From 6e7cca404748dd4b1a3affd0d1296e37f4ac0a6f Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Sat, 15 Jul 2023 06:34:16 -0400 Subject: llama : add custom RoPE (#2054) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Implement customizable RoPE The original RoPE has pre-defined parameters theta_i = 10000^(āˆ’2(iāˆ’1)/d), for i in [1, 2, ..., d/2] Our customizable RoPE, ggml_rope_custom_inplace, uses theta_i = scale * base^(āˆ’2(iāˆ’1)/d), for i in [1, 2, ..., d/2] with the default matches the original scale = 1.0 base = 10000 The new command line arguments --rope-freq-base --rope-freq-scale set the two new RoPE parameter. Recent researches show changing these two parameters extends the context limit with minimal loss. 1. Extending Context to 8K kaiokendev https://kaiokendev.github.io/til#extending-context-to-8k 2. Extending Context Window of Large Language Models via Positional Interpolation Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian https://arxiv.org/abs/2306.15595 3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation. https://www.reddit.com/user/bloc97 https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ For the bold, try adding the following command line parameters to your favorite model: -c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5 * ggml-metal: fix custom rope * common: fix argument names in help * llama: increase MEM_REQ_EVAL for MODEL_3B It avoids crashing for quantized weights on CPU. Better ways to calculate the required buffer size would be better. * llama: make MEM_REQ_EVAL depend on n_ctx * server: use proper Content-Type in curl examples Without the header Content-Type: application/json, curl will POST with Content-Type: application/x-www-form-urlencoded Though our simple server doesn't care, the httplib.h used has a limit with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 With Content-Type: application/json, we can send large json data. * style : minor fixes, mostly indentations * ggml : fix asserts --------- Co-authored-by: Georgi Gerganov --- llama.cpp | 84 +++++++++++++++++++++++++++++++++++++++------------------------ 1 file changed, 52 insertions(+), 32 deletions(-) (limited to 'llama.cpp') diff --git a/llama.cpp b/llama.cpp index b0cd941..27e1ee9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -101,14 +101,15 @@ static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * // memory sizes // -static const std::map & MEM_REQ_SCRATCH0() +static const std::map & MEM_REQ_SCRATCH0(int n_ctx) { static std::map k_sizes = { - { MODEL_3B, 256ull * MB }, - { MODEL_7B, 512ull * MB }, - { MODEL_13B, 512ull * MB }, - { MODEL_30B, 512ull * MB }, - { MODEL_65B, 1024ull * MB }, + /* empirical scaling, still a guess */ + { MODEL_3B, ((size_t) n_ctx / 16ull + 128ull) * MB }, + { MODEL_7B, ((size_t) n_ctx / 16ull + 256ull) * MB }, + { MODEL_13B, ((size_t) n_ctx / 12ull + 256ull) * MB }, + { MODEL_30B, ((size_t) n_ctx / 10ull + 256ull) * MB }, + { MODEL_65B, ((size_t) n_ctx / 8ull + 512ull) * MB }, }; return k_sizes; } @@ -140,14 +141,14 @@ static const std::map & MEM_REQ_KV_SELF() // this is mostly needed for temporary mul_mat buffers to dequantize the data // not actually needed if BLAS is disabled -static const std::map & MEM_REQ_EVAL() +static const std::map & MEM_REQ_EVAL(int n_ctx) { static std::map k_sizes = { - { MODEL_3B, 512ull * MB }, - { MODEL_7B, 768ull * MB }, - { MODEL_13B, 1024ull * MB }, - { MODEL_30B, 1280ull * MB }, - { MODEL_65B, 1536ull * MB }, + { MODEL_3B, ((size_t) n_ctx / 256ull + 512ull) * MB }, + { MODEL_7B, ((size_t) n_ctx / 256ull + 768ull) * MB }, + { MODEL_13B, ((size_t) n_ctx / 256ull + 1024ull) * MB }, + { MODEL_30B, ((size_t) n_ctx / 256ull + 1280ull) * MB }, + { MODEL_65B, ((size_t) n_ctx / 256ull + 1536ull) * MB }, }; return k_sizes; } @@ -189,6 +190,10 @@ struct llama_hparams { uint32_t n_head = 32; uint32_t n_layer = 32; uint32_t n_rot = 64; + + float rope_freq_base = 10000.0f; + float rope_freq_scale = 1.0f; + enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16; bool operator!=(const llama_hparams & other) const { @@ -647,7 +652,7 @@ struct llama_model_loader { *ctx_size_p = *mmapped_size_p = 0; for (const llama_load_tensor & lt : tensors_map.tensors) { *ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE; - *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size; + *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size + 16; } } @@ -843,6 +848,8 @@ struct llama_context_params llama_context_default_params() { /*.gpu_layers =*/ 0, /*.main_gpu =*/ 0, /*.tensor_split =*/ {0}, + /*.rope_freq_base =*/ 10000.0f, + /*.rope_freq_scale =*/ 1.0f, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.low_vram =*/ false, @@ -966,6 +973,8 @@ static void llama_model_load_internal( int n_gpu_layers, int main_gpu, const float * tensor_split, + float rope_freq_base, + float rope_freq_scale, bool low_vram, ggml_type memory_type, bool use_mmap, @@ -1000,22 +1009,27 @@ static void llama_model_load_internal( } hparams.n_ctx = n_ctx; + + hparams.rope_freq_base = rope_freq_base; + hparams.rope_freq_scale = rope_freq_scale; } const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult; { - fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version)); - fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab); - fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx); - fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd); - fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult); - fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head); - fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer); - fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot); + fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version)); + fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab); + fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx); + fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd); + fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult); + fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head); + fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer); + fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot); + fprintf(stderr, "%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base); + fprintf(stderr, "%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale); fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype)); - fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff); - fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type)); + fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff); + fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type)); } if (file_version < LLAMA_FILE_VERSION_GGJT_V2) { @@ -1164,9 +1178,9 @@ static void llama_model_load_internal( const size_t mem_required = ctx_size + mmapped_size - vram_weights + // weights in VRAM not in memory - MEM_REQ_SCRATCH0().at(model.type) + + MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) + MEM_REQ_SCRATCH1().at(model.type) + - MEM_REQ_EVAL().at (model.type); + MEM_REQ_EVAL(hparams.n_ctx).at(model.type); // this is the memory required by one llama_state const size_t mem_required_state = @@ -1270,6 +1284,8 @@ static bool llama_model_load( int n_gpu_layers, int main_gpu, float * tensor_split, + float rope_freq_base, + float rope_freq_scale, bool low_vram, ggml_type memory_type, bool use_mmap, @@ -1278,7 +1294,7 @@ static bool llama_model_load( llama_progress_callback progress_callback, void *progress_callback_user_data) { try { - llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type, + llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type, use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data); return true; } catch (const std::exception & err) { @@ -1330,6 +1346,9 @@ static bool llama_eval_internal( const int n_rot = hparams.n_embd/hparams.n_head; const int n_gpu_layers = model.n_gpu_layers; + const float freq_base = hparams.rope_freq_base; + const float freq_scale = hparams.rope_freq_scale; + auto & mem_per_token = lctx.mem_per_token; auto & buf_compute = lctx.buf_compute; @@ -1427,11 +1446,11 @@ static bool llama_eval_internal( offload_func_kq(tmpq); ggml_set_name(tmpq, "tmpq"); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0); offload_func_kq(Kcur); ggml_set_name(Kcur, "Kcur"); - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0); offload_func_kq(Qcur); ggml_set_name(Qcur, "Qcur"); @@ -2674,8 +2693,9 @@ struct llama_model * llama_load_model_from_file( ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers, - params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock, - params.vocab_only, params.progress_callback, params.progress_callback_user_data)) { + params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram, + memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback, + params.progress_callback_user_data)) { delete model; fprintf(stderr, "%s: failed to load model\n", __func__); return nullptr; @@ -2750,9 +2770,9 @@ struct llama_context * llama_new_context_with_model( ctx->embedding.resize(hparams.n_embd); } - ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type)); + ctx->buf_compute.resize(MEM_REQ_EVAL(hparams.n_ctx).at(ctx->model.type)); - ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type)); + ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type)); ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type)); } -- cgit v1.2.3