+++ /dev/null
-#include "ggml/ggml.h"
-
-#include "common.h"
-#include "common-ggml.h"
-
-#include <cassert>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-
-#if !defined(_WIN32)
-// mmap
-#include <sys/types.h>
-#include <sys/mman.h>
-#include <unistd.h>
-#include <fcntl.h>
-#else
-#define NOMINMAX
-#include <Windows.h>
-#endif
-
-#ifdef GGML_USE_CUBLAS
-#include "ggml-cuda.h"
-#endif
-
-#ifdef GGML_USE_CLBLAST
-#include "ggml-opencl.h"
-#endif
-
-// default hparams (GPT-2 117M)
-// https://huggingface.co/bigcode/gpt_bigcode-santacoder/blob/main/config.json
-struct starcoder_hparams {
- int32_t n_vocab = 49280;
- int32_t n_ctx = 2048;
- int32_t n_embd = 2048;
- int32_t n_head = 16;
- int32_t n_layer = 24;
- int32_t ftype = 1;
- float eps = 1e-5f;
-};
-
-struct starcoder_layer {
- // normalization
- struct ggml_tensor * ln_1_g;
- struct ggml_tensor * ln_1_b;
-
- struct ggml_tensor * ln_2_g;
- struct ggml_tensor * ln_2_b;
-
- // attention
- struct ggml_tensor * c_attn_attn_w;
- struct ggml_tensor * c_attn_attn_b;
-
- struct ggml_tensor * c_attn_proj_w;
- struct ggml_tensor * c_attn_proj_b;
-
- // mlp
- struct ggml_tensor * c_mlp_fc_w;
- struct ggml_tensor * c_mlp_fc_b;
-
- struct ggml_tensor * c_mlp_proj_w;
- struct ggml_tensor * c_mlp_proj_b;
-};
-
-struct llama_buffer {
- uint8_t * addr = NULL;
- size_t size = 0;
-
- llama_buffer() = default;
-
- void resize(size_t len) {
-#ifdef GGML_USE_METAL
- free(addr);
- int result = posix_memalign((void **) &addr, sysconf(_SC_PAGESIZE), len);
- if (result == 0) {
- memset(addr, 0, len);
- }
- else {
- addr = NULL;
- }
-#else
- delete[] addr;
- addr = new uint8_t[len];
-#endif
- size = len;
- }
-
- ~llama_buffer() {
-#ifdef GGML_USE_METAL
- free(addr);
-#else
- delete[] addr;
-#endif
- addr = NULL;
- }
-
- // disable copy and move
- llama_buffer(const llama_buffer&) = delete;
- llama_buffer(llama_buffer&&) = delete;
- llama_buffer& operator=(const llama_buffer&) = delete;
- llama_buffer& operator=(llama_buffer&&) = delete;
-};
-
-
-struct kv_cache {
- struct ggml_tensor * k;
- struct ggml_tensor * v;
-
- struct ggml_context * ctx = NULL;
-
- //std::vector<uint8_t> buf;
- llama_buffer buf;
-
- int n;
-};
-
-struct starcoder_model {
- starcoder_hparams hparams;
-
- // normalization
- struct ggml_tensor * ln_f_g;
- struct ggml_tensor * ln_f_b;
-
- struct ggml_tensor * wte; // position embedding
- struct ggml_tensor * wpe; // token embedding
- struct ggml_tensor * lm_head; // language model head
-
- std::vector<starcoder_layer> layers;
-
- // key + value memory
- //struct ggml_tensor * memory_k;
- //struct ggml_tensor * memory_v;
- struct kv_cache cache;
-
- // model memory mapped file
- void * mm_addr = NULL;
- uint64_t mm_length = 0;
-
- //
- struct ggml_context * ctx;
- std::map<std::string, struct ggml_tensor *> tensors;
-};
-
-// From PR #613 (https://github.com/ggerganov/llama.cpp/pull/613)
-static void *mmap_file(const char *fname, uint64_t *mm_length) {
-#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
- HANDLE hFile = CreateFileA(fname,
- GENERIC_READ,
- FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE,
- NULL,
- OPEN_EXISTING,
- FILE_ATTRIBUTE_NORMAL | FILE_ATTRIBUTE_NOT_CONTENT_INDEXED,
- NULL);
- if (hFile == INVALID_HANDLE_VALUE) return 0;
- LARGE_INTEGER fileSize;
- fileSize.QuadPart = -1;
- GetFileSizeEx(hFile, &fileSize);
- int64_t length = fileSize.QuadPart;
- HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
- CloseHandle(hFile);
- if (!hMapping) return 0;
- void *addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
- CloseHandle(hMapping);
- if (!addr) return 0;
-#else
- int fd = open(fname, O_RDONLY);
- if (fd == -1) return 0;
- int64_t length = lseek(fd, 0, SEEK_END);
- void *addr = mmap(NULL, length, PROT_READ, MAP_SHARED, fd, 0);
- close(fd);
- if (addr == MAP_FAILED) return 0;
-#endif
- *mm_length = length;
- return addr;
-}
-
-static void munmap_file(void * addr, size_t length) {
-#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
- UnmapViewOfFile(addr);
-#else
- munmap(addr, length);
-#endif
-}
-
-// load the model's weights from a file
-bool starcoder_model_load(const std::string & fname, starcoder_model & model, gpt_vocab & vocab, int32_t n_gpu_layers) {
- printf("%s: loading model from '%s'\n", __func__, fname.c_str());
-
- auto fin = std::ifstream(fname, std::ios::binary);
- if (!fin) {
- fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
- return false;
- }
-
- std::vector<char> f_buf(1024*1024);
- fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
-
-
- // verify magic
- {
- uint32_t magic;
- fin.read((char *) &magic, sizeof(magic));
- //if (magic != 0x67676a74) {
- if (magic != 0x67676d6c) {
- fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
- return false;
- }
- }
-
- // load hparams
- {
- auto & hparams = model.hparams;
-
- fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
- fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
- fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
- fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
- fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
- fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
-
- const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
-
- printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
- printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
- printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
- printf("%s: n_head = %d\n", __func__, hparams.n_head);
- printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
- printf("%s: ftype = %d\n", __func__, hparams.ftype);
- printf("%s: qntvr = %d\n", __func__, qntvr);
-
- hparams.ftype %= GGML_QNT_VERSION_FACTOR;
- }
-
- // load vocab
- {
- int32_t n_vocab = 0;
- fin.read((char *) &n_vocab, sizeof(n_vocab));
-
- if (n_vocab != model.hparams.n_vocab) {
- fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
- __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
- return false;
- }
-
- std::string word;
- std::vector<char> buf(128);
-
- for (int i = 0; i < n_vocab; i++) {
- uint32_t len;
- fin.read((char *) &len, sizeof(len));
-
- buf.resize(len);
- fin.read((char *) buf.data(), len);
- word.assign(buf.data(), len);
-
- vocab.token_to_id[word] = i;
- vocab.id_to_token[i] = word;
-
- // if (i < 10) fprintf(stderr, "%.s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
- }
-
- // Add StarChat special tokens.
- for (std::string token : {
- "<|system|>",
- "<|user|>",
- "<|assistant|>",
- "<|end|>",
- }) {
- if (vocab.token_to_id.find(token) != vocab.token_to_id.end()) {
- vocab.add_special_token(token);
- }
- }
- }
-
- char *mm_addr = NULL;
- model.mm_addr = mmap_file(fname.c_str(), &model.mm_length);
- if (model.mm_addr == NULL) {
- fprintf(stderr, "%s: failed to mmap '%s'\n", __func__, fname.c_str());
- return false;
- }
- mm_addr = (char *)model.mm_addr;
- fprintf(stderr, "%s: ggml map size = %6.2f MB\n", __func__, model.mm_length/(1024.0*1024.0));
-
- // for the big tensors, we have the option to store the data in 16-bit floats or quantized
- // in order to save memory and also to speed up the computation
- ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
- if (wtype == GGML_TYPE_COUNT) {
- fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
- __func__, fname.c_str(), model.hparams.ftype);
- return false;
- }
-
- auto & ctx = model.ctx;
-
- size_t ctx_size = 0;
-
- {
- const auto & hparams = model.hparams;
-
- const int n_layer = hparams.n_layer;
-
-
- /*
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
- const int n_vocab = hparams.n_vocab;
-
- const int head_dim = n_embd / hparams.n_head;
- const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head
- const int kv_dim = kv_heads * head_dim;
-
-
- ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
- ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
-
- ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // wte
- ctx_size += n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
- ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // lm_head
-
- ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
- ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
-
- ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
- ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b
-
- ctx_size += n_layer*((n_embd + 2*kv_dim)*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w // TODO:
- ctx_size += n_layer*( (n_embd + 2*kv_dim)*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b
-
- ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
- ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b
-
- ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
- ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
-
- ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
- ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
-
- ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
- ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
- */
-
- ctx_size += (6 + 12*n_layer)*512; // object overhead
-
- printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
- }
-
- // create the ggml context
- {
- struct ggml_init_params params = {
- /*.mem_size =*/ ctx_size,
- /*.mem_buffer =*/ NULL,
- /*.no_alloc =*/ true,
- };
-
- model.ctx = ggml_init(params);
- if (!model.ctx) {
- fprintf(stderr, "%s: ggml_init() failed\n", __func__);
- return false;
- }
- }
-
- // prepare memory for the weights
- {
- const auto & hparams = model.hparams;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
- const int n_vocab = hparams.n_vocab;
-
- const int head_dim = n_embd / hparams.n_head;
- const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head
- const int kv_dim = kv_heads * head_dim;
-
- model.layers.resize(n_layer);
-
- model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
- model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
- model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
- model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
-
- // map by name
- model.tensors["model/ln_f/g"] = model.ln_f_g;
- model.tensors["model/ln_f/b"] = model.ln_f_b;
-
- model.tensors["model/wte"] = model.wte;
- model.tensors["model/wpe"] = model.wpe;
- model.tensors["model/lm_head"] = model.lm_head;
-
- for (int i = 0; i < n_layer; ++i) {
- auto & layer = model.layers[i];
-
- layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
- layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
- layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd + 2*kv_dim);
- layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd + 2*kv_dim);
-
- layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); //TODO: 4*n_embd = config.n_inner
- layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
-
- layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
- layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- // map by name
- model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
- model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
-
- model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
- model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
-
- model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
- model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
-
- model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
- model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
-
- model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
- model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
-
- model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;
- model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
- }
- }
-
- // key + value memory
- {
- const auto & hparams = model.hparams;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
-
- const int n_mem = n_layer*n_ctx;
- const int n_elements = n_embd*n_mem;
-
- model.cache.buf.resize(2u*n_elements*ggml_type_size(GGML_TYPE_F16) + 2u*1024*1024);
-
- struct ggml_init_params c_params;
- c_params.mem_size = model.cache.buf.size;
- c_params.mem_buffer = model.cache.buf.addr;
- c_params.no_alloc = false;
-
- model.cache.ctx = ggml_init(c_params);
-
- if (!model.cache.ctx) {
- fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
- return false;
- }
-
- model.cache.k = ggml_new_tensor_1d(model.cache.ctx, GGML_TYPE_F16, n_elements);
- model.cache.v = ggml_new_tensor_1d(model.cache.ctx, GGML_TYPE_F16, n_elements);
-
- const size_t memory_size = ggml_nbytes(model.cache.k) + ggml_nbytes(model.cache.v);
-
- printf("%s: kv_cache memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
- }
-
- // load weights
- {
- size_t total_size = 0;
-
- bool has_lm_head = false;
-
- while (true) {
- int32_t n_dims;
- int32_t length;
- int32_t ttype;
-
- fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
- fin.read(reinterpret_cast<char *>(&length), sizeof(length));
- fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
-
- if (fin.eof()) {
- break;
- }
-
- int32_t nelements = 1;
- int32_t ne[2] = { 1, 1 };
- for (int i = 0; i < n_dims; ++i) {
- fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
- nelements *= ne[i];
- }
-
- std::string name(length, 0);
- fin.read(&name[0], length);
-
- if (model.tensors.find(name.data()) == model.tensors.end()) {
- fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
- return false;
- }
-
- auto tensor = model.tensors[name.data()];
-
- if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
- fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
- __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
- return false;
- }
- if (ggml_nelements(tensor) != nelements) {
- fprintf(stderr, "%s: tensor '%s' has wrong size in model file. got %d, expected %d\n",
- __func__, name.data(), (int) ggml_nelements(tensor), nelements);
- return false;
- }
-
- // for debugging
- if (0) {
- printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
- }
-
- const size_t bpe = ggml_type_size(ggml_type(ttype));
-
- if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
- fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
- __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
- return false;
- }
-
- // mmap
- size_t offset = fin.tellg();
- size_t tensor_data_size = ggml_nbytes(tensor);
- //offset = (offset + 31) & -32;
- tensor->data = mm_addr + offset;
- fin.seekg(offset + tensor_data_size);
- total_size += tensor_data_size;
-
- // GPT-2 models share the WTE tensor as the LM head
- if (name == "model/wte" && has_lm_head == false) {
- // Dont know if this is required, test models have an lm_head
- model.lm_head->data = tensor->data;
- }
-
- if (name == "model/lm_head") {
- has_lm_head = true;
- }
- }
-
- printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
- }
-
- fin.close();
-
-#ifdef GGML_USE_CUBLAS
- {
- const auto & hparams = model.hparams;
- const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
-
- fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu);
-
- size_t vram_total = 0;
-
- for (int i = 0; i < n_gpu; ++i) {
- const auto & layer = model.layers[i];
-
- layer.c_attn_attn_w->backend = GGML_BACKEND_GPU;
- ggml_cuda_transform_tensor((uint8_t *)layer.c_attn_attn_w->data, layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
-
- layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
- ggml_cuda_transform_tensor((uint8_t *)layer.c_attn_proj_w->data, layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
-
- layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
- ggml_cuda_transform_tensor((uint8_t *)layer.c_mlp_fc_w->data, layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
-
- layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
- ggml_cuda_transform_tensor((uint8_t *)layer.c_mlp_proj_w->data, layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
- }
-
- ggml_cuda_set_scratch_size(0); // disable scratch
-
- //if (n_gpu_layers > (int) hparams.n_layer) {
- // fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
- // ggml_cuda_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
- //}
-
- fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
- }
-#elif defined(GGML_USE_CLBLAST)
- //From koboldcpp
- {
- const auto & hparams = model.hparams;
- size_t vram_total = 0;
- const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
- fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
- for (int i = 0; i < n_gpu; ++i) {
- const auto & layer = model.layers[i];
- layer.c_attn_attn_w->backend = GGML_BACKEND_GPU;
- layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
- layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
- layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
- ggml_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
- ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
- ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
- ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
- }
- fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
- }
- #endif
-
- return true;
-}
-
-// evaluate the transformer
-//
-// - model: the model
-// - n_threads: number of threads to use
-// - n_past: the context size so far
-// - embd_inp: the embeddings of the tokens in the context
-// - embd_w: the predicted logits for the next token
-//
-bool starcoder_eval(
- const starcoder_model & model,
- const int n_threads,
- const int n_past,
- const std::vector<gpt_vocab::id> & embd_inp,
- std::vector<float> & embd_w,
- size_t & mem_per_token) {
-
- const int N = int(embd_inp.size());
-
- const auto & hparams = model.hparams;
-
- auto & cache = model.cache;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
- const int n_head = hparams.n_head;
- const int n_vocab = hparams.n_vocab;
-
- // Scratch is too small for large n_batch (256)
- //static size_t buf_size = 256u*1024*1024;
- static size_t buf_size = 256u*1024*1024*2;
- static void * buf = malloc(buf_size);
-
- // use 2 scratch buffers
- // TODO: very hacky solution - reimplement in a more elegant way
- static size_t scratch0_size = 256u*1024*1024*2;
- static void * scratch0 = malloc(scratch0_size);
-
- static size_t scratch1_size = 256u*1024*1024*2;
- static void * scratch1 = malloc(scratch1_size);
-
- if (mem_per_token > 0 && mem_per_token*N > buf_size) {
- const size_t buf_size_new = size_t(1.1*(mem_per_token*N)); // add 10% to account for ggml object overhead
- printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
- // reallocate
- buf_size = buf_size_new;
- buf = realloc(buf, buf_size);
- if (buf == nullptr) {
- fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
- return false;
- }
- }
-
- struct ggml_init_params params = {
- /*.mem_size =*/ buf_size,
- /*.mem_buffer =*/ buf,
- /*.no_alloc =*/ false,
- };
-
- struct ggml_context * ctx0 = ggml_init(params);
- struct ggml_cgraph * gf = ggml_new_graph(ctx0);
-
- struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-
-
- memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
-
- struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
- for (int i = 0; i < N; ++i) {
- ((int32_t *) position->data)[i] = n_past + i;
- }
-
- // wte + wpe
- struct ggml_tensor * inpL =
- ggml_add(ctx0,
- ggml_get_rows(ctx0, model.wte, embd),
- ggml_get_rows(ctx0, model.wpe, position));
-
- for (int il = 0; il < n_layer; ++il) {
- struct ggml_tensor * cur;
-
- ggml_set_scratch(ctx0, { 0, scratch0_size, scratch0, });
-
- // norm
- {
- // [ 768, N]
- cur = ggml_norm(ctx0, inpL, hparams.eps);
-
- // cur = ln_1_g*cur + ln_1_b
- // [ 768, N]
- cur = ggml_add(ctx0,
- ggml_mul(ctx0,
- ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
- cur),
- ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
- }
-
- // attn
- // [2304, 768] - model.layers[il].c_attn_attn_w
- // [2304, 1] - model.layers[il].c_attn_attn_b
- // [ 768, N] - cur (in)
- // [2304, N] - cur (out)
- //
- // cur = attn_w*cur + attn_b
- // [2304, N]
- {
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_attn_attn_w,
- cur);
-
- cur = ggml_add(ctx0,
- ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
- cur);
- }
-
- // self-attention
- {
- struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
- struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
- struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
-
- // store key and value to memory
- if (N >= 1) {
- struct ggml_tensor * k = ggml_view_1d(ctx0, cache.k, N*n_embd, (ggml_element_size(cache.k)*n_embd)*(il*n_ctx + n_past));
- struct ggml_tensor * v = ggml_view_1d(ctx0, cache.v, N*n_embd, (ggml_element_size(cache.v)*n_embd)*(il*n_ctx + n_past));
-
- ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
- ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
- }
-
- // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
- // [64, N, 12]
- struct ggml_tensor * Q =
- ggml_permute(ctx0,
- ggml_cpy(ctx0,
- Qcur,
- ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
- 0, 2, 1, 3);
-
- // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
- // [64, n_past + N, 12]
- struct ggml_tensor * K =
- ggml_permute(ctx0,
- ggml_reshape_3d(ctx0,
- ggml_view_1d(ctx0, cache.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(cache.k)*n_embd),
- n_embd/n_head, n_head, n_past + N),
- 0, 2, 1, 3); //TODO: need to be tiled
-
- // GG: flash attention
- //struct ggml_tensor * V =
- // 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),
- // 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));
-
- //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
-
- // K * Q
- // [n_past + N, N, 12]
- struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); //TODO: check if it broadcasts
-
- // KQ_scaled = KQ / sqrt(n_embd/n_head)
- // [n_past + N, N, 12]
- struct ggml_tensor * KQ_scaled =
- ggml_scale_inplace(ctx0,
- KQ,
- 1.0f/sqrt(float(n_embd)/n_head));
-
- // KQ_masked = mask_past(KQ_scaled)
- // [n_past + N, N, 12]
- struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
-
- // KQ = soft_max(KQ_masked)
- // [n_past + N, N, 12]
- struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-
- // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
- // [n_past + N, 64, 12]
- struct ggml_tensor * V_trans =
- ggml_cpy(ctx0,
- ggml_permute(ctx0,
- ggml_reshape_3d(ctx0,
- ggml_view_1d(ctx0, cache.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(cache.v)*n_embd),
- n_embd/n_head, n_head, n_past + N),
- 1, 2, 0, 3),
- ggml_new_tensor_3d(ctx0, cache.v->type, n_past + N, n_embd/n_head, n_head));
-
- // KQV = transpose(V) * KQ_soft_max
- // [64, N, 12]
- struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
-
- // KQV_merged = KQV.permute(0, 2, 1, 3)
- // [64, 12, N]
- struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-
- // cur = KQV_merged.contiguous().view(n_embd, N)
- // [768, N]
- cur = ggml_cpy(ctx0,
- KQV_merged,
- ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
- }
-
- // projection
- // [ 768, 768] - model.layers[il].c_attn_proj_w
- // [ 768, 1] - model.layers[il].c_attn_proj_b
- // [ 768, N] - cur (in)
- // [ 768, N] - cur (out)
- //
- // cur = proj_w*cur + proj_b
- // [768, N]
- {
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_attn_proj_w,
- cur);
-
- cur = ggml_add(ctx0,
- ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
- cur);
- }
-
- // add the input
- cur = ggml_add(ctx0, cur, inpL);
-
- struct ggml_tensor * inpFF = cur;
-
- ggml_set_scratch(ctx0, { 0, scratch1_size, scratch1, });
-
- // feed-forward network
- {
- // norm
- {
- cur = ggml_norm(ctx0, inpFF, hparams.eps);
-
- // cur = ln_2_g*cur + ln_2_b
- // [ 768, N]
- cur = ggml_add(ctx0,
- ggml_mul(ctx0,
- ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
- cur),
- ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
- }
-
- // fully connected
- // [3072, 768] - model.layers[il].c_mlp_fc_w
- // [3072, 1] - model.layers[il].c_mlp_fc_b
- // [ 768, N] - cur (in)
- // [3072, N] - cur (out)
- //
- // cur = fc_w*cur + fc_b
- // [3072, N]
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_mlp_fc_w,
- cur);
-
- cur = ggml_add(ctx0,
- ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
- cur);
-
- // GELU activation
- // [3072, N]
- cur = ggml_gelu(ctx0, cur);
-
- // projection
- // [ 768, 3072] - model.layers[il].c_mlp_proj_w
- // [ 768, 1] - model.layers[il].c_mlp_proj_b
- // [3072, N] - cur (in)
- // [ 768, N] - cur (out)
- //
- // cur = proj_w*cur + proj_b
- // [768, N]
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_mlp_proj_w,
- cur);
-
- cur = ggml_add(ctx0,
- ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
- cur);
- }
-
- // input for next layer
- inpL = ggml_add(ctx0, cur, inpFF);
- }
-
- ggml_set_scratch(ctx0, { 0, scratch0_size, scratch0, });
-
- // norm
- {
- // [ 768, N]
- inpL = ggml_norm(ctx0, inpL, hparams.eps);
-
- // inpL = ln_f_g*inpL + ln_f_b
- // [ 768, N]
- inpL = ggml_add(ctx0,
- ggml_mul(ctx0,
- ggml_repeat(ctx0, model.ln_f_g, inpL),
- inpL),
- ggml_repeat(ctx0, model.ln_f_b, inpL));
- }
-
- ggml_set_scratch(ctx0, { 0, 0, nullptr, });
-
- // inpL = WTE * inpL
- // [ 768, 50257] - model.lm_head
- // [ 768, N] - inpL
- inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
-
- // logits -> probs
- //inpL = ggml_soft_max_inplace(ctx0, inpL);
-
- // run the computation
- ggml_build_forward_expand(gf, inpL);
- ggml_graph_compute_with_ctx(ctx0, gf, n_threads);
-
- //if (n_past%100 == 0) {
- // ggml_graph_print (&gf);
- // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
- //}
-
- //embd_w.resize(n_vocab*N);
- //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
-
- // return result just for the last token
- embd_w.resize(n_vocab);
- memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
- if (mem_per_token == 0) {
- mem_per_token = ggml_used_mem(ctx0)/N;
- }
- //printf("used_mem = %zu MB\n", ggml_used_mem(ctx0)/(1024*1024));
-
- ggml_free(ctx0);
-
- return true;
-}
-
-
-int main(int argc, char ** argv) {
- ggml_time_init();
-
- const int64_t t_main_start_us = ggml_time_us();
-
- gpt_params params;
- params.model = "models/gpt-2-117M/ggml-model.bin";
-
- if (gpt_params_parse(argc, argv, params) == false) {
- return 1;
- }
-
- if (params.seed < 0) {
- params.seed = int(time(NULL));
- }
-
- printf("%s: seed = %d\n", __func__, params.seed);
-
- std::mt19937 rng(params.seed);
- if (params.prompt.empty()) {
- params.prompt = gpt_random_prompt(rng);
- }
-
- int64_t t_load_us = 0;
-
- gpt_vocab vocab;
- starcoder_model model;
-
- // load the model
- {
- const int64_t t_start_us = ggml_time_us();
-
- if (!starcoder_model_load(params.model, model, vocab, params.n_gpu_layers)) {
- fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
- return 1;
- }
-
- t_load_us = ggml_time_us() - t_start_us;
-
- test_gpt_tokenizer(vocab, params.token_test);
- }
-
- int n_past = 0;
-
- int64_t t_sample_us = 0;
- int64_t t_predict_us = 0;
-
- std::vector<float> logits;
-
- // tokenize the prompt
- std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
-
- params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
-
- printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
- printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
- for (size_t i = 0; i < embd_inp.size(); i++) {
- printf("%s: token[%zu] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
- }
- printf("\n\n");
-
- // Handle StarChat "<|end|>" token.
- gpt_vocab::id starchat_end_token = -1;
- {
- const auto it = vocab.token_to_id.find("<|end|>");
- if (it != vocab.token_to_id.end()) {
- starchat_end_token = it->second;
- }
- }
-
- // submit the input prompt token-by-token
- // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning
- std::vector<gpt_vocab::id> embd;
-
- // determine the required inference memory per token:
- size_t mem_per_token = 0;
- printf("Calling starcoder_eval\n");
- starcoder_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
-
- for (size_t i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
- // predict
- if (embd.size() > 0) {
- const int64_t t_start_us = ggml_time_us();
-
- if (!starcoder_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
- printf("Failed to predict\n");
- return 1;
- }
-
- // Should input processing count towards t_predict?
- if (i > embd_inp.size()) {
- t_predict_us += ggml_time_us() - t_start_us;
- }
- }
-
- n_past += int(embd.size());
- embd.clear();
-
- if (i >= embd_inp.size()) {
- // sample next token
- const int top_k = params.top_k;
- const float top_p = params.top_p;
- const float temp = params.temp;
-
- const int n_vocab = model.hparams.n_vocab;
-
- gpt_vocab::id id = 0;
-
- {
- const int64_t t_start_sample_us = ggml_time_us();
-
- id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
-
- t_sample_us += ggml_time_us() - t_start_sample_us;
- }
-
- // add it to the context
- embd.push_back(id);
- } else {
- // if here, it means we are still processing the input prompt
- for (size_t k = i; k < embd_inp.size(); k++) {
- embd.push_back(embd_inp[k]);
- if (int32_t(embd.size()) >= params.n_batch) {
- break;
- }
- }
- i += int(embd.size()) - 1;
- }
-
- // display text
- for (auto id : embd) {
- printf("%s", vocab.id_to_token[id].c_str());
- }
- fflush(stdout);
-
- // check if model is santacoder
- if (model.hparams.n_layer <= 30 && embd.back() == 49152) {
- break;
- }
- // check if model is starcoder
- else if (embd.back() == 0) { //TODO: this is only for starcoder
- break;
- }
- // Handle StarChat "<|end|>" token.
- else if (embd.back() == starchat_end_token) {
- //break;
- }
- }
-
- // report timing
- {
- const int64_t t_main_end_us = ggml_time_us();
-
- printf("\n\n");
- printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
- printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
- printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
- //Shouldnt the input prompt be subracted?
- printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/(n_past - embd_inp.size()));
- //printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
-
- printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
- }
-
- ggml_free(model.ctx);
-
- if (model.mm_addr) {
- munmap_file(model.mm_addr, model.mm_length);
- }
-
- return 0;
-}
overview / pkg / ggml / sources / ggml / commitdiff