diff --git a/common/common.cpp b/common/common.cpp index 6d655fd5548c5..2597ba06aee16 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -647,9 +647,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --cfg-negative-prompt-file FNAME\n"); printf(" negative prompt file to use for guidance. (default: empty)\n"); printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); - printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale); - printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base); - printf(" --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale); + printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale\n"); + printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n"); + printf(" --rope-freq-scale N RoPE frequency linear scaling factor (default: loaded from model)\n"); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index c8f7d486976d7..f1c382aa9b955 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -21,7 +21,7 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { +static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { struct ggml_cplan plan = ggml_graph_plan(graph, n_threads); if (plan.work_size > 0) { @@ -32,7 +32,7 @@ void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, ggml_graph_compute(graph, &plan); } -float tensor_sum_elements(const ggml_tensor * tensor) { +static float tensor_sum_elements(const ggml_tensor * tensor) { double sum = 0; if (tensor->type == GGML_TYPE_F32) { for (int j = 0; j < tensor->ne[1]; j++) { @@ -44,7 +44,7 @@ float tensor_sum_elements(const ggml_tensor * tensor) { return sum; } -void tensor_dump(const ggml_tensor * tensor, const char * name) { +static void tensor_dump(const ggml_tensor * tensor, const char * name) { printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name, tensor->type, ggml_type_name(tensor->type), tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]); @@ -59,7 +59,7 @@ struct benchmark_params_struct { int32_t n_iterations = 10; }; -void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct params) { +static void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct params) { fprintf(stderr, "usage: %s [options]\n", argv[0]); fprintf(stderr, "\n"); fprintf(stderr, "options:\n"); @@ -253,7 +253,7 @@ int main(int argc, char ** argv) { // Check that the matrix multiplication result is in the right ballpark // We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]); - float delta = abs(sum_of_Q4_result - sum_of_F32_reference); + float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference); float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6 if (delta > allowed_delta) { diff --git a/examples/embedding/README.md b/examples/embedding/README.md index fe8f5dcc62ed9..6929454c5e549 100644 --- a/examples/embedding/README.md +++ b/examples/embedding/README.md @@ -1,3 +1,21 @@ -# embedding +# llama.cpp/example/embedding -TODO +This example demonstrates generate high-dimensional embedding vector of a given text with llama.cpp. + +## Quick Start + +To get started right away, run the following command, making sure to use the correct path for the model you have: + +### Unix-based systems (Linux, macOS, etc.): + +```bash +./embedding -m ./path/to/model --log-disable -p "Hello World!" 2>/dev/null +``` + +### Windows: + +```powershell +embedding.exe -m ./path/to/model --log-disable -p "Hello World!" 2>$null +``` + +The above command will output space-separated float values. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1bb8e92c0f95e..ebd7f2fc579e9 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -701,8 +701,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - printf(" --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base); - printf(" --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale); + printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); + printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 955d4e9c2945e..9225749dd48b4 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -850,7 +850,7 @@ std::array mul_str_values = { "mul_f32", "float" }; -std::string& replace(std::string& s, const std::string& from, const std::string& to) { +static std::string& replace(std::string& s, const std::string& from, const std::string& to) { size_t pos = 0; while ((pos = s.find(from, pos)) != std::string::npos) { s.replace(pos, from.length(), to); @@ -859,7 +859,7 @@ std::string& replace(std::string& s, const std::string& from, const std::string& return s; } -std::string generate_kernels() { +static std::string generate_kernels() { std::stringstream src; src << program_source << '\n'; src << k_quants_source << '\n'; @@ -1808,7 +1808,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens return false; } -bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) { +static bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) { // If device doesn't support FP16 if (!fp16_support) { return false; diff --git a/llama.cpp b/llama.cpp index c07c35466ff03..e9eae1d04864a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -934,23 +934,22 @@ static const size_t kB = 1024; static const size_t MB = kB*kB; static const size_t GB = kB*kB*kB; -// default hparams (LLaMA 7B) struct llama_hparams { - uint32_t n_vocab = 32000; - uint32_t n_ctx_train = 2048; // the context size used during training - uint32_t n_ctx = 512; // the context size used during inference - uint32_t n_embd = 4096; - uint32_t n_head = 32; - uint32_t n_head_kv = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; - uint32_t n_ff = 11008; - - float f_norm_eps = 1e-5; - float f_norm_rms_eps = 1e-5; - - float rope_freq_base = 10000.0f; - float rope_freq_scale = 1.0f; + uint32_t n_vocab; + uint32_t n_ctx_train; // context size the model was trained on + uint32_t n_ctx; // context size used during inference + uint32_t n_embd; + uint32_t n_head; + uint32_t n_head_kv; + uint32_t n_layer; + uint32_t n_rot; + uint32_t n_ff; + + float f_norm_eps; + float f_norm_rms_eps; + + float rope_freq_base; + float rope_freq_scale; bool operator!=(const llama_hparams & other) const { return static_cast(memcmp(this, &other, sizeof(llama_hparams))); // NOLINT @@ -1081,7 +1080,7 @@ struct llama_model { std::string name = "n/a"; - llama_hparams hparams; + llama_hparams hparams = {}; llama_vocab vocab; struct ggml_tensor * tok_embeddings; @@ -1680,28 +1679,17 @@ static void llm_load_hparams( hparams.n_head_kv = hparams.n_head; GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV)); - // TODO: manually setting rope freq base and scale should override this - // FIXME: partial fix when the param specified is not the default value, but - // will not work for overriding the model value to the params default - - llama_context_params defaults = llama_context_default_params(); - - // rope_freq_base - { - float ropebase = 10000.0f; - GGUF_GET_KEY(ctx, ropebase, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); - if (ropebase != 10000.0f && rope_freq_base == defaults.rope_freq_base) { - rope_freq_base = ropebase; - } + // rope_freq_base (optional) + if (rope_freq_base == 0.0f) { + rope_freq_base = 10000.0f; + GGUF_GET_KEY(ctx, rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); } // rope_freq_scale (inverse of the kv) is optional - { + if (rope_freq_scale == 0.0f) { float ropescale = 1.0f; GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); - if (ropescale != 1.0f && rope_freq_scale == defaults.rope_freq_scale) { - rope_freq_scale = 1.0f/ropescale; - } + rope_freq_scale = 1.0f/ropescale; } // sanity check for n_rot (optional) @@ -3787,6 +3775,15 @@ static bool llama_eval_internal( n_threads = std::min(4, n_threads); } + // If all tensors can be run on the GPU then using more than 1 thread is detrimental. + const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA || + model.arch == LLM_ARCH_BAICHUAN || + model.arch == LLM_ARCH_FALCON; + const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3; + if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) { + n_threads = 1; + } + struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; @@ -6201,8 +6198,8 @@ struct llama_context_params llama_context_default_params() { /*.n_gpu_layers =*/ 0, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, - /*.rope_freq_base =*/ 10000.0f, - /*.rope_freq_scale =*/ 1.0f, + /*.rope_freq_base =*/ 0.0f, + /*.rope_freq_scale =*/ 0.0f, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.low_vram =*/ false,