--- /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>
+
+// mmap
+#include <sys/types.h>
+#include <sys/mman.h>
+#include <unistd.h>
+#include <fcntl.h>
+
+#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;
+};
+
+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, getpagesize(), 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());
+
+ fin.seekg(0, fin.end);
+ const size_t file_size = fin.tellg();
+ fin.seekg(0);
+
+ // 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 (const 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_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));
+ printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(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 = 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 scr0_size = 256u*1024*1024*2;
+ static void * scr0 = malloc(scr0_size);
+
+ static size_t scr1_size = 256u*1024*1024*2;
+ static void * scr1 = malloc(scr1_size);
+
+ if (mem_per_token > 0 && mem_per_token*N > buf_size) {
+ const size_t buf_size_new = 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 = {};
+
+ 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, scr0_size, scr0, });
+
+ // norm
+ {
+ // [ 768, N]
+ cur = ggml_norm(ctx0, inpL);
+
+ // 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,
+ ggml_new_f32(ctx0, 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, scr1_size, scr1, });
+
+ // feed-forward network
+ {
+ // norm
+ {
+ cur = ggml_norm(ctx0, inpFF);
+
+ // 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, scr0_size, scr0, });
+
+ // norm
+ {
+ // [ 768, N]
+ inpL = ggml_norm(ctx0, inpL);
+
+ // 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 = 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 (int i = 0; i < embd_inp.size(); i++) {
+ printf("%s: token[%d] = %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 (int 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 += 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 (int k = i; k < embd_inp.size(); k++) {
+ embd.push_back(embd_inp[k]);
+ if (embd.size() >= params.n_batch) {
+ break;
+ }
+ }
+ i += 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