Merge branch 'master' into sync
ggml-ci
This commit is contained in:
Georgi Gerganov
commit
075ee61191
8 changed files with 62 additions and 38 deletions
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@ -2,7 +2,6 @@
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[](https://github.com/ggerganov/llama.cpp/actions)
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[](https://opensource.org/licenses/MIT)
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[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
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@ -11,8 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
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### Hot topics
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- LLaVA support: https://github.com/ggerganov/llama.cpp/pull/3436
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- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252)
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- ⚠️ **Upcoming change that might break functionality. Help with testing is needed:** https://github.com/ggerganov/llama.cpp/pull/3912
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----
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@ -11,7 +11,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
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if(NOT IS_DIRECTORY "${GIT_DIR}")
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file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
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string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
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set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}")
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set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
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endif()
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set(GIT_INDEX "${GIT_DIR}/index")
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@ -403,6 +403,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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break;
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}
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params.n_sequences = std::stoi(argv[i]);
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} else if (arg == "--p-accept" || arg == "-pa") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.p_accept = std::stof(argv[i]);
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} else if (arg == "--p-split" || arg == "-ps") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.p_split = std::stof(argv[i]);
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} else if (arg == "-m" || arg == "--model") {
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if (++i >= argc) {
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invalid_param = true;
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@ -778,6 +790,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
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printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
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printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
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printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept);
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printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
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printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
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printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
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printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n");
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@ -43,30 +43,34 @@ extern char const *LLAMA_BUILD_TARGET;
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int32_t get_num_physical_cores();
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struct gpt_params {
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uint32_t seed = -1; // RNG seed
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uint32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_draft = 16; // number of tokens to draft during speculative decoding
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int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
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int32_t n_parallel = 1; // number of parallel sequences to decode
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int32_t n_sequences = 1; // number of sequences to decode
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int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
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int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
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int32_t n_beams = 0; // if non-zero then use beam search of given width.
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float rope_freq_base = 0.0f; // RoPE base frequency
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float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
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float yarn_ext_factor = NAN; // YaRN extrapolation mix factor
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float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
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float yarn_beta_fast = 32.0f;// YaRN low correction dim
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float yarn_beta_slow = 1.0f; // YaRN high correction dim
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED;
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int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_draft = 16; // number of tokens to draft during speculative decoding
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int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
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int32_t n_parallel = 1; // number of parallel sequences to decode
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int32_t n_sequences = 1; // number of sequences to decode
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float p_accept = 0.5f; // speculative decoding accept probability
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float p_split = 0.1f; // speculative decoding split probability
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int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
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int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
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int32_t n_beams = 0; // if non-zero then use beam search of given width.
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float rope_freq_base = 0.0f; // RoPE base frequency
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float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
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float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
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float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
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float yarn_beta_fast = 32.0f; // YaRN low correction dim
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float yarn_beta_slow = 1.0f; // YaRN high correction dim
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
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// pinging @cebtenzzre
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// // sampling parameters
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struct llama_sampling_params sparams;
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@ -90,7 +94,7 @@ struct gpt_params {
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int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
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// (which is more convenient to use for plotting)
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//
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bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
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bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
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@ -37,9 +37,11 @@ int main(int argc, char ** argv) {
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// max number of parallel drafting sequences (i.e. tree branches)
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const int n_seq_dft = params.n_parallel;
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// TODO: make this configurable
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const float p_accept = 0.80f;
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const float p_split = 0.10f;
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// probability threshold for accepting a token from the draft model
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const float p_accept = params.p_accept;
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// probability threshold for splitting a draft branch (only for n_seq_dft > 1)
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const float p_split = params.p_split;
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#ifndef LOG_DISABLE_LOGS
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log_set_target(log_filename_generator("speculative", "log"));
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@ -39,6 +39,10 @@
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#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
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#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
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#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
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#define cudaDeviceGetMemPool hipDeviceGetMemPool
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#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold
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#define cudaMemPoolSetAttribute hipMemPoolSetAttribute
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#define cudaMemPool_t hipMemPool_t
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#define cudaDeviceProp hipDeviceProp_t
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#define cudaDeviceSynchronize hipDeviceSynchronize
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#define cudaError_t hipError_t
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@ -48,6 +52,7 @@
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#define cudaEvent_t hipEvent_t
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#define cudaEventDestroy hipEventDestroy
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#define cudaFree hipFree
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#define cudaFreeAsync hipFreeAsync
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#define cudaFreeHost hipHostFree
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#define cudaGetDevice hipGetDevice
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#define cudaGetDeviceCount hipGetDeviceCount
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@ -55,6 +60,7 @@
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#define cudaGetErrorString hipGetErrorString
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#define cudaGetLastError hipGetLastError
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#define cudaMalloc hipMalloc
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#define cudaMallocFromPoolAsync hipMallocFromPoolAsync
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#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
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#define cudaMemcpy hipMemcpy
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#define cudaMemcpy2DAsync hipMemcpy2DAsync
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@ -7982,7 +7982,7 @@ struct llama_context_params llama_context_default_params() {
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/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
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/*.rope_freq_base =*/ 0.0f,
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/*.rope_freq_scale =*/ 0.0f,
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/*.yarn_ext_factor =*/ NAN,
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/*.yarn_ext_factor =*/ -1.0f,
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/*.yarn_attn_factor =*/ 1.0f,
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/*.yarn_beta_fast =*/ 32.0f,
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/*.yarn_beta_slow =*/ 1.0f,
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@ -8125,7 +8125,7 @@ struct llama_context * llama_new_context_with_model(
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cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
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}
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if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set'
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if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
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cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
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}
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10
llama.h
10
llama.h
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@ -175,11 +175,11 @@ extern "C" {
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};
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struct llama_context_params {
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uint32_t seed; // RNG seed, -1 for random
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uint32_t n_ctx; // text context, 0 = from model
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uint32_t n_batch; // prompt processing maximum batch size
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uint32_t n_threads; // number of threads to use for generation
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uint32_t n_threads_batch; // number of threads to use for batch processing
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uint32_t seed; // RNG seed, -1 for random
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uint32_t n_ctx; // text context, 0 = from model
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uint32_t n_batch; // prompt processing maximum batch size
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uint32_t n_threads; // number of threads to use for generation
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uint32_t n_threads_batch; // number of threads to use for batch processing
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int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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// ref: https://github.com/ggerganov/llama.cpp/pull/2054
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