mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 23:29:44 +00:00
Reduce memory usage and allocate enough memory for largest context (#473)
* Reduce memory usage and allocate enough memory for large contexts * Simpler scratch buffer usage * Reenable BLAS for quantized mul_mat * Fix number of layers in 30B and 65B * Fix KV cache size for F32
This commit is contained in:
Georgi Gerganov
GitHub
parent
31572d9665
commit
7a9b6c3a8b
5 changed files with 307 additions and 80 deletions
12
ggml.c
12
ggml.c
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@ -5846,7 +5846,8 @@ static bool ggml_compute_forward_mul_mat_use_blas(
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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UNUSED(src0);
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const int ne00 = src0->ne[0];
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const int ne01 = src0->ne[1];
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const int ne10 = src1->ne[0];
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@ -5856,7 +5857,14 @@ static bool ggml_compute_forward_mul_mat_use_blas(
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// TODO: find the optimal values for these
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if (ggml_is_contiguous(src0) &&
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ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) {
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//printf("BLAS: %d %d %d\n", ne0, ne1, ne10);
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//// disable BLAS for Q4_0 and Q4_1
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//// looks like there is no benefit and we only waste a lot of memory
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//if (src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1) {
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// return false;
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//}
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//printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);
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return true;
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}
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326
llama.cpp
326
llama.cpp
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@ -5,12 +5,25 @@
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#include <cinttypes>
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#include <fstream>
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#include <random>
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#include <map>
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#include <unordered_map>
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#include <queue>
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#include <regex>
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#include <cassert>
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#include <cstring>
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#define LLAMA_USE_SCRATCH
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#define LLAMA_MAX_SCRATCH_BUFFERS 16
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#define LLAMA_ASSERT(x) \
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do { \
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if (!(x)) { \
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fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
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abort(); \
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} \
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} while (0)
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// determine number of model parts based on the dimension
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static const std::unordered_map<int, int> LLAMA_N_PARTS = {
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{ 4096, 1 },
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@ -19,6 +32,52 @@ static const std::unordered_map<int, int> LLAMA_N_PARTS = {
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{ 8192, 8 },
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};
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// available llama models
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enum e_model {
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MODEL_UNKNOWN,
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MODEL_7B,
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MODEL_13B,
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MODEL_30B,
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MODEL_65B,
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};
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static const size_t MB = 1024*1024;
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// computed for n_ctx == 2048
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// TODO: dynamically determine these sizes
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// needs modifications in ggml
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
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{ MODEL_7B, 512ull*MB },
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{ MODEL_13B, 512ull*MB },
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{ MODEL_30B, 512ull*MB },
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{ MODEL_65B, 512ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
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{ MODEL_7B, 512ull*MB },
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{ MODEL_13B, 512ull*MB },
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{ MODEL_30B, 512ull*MB },
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{ MODEL_65B, 512ull*MB },
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};
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// 2*n_embd*n_ctx*n_layer*sizeof(float16)
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static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
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{ MODEL_7B, 1026ull*MB },
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{ MODEL_13B, 1608ull*MB },
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{ MODEL_30B, 3124ull*MB },
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{ MODEL_65B, 5120ull*MB },
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};
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// this is mostly needed for temporary mul_mat buffers to dequantize the data
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// not actually needed if BLAS is disabled
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static const std::map<e_model, size_t> MEM_REQ_EVAL = {
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{ MODEL_7B, 768ull*MB },
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{ MODEL_13B, 1024ull*MB },
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{ MODEL_30B, 1280ull*MB },
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{ MODEL_65B, 1536ull*MB },
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};
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// default hparams (LLaMA 7B)
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struct llama_hparams {
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int32_t n_vocab = 32000;
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@ -50,7 +109,20 @@ struct llama_layer {
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struct ggml_tensor * w3;
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};
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struct llama_kv_cache {
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struct ggml_tensor * k;
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struct ggml_tensor * v;
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struct ggml_context * ctx;
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std::vector<uint8_t> buf;
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int n; // number of tokens currently in the cache
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};
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struct llama_model {
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e_model type = MODEL_UNKNOWN;
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llama_hparams hparams;
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struct ggml_tensor * tok_embeddings;
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@ -60,12 +132,18 @@ struct llama_model {
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std::vector<llama_layer> layers;
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// key + value memory
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struct ggml_tensor * memory_k;
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struct ggml_tensor * memory_v;
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//
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// context
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struct ggml_context * ctx;
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// key + value cache for the self attention
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// TODO: move to llama_state
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struct llama_kv_cache kv_self;
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// the model memory buffer
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std::vector<uint8_t> buf;
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// tensors
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int n_loaded;
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std::unordered_map<std::string, struct ggml_tensor *> tensors;
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};
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@ -105,8 +183,88 @@ struct llama_context {
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// input embedding (1-dimensional array: [n_embd])
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std::vector<float> embedding;
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// memory buffers used to evaluate the model
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// TODO: move in llama_state
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std::vector<uint8_t> buf_compute;
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std::vector<uint8_t> buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
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int buf_last = 0;
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size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
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void use_buf(struct ggml_context * ctx, int i) {
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#if defined(LLAMA_USE_SCRATCH)
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size_t last_size = 0;
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if (i == -1) {
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last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
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} else {
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auto & buf = buf_scratch[i];
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last_size = ggml_set_scratch(ctx, { 0, buf.size(), buf.data(), });
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}
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if (buf_last >= 0) {
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buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
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}
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buf_last = i;
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#else
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(void) i;
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(void) ctx;
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#endif
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}
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size_t get_buf_max_mem(int i) const {
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#if defined(LLAMA_USE_SCRATCH)
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return buf_max_size[i];
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#else
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(void) i;
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return 0;
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#endif
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}
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};
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//
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// kv cache
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//
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static bool kv_cache_init(
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const struct llama_hparams & hparams,
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struct llama_kv_cache & cache,
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ggml_type wtype,
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int n_ctx) {
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_mem = n_layer*n_ctx;
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const int n_elements = n_embd*n_mem;
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cache.buf.resize(2*n_elements*ggml_type_size(wtype) + 2u*MB);
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struct ggml_init_params params;
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params.mem_size = cache.buf.size();
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params.mem_buffer = cache.buf.data();
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cache.ctx = ggml_init(params);
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if (!cache.ctx) {
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fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
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return false;
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}
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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return true;
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}
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static void kv_cache_free(struct llama_kv_cache & cache) {
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if (cache.ctx) {
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ggml_free(cache.ctx);
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cache.ctx = nullptr;
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}
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}
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struct llama_context_params llama_context_default_params() {
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struct llama_context_params result = {
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/*.n_ctx =*/ 512,
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@ -204,6 +362,22 @@ static bool llama_model_load(
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fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
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}
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if (hparams.n_layer == 32) {
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model.type = e_model::MODEL_7B;
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}
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if (hparams.n_layer == 40) {
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model.type = e_model::MODEL_13B;
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}
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if (hparams.n_layer == 60) {
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model.type = e_model::MODEL_30B;
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}
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if (hparams.n_layer == 80) {
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model.type = e_model::MODEL_65B;
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}
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fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd);
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@ -214,6 +388,7 @@ static bool llama_model_load(
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fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
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fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff);
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fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
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fprintf(stderr, "%s: type = %d\n", __func__, model.type);
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}
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// load vocab
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@ -307,11 +482,32 @@ static bool llama_model_load(
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fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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// print memory requirements
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{
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const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
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// this is the total memory required to run the inference
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const size_t mem_required =
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ctx_size +
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MEM_REQ_SCRATCH0.at(model.type) +
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MEM_REQ_SCRATCH1.at(model.type) +
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MEM_REQ_EVAL.at (model.type);
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// this is the memory required by one llama_state
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const size_t mem_required_state =
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scale*MEM_REQ_KV_SELF.at(model.type);
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fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
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mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
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}
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// create the ggml context
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{
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lctx.model.buf.resize(ctx_size);
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/*.mem_size =*/ lctx.model.buf.size(),
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/*.mem_buffer =*/ lctx.model.buf.data(),
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};
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model.ctx = ggml_init(params);
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@ -374,25 +570,6 @@ static bool llama_model_load(
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}
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}
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// key + value memory
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_mem = n_layer*n_ctx;
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const int n_elements = n_embd*n_mem;
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model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
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const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
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fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
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}
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const size_t file_offset = fin.tellg();
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fin.close();
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@ -416,9 +593,10 @@ static bool llama_model_load(
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// load weights
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{
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int n_tensors = 0;
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size_t total_size = 0;
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model.n_loaded = 0;
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fprintf(stderr, "%s: ", __func__);
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while (true) {
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@ -583,7 +761,10 @@ static bool llama_model_load(
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}
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//fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
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if (++n_tensors % 8 == 0) {
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model.n_loaded++;
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// progress
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if (model.n_loaded % 8 == 0) {
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fprintf(stderr, ".");
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fflush(stderr);
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}
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@ -591,7 +772,13 @@ static bool llama_model_load(
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fprintf(stderr, " done\n");
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fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
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fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
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if (model.n_loaded == 0) {
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fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
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} else if (model.n_loaded != (int) model.tensors.size()) {
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fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
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return false;
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}
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}
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fin.close();
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@ -622,6 +809,10 @@ static bool llama_eval_internal(
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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auto & kv_self = model.kv_self;
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LLAMA_ASSERT(!!kv_self.ctx);
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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@ -630,27 +821,11 @@ static bool llama_eval_internal(
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const int n_rot = hparams.n_embd/hparams.n_head;
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auto & mem_per_token = lctx.mem_per_token;
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// TODO: fix this hardcoded size
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static size_t buf_size = 2048u*1024*1024; // TMP !!!
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static void * buf = malloc(buf_size);
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if (mem_per_token > 0 && mem_per_token*N > buf_size) {
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const size_t buf_size_new = 1.3*(mem_per_token*N); // add 30% to account for ggml object overhead
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//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
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// reallocate
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buf_size = buf_size_new;
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buf = realloc(buf, buf_size);
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if (buf == nullptr) {
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fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
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return false;
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}
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}
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auto & buf_compute = lctx.buf_compute;
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_size,
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/*.mem_buffer =*/ buf,
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/*.mem_size =*/ buf_compute.size(),
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/*.mem_buffer =*/ buf_compute.data(),
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};
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struct ggml_context * ctx0 = ggml_init(params);
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@ -667,6 +842,8 @@ static bool llama_eval_internal(
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struct ggml_tensor * cur;
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lctx.use_buf(ctx0, 0);
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// norm
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{
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cur = ggml_rms_norm(ctx0, inpL);
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@ -685,8 +862,8 @@ static bool llama_eval_internal(
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// store key and value to memory
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if (N >= 1) {
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struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
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struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
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struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
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struct ggml_tensor * v = ggml_view_1d(ctx0, kv_self.v, N*n_embd, (ggml_element_size(kv_self.v)*n_embd)*(il*n_ctx + n_past));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
||||
|
|
@ -707,7 +884,7 @@ static bool llama_eval_internal(
|
|||
ggml_permute(ctx0,
|
||||
ggml_rope(ctx0,
|
||||
ggml_reshape_3d(ctx0,
|
||||
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
|
||||
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
n_past, n_rot, 1),
|
||||
0, 2, 1, 3);
|
||||
|
|
@ -733,7 +910,7 @@ static bool llama_eval_internal(
|
|||
ggml_cpy(ctx0,
|
||||
ggml_permute(ctx0,
|
||||
ggml_reshape_3d(ctx0,
|
||||
ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
|
||||
ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.v)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
1, 2, 0, 3),
|
||||
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
|
||||
|
|
@ -755,6 +932,8 @@ static bool llama_eval_internal(
|
|||
cur);
|
||||
}
|
||||
|
||||
lctx.use_buf(ctx0, 1);
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
||||
|
||||
// feed-forward network
|
||||
|
|
@ -773,7 +952,6 @@ static bool llama_eval_internal(
|
|||
model.layers[il].w3,
|
||||
cur);
|
||||
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w1,
|
||||
cur);
|
||||
|
|
@ -788,17 +966,20 @@ static bool llama_eval_internal(
|
|||
cur);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
lctx.use_buf(ctx0, 0);
|
||||
|
||||
// used at the end to optionally extract the embeddings
|
||||
struct ggml_tensor * embeddings = NULL;
|
||||
|
||||
// norm
|
||||
{
|
||||
|
||||
inpL = ggml_rms_norm(ctx0, inpL);
|
||||
|
||||
// inpL = norm*inpL
|
||||
|
|
@ -810,9 +991,9 @@ static bool llama_eval_internal(
|
|||
}
|
||||
|
||||
// lm_head
|
||||
{
|
||||
inpL = ggml_mul_mat(ctx0, model.output, inpL);
|
||||
}
|
||||
inpL = ggml_mul_mat(ctx0, model.output, inpL);
|
||||
|
||||
lctx.use_buf(ctx0, -1);
|
||||
|
||||
// logits -> probs
|
||||
//inpL = ggml_soft_max(ctx0, inpL);
|
||||
|
|
@ -854,7 +1035,13 @@ static bool llama_eval_internal(
|
|||
if (mem_per_token == 0) {
|
||||
mem_per_token = ggml_used_mem(ctx0)/N;
|
||||
}
|
||||
//fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
|
||||
|
||||
#if 0
|
||||
printf("\n%s: used_mem = %.3f MB, scratch -- %.3f MB %.3f MB\n", __func__,
|
||||
ggml_used_mem(ctx0)/1024.0/1024.0,
|
||||
lctx.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
lctx.get_buf_max_mem(1)/1024.0/1024.0);
|
||||
#endif
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
|
|
@ -1427,9 +1614,9 @@ struct llama_context * llama_init_from_file(
|
|||
ctx->rng = std::mt19937(params.seed);
|
||||
ctx->logits_all = params.logits_all;
|
||||
|
||||
ggml_type type_memory = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, type_memory,
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, memory_type,
|
||||
params.vocab_only)) {
|
||||
fprintf(stderr, "%s: failed to load model\n", __func__);
|
||||
llama_free(ctx);
|
||||
|
|
@ -1448,6 +1635,17 @@ struct llama_context * llama_init_from_file(
|
|||
|
||||
// reserve memory for context buffers
|
||||
{
|
||||
if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx)) {
|
||||
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
||||
llama_free(ctx);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
{
|
||||
const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
|
||||
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
const auto & hparams = ctx->model.hparams;
|
||||
if (params.logits_all) {
|
||||
ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
|
||||
|
|
@ -1458,12 +1656,19 @@ struct llama_context * llama_init_from_file(
|
|||
if (params.embedding){
|
||||
ctx->embedding.reserve(hparams.n_embd);
|
||||
}
|
||||
|
||||
ctx->buf_compute.resize(MEM_REQ_EVAL.at(ctx->model.type));
|
||||
|
||||
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0.at(ctx->model.type));
|
||||
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1.at(ctx->model.type));
|
||||
}
|
||||
|
||||
return ctx;
|
||||
}
|
||||
|
||||
void llama_free(struct llama_context * ctx) {
|
||||
kv_cache_free(ctx->model.kv_self);
|
||||
|
||||
if (ctx->model.ctx) {
|
||||
ggml_free(ctx->model.ctx);
|
||||
}
|
||||
|
|
@ -1619,4 +1824,3 @@ const char * llama_print_system_info(void) {
|
|||
|
||||
return s.c_str();
|
||||
}
|
||||
|
||||
|
|
|
|||
23
main.cpp
23
main.cpp
|
|
@ -217,11 +217,23 @@ int main(int argc, char ** argv) {
|
|||
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
|
||||
}
|
||||
|
||||
// determine the required inference memory per token:
|
||||
// TODO: better way to do that
|
||||
{
|
||||
const std::vector<llama_token> tmp = { 0, 1, 2, 3 };
|
||||
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
|
||||
// determine the maximum memory usage needed to do inference for the given n_batch and n_predict 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, 0);
|
||||
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
|
||||
}
|
||||
|
||||
{
|
||||
const std::vector<llama_token> tmp = { 0, };
|
||||
llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads);
|
||||
}
|
||||
|
||||
llama_print_timings(ctx);
|
||||
llama_free(ctx);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
if (params.perplexity) {
|
||||
|
|
@ -508,7 +520,6 @@ int main(int argc, char ** argv) {
|
|||
#endif
|
||||
|
||||
llama_print_timings(ctx);
|
||||
|
||||
llama_free(ctx);
|
||||
|
||||
set_console_state(CONSOLE_STATE_DEFAULT);
|
||||
|
|
|
|||
10
utils.cpp
10
utils.cpp
|
|
@ -79,8 +79,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.n_ctx = std::stoi(argv[i]);
|
||||
} else if (arg == "--memory_f16") {
|
||||
params.memory_f16 = true;
|
||||
} else if (arg == "--memory_f32") {
|
||||
params.memory_f16 = false;
|
||||
} else if (arg == "--top_p") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -111,6 +111,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.n_batch = std::stoi(argv[i]);
|
||||
params.n_batch = std::min(512, params.n_batch);
|
||||
} else if (arg == "-m" || arg == "--model") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -131,6 +132,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
params.use_color = true;
|
||||
} else if (arg == "--mlock") {
|
||||
params.use_mlock = true;
|
||||
} else if (arg == "--mtest") {
|
||||
params.mem_test = true;
|
||||
} else if (arg == "-r" || arg == "--reverse-prompt") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -193,7 +196,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
|
||||
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\n");
|
||||
fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n");
|
||||
fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
|
||||
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
|
||||
fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
|
||||
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
||||
|
|
@ -201,6 +204,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
if (ggml_mlock_supported()) {
|
||||
fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
||||
}
|
||||
fprintf(stderr, " --mtest compute maximum memory usage\n");
|
||||
fprintf(stderr, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
|
||||
fprintf(stderr, "\n");
|
||||
|
|
|
|||
16
utils.h
16
utils.h
|
|
@ -14,12 +14,13 @@
|
|||
//
|
||||
|
||||
struct gpt_params {
|
||||
int32_t seed = -1; // RNG seed
|
||||
int32_t seed = -1; // RNG seed
|
||||
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
||||
int32_t n_predict = 128; // new tokens to predict
|
||||
int32_t repeat_last_n = 64; // last n tokens to penalize
|
||||
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
|
||||
int32_t n_ctx = 512; //context size
|
||||
int32_t n_predict = 128; // new tokens to predict
|
||||
int32_t repeat_last_n = 64; // last n tokens to penalize
|
||||
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
|
||||
int32_t n_ctx = 512; // context size
|
||||
int32_t n_batch = 8; // batch size for prompt processing
|
||||
|
||||
// sampling parameters
|
||||
int32_t top_k = 40;
|
||||
|
|
@ -27,15 +28,13 @@ struct gpt_params {
|
|||
float temp = 0.80f;
|
||||
float repeat_penalty = 1.10f;
|
||||
|
||||
int32_t n_batch = 8; // batch size for prompt processing
|
||||
|
||||
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
|
||||
std::string prompt = "";
|
||||
|
||||
|
||||
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
|
||||
|
||||
bool memory_f16 = false; // use f16 instead of f32 for memory kv
|
||||
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
|
||||
bool interactive = false; // interactive mode
|
||||
|
|
@ -47,6 +46,7 @@ struct gpt_params {
|
|||
bool ignore_eos = false; // do not stop generating after eos
|
||||
bool perplexity = false; // compute perplexity over the prompt
|
||||
bool use_mlock = false; // use mlock to keep model in memory
|
||||
bool mem_test = false; // compute maximum memory usage
|
||||
};
|
||||
|
||||
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
|
||||
|
|
|
|||
Loading…
Reference in a new issue