low quality loss", - }, + { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.67G, +0.8698 ppl @ 7B", }, + { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, + { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5505 ppl @ 7B", }, + { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.06G, +0.2437 ppl @ 7B", }, + { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1803 ppl @ 7B", }, + { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, + { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.56G, +0.1149 ppl @ 7B", }, + { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0535 ppl @ 7B", }, + { "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", }, + { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0353 ppl @ 7B", }, + { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0142 ppl @ 7B", }, + { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, +0.0044 ppl @ 7B", }, #endif - { - "Q8_0", - LLAMA_FTYPE_MOSTLY_Q8_0, - " 6.70G, +0.0004 ppl @ 7B - very large, extremely low quality loss - not recommended", - }, - { - "F16", - LLAMA_FTYPE_MOSTLY_F16, - "13.00G @ 7B - extremely large, virtually no quality loss - not recommended", - }, - { - "F32", - LLAMA_FTYPE_ALL_F32, - "26.00G @ 7B - absolutely huge, lossless - not recommended", - }, + { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ 7B", }, + { "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", }, + { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, }; diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index 08dbe5c..eadd13c 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET save-load-state) add_executable(${TARGET} save-load-state.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 4c86885..61c71c3 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -26,6 +26,7 @@ int main(int argc, char ** argv) { auto lparams = llama_context_default_params(); lparams.n_ctx = params.n_ctx; + lparams.n_gqa = params.n_gqa; lparams.seed = params.seed; lparams.f16_kv = params.memory_f16; lparams.use_mmap = params.use_mmap; diff --git a/examples/server-llama2-13B.sh b/examples/server-llama2-13B.sh new file mode 100644 index 0000000..17fedc2 --- /dev/null +++ b/examples/server-llama2-13B.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +set -e + +cd "$(dirname "$0")/.." || exit + +# Specify the model you want to use here: +MODEL="${MODEL:-./models/llama-2-13b-chat.ggmlv3.q5_K_M.bin}" +PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat-system.txt} + +# Adjust to the number of CPU cores you want to use. +N_THREAD="${N_THREAD:-12}" + +# Note: you can also override the generation options by specifying them on the command line: +GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}" + + +# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS +./server $GEN_OPTIONS \ + --model "$MODEL" \ + --threads "$N_THREAD" \ + --rope-freq-scale 1.0 \ + "$@" + +# I used this to test the model with mps, but omitted it from the general purpose. If you want to use it, just specify it on the command line. +# -ngl 1 \ diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 07ba76a..3782f9b 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -2,10 +2,14 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) add_executable(${TARGET} server.cpp json.hpp httplib.h) +install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}> ) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +if (WIN32) + TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) +endif() target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) add_dependencies(${TARGET} BUILD_INFO) diff --git a/examples/server/README.md b/examples/server/README.md index 3691abd..e56ca06 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -16,7 +16,7 @@ Command line options: - `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. - `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. - `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model. This allows you to adapt the pretrained model to specific tasks or domains. +- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. @@ -66,6 +66,7 @@ Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the ```sh curl --request POST \ --url http://localhost:8080/completion \ + --header "Content-Type: application/json" \ --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}' ``` @@ -150,6 +151,8 @@ node . `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1). + `grammar`: Set grammar for grammar-based sampling (default: no grammar) + `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed). `ignore_eos`: Ignore end of stream token and continue generating (default: false). @@ -162,7 +165,7 @@ node . `content`: Set the text to tokenize. - Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`. + Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`. - **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does. diff --git a/examples/server/chat-llama2.sh b/examples/server/chat-llama2.sh new file mode 100644 index 0000000..1fc79b7 --- /dev/null +++ b/examples/server/chat-llama2.sh @@ -0,0 +1,109 @@ +#!/bin/bash + +API_URL="${API_URL:-http://127.0.0.1:8080}" + +CHAT=( + "Hello, Assistant." + "Hello. How may I help you today?" +) + +INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions." + +trim() { + shopt -s extglob + set -- "${1##+([[:space:]])}" + printf "%s" "${1%%+([[:space:]])}" +} + +trim_trailing() { + shopt -s extglob + printf "%s" "${1%%+([[:space:]])}" +} + +format_prompt() { + if [[ "${#CHAT[@]}" -eq 0 ]]; then + echo -n "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>" + else + LAST_INDEX=$(( ${#CHAT[@]} - 1 )) + echo -n "${CHAT[$LAST_INDEX]}\n[INST] $1 [/INST]" + fi +} + +tokenize() { + curl \ + --silent \ + --request POST \ + --url "${API_URL}/tokenize" \ + --header "Content-Type: application/json" \ + --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \ + | jq '.tokens[]' +} + +N_KEEP=$(tokenize "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>" | wc -l) + +chat_completion() { + PROMPT="$(trim_trailing "$(format_prompt "$1")")" + DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{ + prompt: ., + temperature: 0.2, + top_k: 40, + top_p: 0.9, + n_keep: $n_keep, + n_predict: 1024, + stop: ["[INST]"], + stream: true + }')" + + # Create a temporary file to hold the Python output + TEMPFILE=$(mktemp) + + exec 3< <(curl \ + --silent \ + --no-buffer \ + --request POST \ + --url "${API_URL}/completion" \ + --header "Content-Type: application/json" \ + --data-raw "${DATA}") + + python -c " +import json +import sys + +answer = '' +while True: + line = sys.stdin.readline() + if not line: + break + if line.startswith('data: '): + json_content = line[6:].strip() + content = json.loads(json_content)['content'] + sys.stdout.write(content) + sys.stdout.flush() + answer += content + +answer = answer.rstrip('\n') + +# Write the answer to the temporary file +with open('$TEMPFILE', 'w') as f: + f.write(answer) + " <&3 + + exec 3<&- + + # Read the answer from the temporary file + ANSWER=$(cat $TEMPFILE) + + # Clean up the temporary file + rm $TEMPFILE + + printf "\n" + + CHAT+=("$1" "$(trim "$ANSWER")") +} + +while true; do + echo -en "\033[0;32m" # Green color + read -r -e -p "> " QUESTION + echo -en "\033[0m" # Reset color + chat_completion "${QUESTION}" +done diff --git a/examples/server/chat.sh b/examples/server/chat.sh index a89f8e9..0143601 100644 --- a/examples/server/chat.sh +++ b/examples/server/chat.sh @@ -32,6 +32,7 @@ tokenize() { --silent \ --request POST \ --url "${API_URL}/tokenize" \ + --header "Content-Type: application/json" \ --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \ | jq '.tokens[]' } @@ -64,6 +65,7 @@ chat_completion() { --no-buffer \ --request POST \ --url "${API_URL}/completion" \ + --header "Content-Type: application/json" \ --data-raw "${DATA}") printf "\n" diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index f399fb1..f0a071a 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -87,289 +87,342 @@ unsigned char completion_js[] = { 0x20, 0x54, 0x65, 0x78, 0x74, 0x44, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 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b/examples/server/public/completion.js @@ -43,6 +43,7 @@ export async function* llama(prompt, params = {}, config = {}) { const decoder = new TextDecoder(); let content = ""; + let leftover = ""; // Buffer for partially read lines try { let cont = true; @@ -53,29 +54,47 @@ export async function* llama(prompt, params = {}, config = {}) { break; } - // sse answers in the form multiple lines of: value\n with data always present as a key. in our case we - // mainly care about the data: key here, which we expect as json - const text = decoder.decode(result.value); + // Add any leftover data to the current chunk of data + const text = leftover + decoder.decode(result.value); - // parse all sse events and add them to result - const regex = /^(\S+):\s(.*)$/gm; - for (const match of text.matchAll(regex)) { - result[match[1]] = match[2] - } + // Check if the last character is a line break + const endsWithLineBreak = text.endsWith('\n'); - // since we know this is llama.cpp, let's just decode the json in data - result.data = JSON.parse(result.data); - content += result.data.content; + // Split the text into lines + let lines = text.split('\n'); - // yield - yield result; + // If the text doesn't end with a line break, then the last line is incomplete + // Store it in leftover to be added to the next chunk of data + if (!endsWithLineBreak) { + leftover = lines.pop(); + } else { + leftover = ""; // Reset leftover if we have a line break at the end + } - // if we got a stop token from server, we will break here - if (result.data.stop) { - if (result.data.generation_settings) { - generation_settings = result.data.generation_settings; + // Parse all sse events and add them to result + const regex = /^(\S+):\s(.*)$/gm; + for (const line of lines) { + const match = regex.exec(line); + if (match) { + result[match[1]] = match[2] + // since we know this is llama.cpp, let's just decode the json in data + if (result.data) { + result.data = JSON.parse(result.data); + content += result.data.content; + + // yield + yield result; + + // if we got a stop token from server, we will break here + if (result.data.stop) { + if (result.data.generation_settings) { + generation_settings = result.data.generation_settings; + } + cont = false; + break; + } + } } - break; } } } catch (e) { diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 8ace0b0..de41da1 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -3,12 +3,11 @@ <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" /> + <meta name="color-scheme" content="light dark"> <title>llama.cpp - chat</title> <style> body { - background-color: #fff; - color: #000; font-family: system-ui; font-size: 90%; } @@ -73,6 +72,37 @@ margin: 0; } + fieldset.two { + display: grid; + grid-template: "a a"; + gap: 1em; + } + + fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + } + + details { + border: 1px solid #aaa; + border-radius: 4px; + padding: 0.5em 0.5em 0; + margin-top: 0.5em; + } + + summary { + font-weight: bold; + margin: -0.5em -0.5em 0; + padding: 0.5em; + cursor: pointer; + } + + details[open] { + padding: 0.5em; + } + + textarea { padding: 5px; flex-grow: 1; @@ -125,10 +155,17 @@ const params = signal({ n_predict: 400, temperature: 0.7, - repeat_last_n: 256, - repeat_penalty: 1.18, - top_k: 40, - top_p: 0.5, + repeat_last_n: 256, // 0 = disable penalty, -1 = context size + repeat_penalty: 1.18, // 1.0 = disabled + top_k: 40, // <= 0 to use vocab size + top_p: 0.5, // 1.0 = disabled + tfs_z: 1.0, // 1.0 = disabled + typical_p: 1.0, // 1.0 = disabled + presence_penalty: 0.0, // 0.0 = disabled + frequency_penalty: 0.0, // 0.0 = disabled + mirostat: 0, // 0/1/2 + mirostat_tau: 5, // target entropy + mirostat_eta: 0.1, // learning rate }) const llamaStats = signal(null) @@ -245,8 +282,9 @@ useEffect(() => { // scroll to bottom (if needed) - if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) { - container.current.scrollTo(0, container.current.scrollHeight) + const parent = container.current.parentElement; + if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) { + parent.scrollTo(0, parent.scrollHeight) } }, [messages]) @@ -264,6 +302,27 @@ const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value } const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value } const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) } + const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) } + + const FloatField = ({label, max, min, name, step, value}) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} /> + <span>${value}</span> + </div> + ` + }; + + const IntField = ({label, max, min, name, value}) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} /> + <span>${value}</span> + </div> + ` + }; return html` <form> @@ -272,7 +331,9 @@ <label for="prompt">Prompt</label> <textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/> </div> + </fieldset> + <fieldset class="two"> <div> <label for="user">User name</label> <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} /> @@ -282,7 +343,9 @@ <label for="bot">Bot name</label> <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} /> </div> + </fieldset> + <fieldset> <div> <label for="template">Prompt template</label> <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/> @@ -292,38 +355,44 @@ <label for="template">Chat history template</label> <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/> </div> + </fieldset> - <div> - <label for="temperature">Temperature</label> - <input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} /> - <span>${params.value.temperature}</span> - </div> - - <div> - <label for="nPredict">Predictions</label> - <input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} /> - <span>${params.value.n_predict}</span> - </div> - - <div> - <label for="repeat_penalty">Penalize repeat sequence</label> - <input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} /> - <span>${params.value.repeat_penalty}</span> - </div> - - <div> - <label for="repeat_last_n">Consider N tokens for penalize</label> - <input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} /> - <span>${params.value.repeat_last_n}</span> - </div> - + <fieldset class="two"> + ${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})} + ${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})} + ${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})} + ${IntField({label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n})} + ${IntField({label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k})} + ${FloatField({label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p})} </fieldset> + <details> + <summary>More options</summary> + <fieldset class="two"> + ${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})} + ${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})} + ${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})} + ${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})} + </fieldset> + <hr /> + <fieldset class="three"> + <div> + <label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label> + <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label> + <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label> + </div> + ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})} + ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})} + </fieldset> + </details> </form> ` } // poor mans markdown replacement const Markdownish = (params) => { const md = params.text + .replace(/&/g, '&') + .replace(/</g, '<') + .replace(/>/g, '>') .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>') .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>') .replace(/__(.*?)__/g, '<strong>$1</strong>') diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 4114343..10ae264 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,7 @@ #include "common.h" #include "llama.h" #include "build-info.h" +#include "grammar-parser.h" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -195,6 +196,8 @@ struct llama_server_context llama_context *ctx = nullptr; gpt_params params; + llama_grammar *grammar = nullptr; + bool truncated = false; bool stopped_eos = false; bool stopped_word = false; @@ -226,6 +229,7 @@ struct llama_server_context void rewind() { params.antiprompt.clear(); + params.grammar.clear(); num_prompt_tokens = 0; num_tokens_predicted = 0; generated_text = ""; @@ -237,6 +241,7 @@ struct llama_server_context stopped_limit = false; stopping_word = ""; multibyte_pending = 0; + grammar = nullptr; n_remain = 0; n_past = 0; @@ -257,6 +262,33 @@ struct llama_server_context return true; } + bool loadGrammar() + { + if (!params.grammar.empty()) { + grammar_parser::parse_state parsed_grammar; + + parsed_grammar = grammar_parser::parse(params.grammar.c_str()); + // will be empty (default) if there are parse errors + if (parsed_grammar.rules.empty()) { + LOG_ERROR("grammar parse error", {{"grammar", params.grammar}}); + return false; + } + grammar_parser::print_grammar(stderr, parsed_grammar); + + { + auto it = params.logit_bias.find(llama_token_eos()); + if (it != params.logit_bias.end() && it->second == -INFINITY) { + LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {}); + } + } + + std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules()); + grammar = llama_grammar_init( + grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + } + return true; + } + void loadPrompt() { params.prompt.insert(0, 1, ' '); // always add a first space @@ -420,6 +452,10 @@ struct llama_server_context logits[llama_token_nl()] = nl_logit; } + if (grammar != nullptr) { + llama_sample_grammar(ctx, &candidates_p, grammar); + } + if (temp <= 0) { // Greedy sampling @@ -457,10 +493,15 @@ struct llama_server_context } } + if (grammar != nullptr) { + llama_grammar_accept_token(ctx, grammar, result.tok); + } + for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i) { result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p}); } + last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(result.tok); num_tokens_predicted++; @@ -601,45 +642,52 @@ struct llama_server_context static void server_print_usage(const char *argv0, const gpt_params ¶ms, const server_params &sparams) { - fprintf(stderr, "usage: %s [options]\n", argv0); - fprintf(stderr, "\n"); - fprintf(stderr, "options:\n"); - fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n"); + 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, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa); + fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps); + 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"); if (llama_mlock_supported()) { - fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); + fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, " -ngl N, --n-gpu-layers N\n"); - fprintf(stderr, " number of layers to store in VRAM\n"); - fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n"); - fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); - fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n"); + 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, " 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, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" ); + fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" ); + fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" ); #endif - fprintf(stderr, " -m FNAME, --model FNAME\n"); - fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); - fprintf(stderr, " -a ALIAS, --alias ALIAS\n"); - fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n"); - fprintf(stderr, " --lora FNAME apply LoRA adapter\n"); - fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port); - fprintf(stderr, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); - fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); - fprintf(stderr, "\n"); + 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"); } static void server_params_parse(int argc, char **argv, server_params &sparams, @@ -722,6 +770,41 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_ctx = std::stoi(argv[i]); } + else if (arg == "-gqa" || arg == "--gqa") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.n_gqa = std::stoi(argv[i]); + } + else if (arg == "-eps" || arg == "--rms-norm-eps") { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.rms_norm_eps = std::stof(argv[i]); + } + else if (arg == "--rope-freq-base") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.rope_freq_base = std::stof(argv[i]); + } + else if (arg == "--rope-freq-scale") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + params.rope_freq_scale = std::stof(argv[i]); + } else if (arg == "--memory-f32" || arg == "--memory_f32") { params.memory_f16 = false; @@ -788,7 +871,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } #else - LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.", {}); + LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--low-vram" || arg == "-lv") @@ -796,7 +879,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, #ifdef GGML_USE_CUBLAS params.low_vram = true; #else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n"); + LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {}); +#endif // GGML_USE_CUBLAS + } + else if (arg == "--mul-mat-q" || arg == "-mmq") + { +#ifdef GGML_USE_CUBLAS + params.mul_mat_q = true; +#else + LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") @@ -820,6 +911,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.lora_adapter = argv[i]; + params.use_mmap = false; } else if (arg == "--lora-base") { @@ -896,6 +988,7 @@ static json format_generation_settings(llama_server_context &llama) {"stream", llama.stream}, {"logit_bias", llama.params.logit_bias}, {"n_probs", llama.params.n_probs}, + {"grammar", llama.params.grammar}, }; } @@ -997,6 +1090,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla llama.params.n_keep = body.value("n_keep", default_params.n_keep); llama.params.seed = body.value("seed", default_params.seed); llama.params.prompt = body.value("prompt", default_params.prompt); + llama.params.grammar = body.value("grammar", default_params.grammar); llama.params.n_probs = body.value("n_probs", default_params.n_probs); llama.params.logit_bias.clear(); @@ -1128,6 +1222,12 @@ int main(int argc, char **argv) parse_options_completion(json::parse(req.body), llama); + if (!llama.loadGrammar()) + { + res.status = 400; + return; + } + llama.loadPrompt(); llama.beginCompletion(); @@ -1223,7 +1323,11 @@ int main(int argc, char **argv) sink.done(); return true; }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider); + const auto on_complete = [&](bool) { + llama.mutex.unlock(); + }; + lock.release(); + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }); svr.Get("/model.json", [&llama](const Request &, Response &res) @@ -1279,8 +1383,12 @@ int main(int argc, char **argv) svr.set_error_handler([](const Request &, Response &res) { - res.set_content("File Not Found", "text/plain"); - res.status = 404; }); + if (res.status == 400) { + res.set_content("Invalid request", "text/plain"); + } else { + res.set_content("File Not Found", "text/plain"); + res.status = 404; + } }); // set timeouts and change hostname and port svr.set_read_timeout(sparams.read_timeout); @@ -1308,6 +1416,9 @@ int main(int argc, char **argv) return 1; } + if (llama.grammar != nullptr) { + llama_grammar_free(llama.grammar); + } llama_backend_free(); return 0; diff --git a/examples/simple/CMakeLists.txt b/examples/simple/CMakeLists.txt index 1568f73..0ac9cb0 100644 --- a/examples/simple/CMakeLists.txt +++ b/examples/simple/CMakeLists.txt @@ -1,5 +1,6 @@ set(TARGET simple) add_executable(${TARGET} simple.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if(TARGET BUILD_INFO) diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index aa2c435..97137a6 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -123,7 +123,7 @@ int main(int argc, char ** argv) // Evaluate the tokens : //--------------------------------- - if ( llama_eval( ctx , tokens_list.data() , tokens_list.size() , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) ) + if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) ) { fprintf( stderr, "%s : failed to eval\n" , __func__ ); return 1; diff --git a/examples/train-text-from-scratch/CMakeLists.txt b/examples/train-text-from-scratch/CMakeLists.txt index 1a44c49..4459516 100644 --- a/examples/train-text-from-scratch/CMakeLists.txt +++ b/examples/train-text-from-scratch/CMakeLists.txt @@ -1,4 +1,5 @@ set(TARGET train-text-from-scratch) add_executable(${TARGET} train-text-from-scratch.cpp) +install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index afbb4a7..54dc2be 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -16,6 +16,8 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; + struct random_normal_distribution { std::mt19937 gen; std::normal_distribution<float> rd; @@ -439,7 +441,7 @@ struct ggml_tensor * forward( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // cur = attention_norm*cur cur = ggml_mul(ctx0, @@ -562,7 +564,7 @@ struct ggml_tensor * forward( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); // cur = ffn_norm*cur // cur shape [n_embd,N,1,1] @@ -606,7 +608,7 @@ struct ggml_tensor * forward( { // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // inpL = norm*inpL // inpL shape [n_embd,N,1,1] @@ -694,7 +696,7 @@ struct ggml_tensor * forward_batch( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = attention_norm*cur @@ -857,7 +859,7 @@ struct ggml_tensor * forward_batch( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = ffn_norm*cur @@ -910,7 +912,7 @@ struct ggml_tensor * forward_batch( { // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(inpL, n_embd, N*n_batch); // inpL = norm*inpL @@ -979,7 +981,7 @@ struct ggml_tensor * forward_batch_wo_cache( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = attention_norm*cur @@ -1085,7 +1087,7 @@ struct ggml_tensor * forward_batch_wo_cache( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = ffn_norm*cur @@ -1138,7 +1140,7 @@ struct ggml_tensor * forward_batch_wo_cache( { // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(inpL, n_embd, N*n_batch); // inpL = norm*inpL @@ -1203,7 +1205,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn( // norm { - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = attention_norm*cur @@ -1267,7 +1269,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn( { // norm { - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = ffn_norm*cur @@ -1311,7 +1313,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn( // norm { - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(inpL, n_embd, N*n_batch); // inpL = norm*inpL @@ -1434,7 +1436,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( gf->perf_time_us = 0; const auto & hparams = model->hparams; - //const int n_ctx = hparams.n_ctx; + const int n_ctx = hparams.n_ctx; const int n_vocab = hparams.n_vocab; const int n_embd = hparams.n_embd; const int n_layer = hparams.n_layer; @@ -1603,7 +1605,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( struct my_llama_layer & layer = model->layers[il]; // tensors with values necessary for backward pass are in persistent buf(-1) // other tensors with buf(0) and buf(1) are only temporary needed, and their memory reused after layer is completed. - use_buf(-1); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t02, n_embd, N*n_batch); + use_buf(-1); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm (ctx0, cur, rms_norm_eps)); assert_shape_2d(t02, n_embd, N*n_batch); use_buf( 0); struct ggml_tensor * t03 = expand(gf, ggml_repeat (ctx0, layer.attention_norm, t02)); assert_shape_2d(t03, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t04 = expand(gf, ggml_mul (ctx0, t02, t03)); assert_shape_2d(t04, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat (ctx0, layer.wq, t04)); assert_shape_2d(t05, n_embd, N*n_batch); @@ -1623,7 +1625,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( use_buf(-1); struct ggml_tensor * t19 = expand(gf, ggml_reshape_2d (ctx0, t18, n_embd, N*n_batch)); assert_shape_2d(t19, n_embd, N*n_batch); use_buf( 0); struct ggml_tensor * t20 = expand(gf, ggml_mul_mat (ctx0, layer.wo, t19)); assert_shape_2d(t20, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t21 = expand(gf, ggml_add (ctx0, t20, cur)); assert_shape_2d(t21, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm (ctx0, t21)); assert_shape_2d(t22, n_embd, N*n_batch); + use_buf(-1); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm (ctx0, t21, rms_norm_eps)); assert_shape_2d(t22, n_embd, N*n_batch); use_buf( 0); struct ggml_tensor * t23 = expand(gf, ggml_repeat (ctx0, layer.ffn_norm, t22)); assert_shape_2d(t23, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t24 = expand(gf, ggml_mul (ctx0, t23, t22)); assert_shape_2d(t24, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t25 = expand(gf, ggml_mul_mat (ctx0, layer.w3, t24)); assert_shape_2d(t25, n_ff, N*n_batch); @@ -1666,7 +1668,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( } clr_buf(0); use_buf(0); - struct ggml_tensor * t31 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t31, n_embd, N*n_batch); + struct ggml_tensor * t31 = expand(gf, ggml_rms_norm (ctx0, cur, rms_norm_eps)); assert_shape_2d(t31, n_embd, N*n_batch); struct ggml_tensor * t32 = expand(gf, ggml_repeat (ctx0, model->norm, t31)); assert_shape_2d(t32, n_embd, N*n_batch); struct ggml_tensor * t33 = expand(gf, ggml_mul (ctx0, t32, t31)); assert_shape_2d(t33, n_embd, N*n_batch); use_buf(-1); @@ -1863,10 +1865,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1)); assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head); t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd)); assert_shape_2d(t11->grad, N*n_batch, n_embd); t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3)); assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch); - t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch); + t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch); t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch)); assert_shape_2d(t08->grad, n_embd, N*n_batch); t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3)); assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch); - t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch); + t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch); t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch)); assert_shape_2d(t05->grad, n_embd, N*n_batch); t04->grad = expand(gb, ggml_add_inplace(ctx0, ggml_add_inplace(ctx0, |