--- /dev/null
+#include "ggml/ggml.h"
+#include "ggml/ggml-alloc.h"
+#include "ggml/ggml-backend.h"
+
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#include "ggml-metal.h"
+#endif
+
+#include "common.h"
+#include "common-ggml.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <set>
+#include <string>
+#include <vector>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+ (void) level;
+ (void) user_data;
+ fputs(text, stderr);
+ fflush(stderr);
+}
+
+typedef int32_t gpt2_pos;
+typedef int32_t gpt2_seq_id;
+
+// default hparams (GPT-2 117M)
+struct gpt2_hparams {
+ int32_t n_vocab = 50257;
+ int32_t n_ctx = 1024;
+ int32_t n_embd = 768;
+ int32_t n_head = 12;
+ int32_t n_layer = 12;
+ int32_t ftype = 1;
+ float eps = 1e-5f;
+};
+
+struct gpt2_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 gpt2_kv_cell {
+ gpt2_pos pos = -1;
+ gpt2_pos delta = 0;
+
+ std::set<gpt2_seq_id> seq_id;
+
+ bool has_seq_id(const gpt2_seq_id & id) const {
+ return seq_id.find(id) != seq_id.end();
+ }
+};
+
+struct gpt2_kv_cache {
+ // key + value memory
+ struct ggml_tensor * k;
+ struct ggml_tensor * v;
+ //
+
+ uint32_t head = 0;
+ uint32_t size = 0;
+
+ // computed before each graph build
+ uint32_t n = 0;
+
+ std::vector<gpt2_kv_cell> cells;
+
+ ggml_backend_buffer_t buffer;
+};
+
+struct gpt2_model {
+ gpt2_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<gpt2_layer> layers;
+
+ gpt2_kv_cache kv_cache;
+
+ struct ggml_context * ctx;
+
+ ggml_backend_t backend = NULL;
+
+ ggml_backend_buffer_t buffer_w;
+
+ std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// Input data for gpt2_decode
+// A gpt2_batch object can contain input about one or many sequences
+// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
+//
+// - token : the token ids of the input (used when embd is NULL)
+// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
+// - pos : the positions of the respective token in the sequence
+// - seq_id : the sequence to which the respective token belongs
+// - logits : if zero, the logits for the respective token will not be output
+//
+struct gpt2_batch {
+ int32_t n_tokens = -1;
+
+ gpt_vocab::id * token = {};
+ float * embd = {};
+ gpt2_pos * pos = {};
+ gpt2_seq_id * seq_id = {};
+ int8_t * logits = {};
+};
+
+// load the model's weights from a file
+bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab, int 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;
+ }
+
+ // verify magic
+ {
+ uint32_t magic;
+ fin.read((char *) &magic, sizeof(magic));
+ if (magic != GGML_FILE_MAGIC) {
+ 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;
+ }
+ }
+
+ // 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 buffer_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;
+
+ buffer_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
+ buffer_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
+
+ buffer_size += n_vocab*n_embd*ggml_type_sizef(wtype); // wte
+ buffer_size += n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
+ buffer_size += n_vocab*n_embd*ggml_type_sizef(wtype); // lm_head
+
+ buffer_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
+ buffer_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
+
+ buffer_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
+ buffer_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b
+
+ buffer_size += n_layer*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w
+ buffer_size += n_layer*( 3*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b
+
+ buffer_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
+ buffer_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b
+
+ buffer_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
+ buffer_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
+
+ buffer_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
+ buffer_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
+
+ buffer_size += (6 + 12*n_layer)*128; // alignment overhead
+
+ printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
+ printf("%s: backend buffer size = %6.2f MB\n", __func__, buffer_size/(1024.0*1024.0));
+ }
+
+ // create the ggml context
+ {
+ size_t n_tensors = 2 + 6 + 12*model.hparams.n_layer;
+ struct ggml_init_params params = {
+ /*.mem_size =*/ ggml_tensor_overhead() * n_tensors,
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ true,
+ };
+
+ model.ctx = ggml_init(params);
+ if (!model.ctx) {
+ fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+ return false;
+ }
+ }
+
+ // initialize the backend
+#ifdef GGML_USE_CUBLAS
+ if (n_gpu_layers > 0) {
+ fprintf(stderr, "%s: using CUDA backend\n", __func__);
+ model.backend = ggml_backend_cuda_init();
+ if (!model.backend) {
+ fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+ }
+ }
+#endif
+
+#ifdef GGML_USE_METAL
+ if (n_gpu_layers > 0) {
+ fprintf(stderr, "%s: using Metal backend\n", __func__);
+ ggml_metal_log_set_callback(ggml_log_callback_default, nullptr);
+ model.backend = ggml_backend_metal_init();
+ if (!model.backend) {
+ fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
+ }
+ }
+#endif
+
+ if (!model.backend) {
+ // fallback to CPU backend
+ fprintf(stderr, "%s: using CPU backend\n", __func__);
+ model.backend = ggml_backend_cpu_init();
+ }
+
+ if (!model.backend) {
+ fprintf(stderr, "%s: ggml_backend_cpu_init() failed\n", __func__);
+ return false;
+ }
+
+ // allocate weights buffer
+ model.buffer_w = ggml_backend_alloc_buffer(model.backend, buffer_size);
+
+ // 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;
+
+ 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, 3*n_embd);
+ layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
+
+ 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);
+ 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.kv_cache.k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+ model.kv_cache.v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+
+ model.kv_cache.head = 0;
+ model.kv_cache.size = n_ctx;
+
+ model.kv_cache.cells.resize(n_ctx);
+
+ const size_t memory_size = ggml_nbytes(model.kv_cache.k) + ggml_nbytes(model.kv_cache.v);
+
+ printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+
+ // create a backend buffer (can be in host or device memory)
+ model.kv_cache.buffer = ggml_backend_alloc_buffer(model.backend, memory_size + 256);
+
+ // allocate the tensors into the backend buffer
+ {
+ ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.kv_cache.buffer);
+
+ // this updates the pointers in the tensors to point to the correct location in the buffer
+ // this is necessary since the ggml_context is .no_alloc == true
+ // note that the buffer can actually be a device buffer, depending on the backend
+ ggml_allocr_alloc(alloc, model.kv_cache.k);
+ ggml_allocr_alloc(alloc, model.kv_cache.v);
+
+ ggml_allocr_free(alloc);
+ }
+ }
+
+ // load weights
+ {
+ ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer_w);
+
+ size_t total_size = 0;
+
+ bool has_lm_head = false;
+
+ std::vector<char> read_buf;
+
+ 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) == model.tensors.end()) {
+ fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.c_str());
+ return false;
+ }
+
+ auto tensor = model.tensors[name];
+ ggml_set_name(tensor, name.c_str());
+ if (ggml_nelements(tensor) != nelements) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.c_str());
+ return false;
+ }
+
+ 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.c_str(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
+ return false;
+ }
+
+ // for debugging
+ if (0) {
+ printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.c_str(), 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.c_str(), ggml_nbytes(tensor), nelements*bpe);
+ return false;
+ }
+
+ ggml_allocr_alloc(alloc, tensor);
+
+ if (ggml_backend_is_cpu (model.backend)
+#ifdef GGML_USE_METAL
+ || ggml_backend_is_metal(model.backend)
+#endif
+ ) {
+ // for the CPU and Metal backend, we can read directly into the tensor
+ fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+ } else {
+ // read into a temporary buffer first, then copy to device memory
+ read_buf.resize(ggml_nbytes(tensor));
+ fin.read(read_buf.data(), ggml_nbytes(tensor));
+ ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
+ }
+
+ // GPT-2 models share the WTE tensor as the LM head
+ if (name == "model/wte" && has_lm_head == false) {
+ //ggml_allocr_alloc(alloc, model.lm_head);
+ //ggml_backend_tensor_copy(tensor, model.lm_head);
+ model.lm_head = tensor;
+ }
+
+ if (name == "model/lm_head") {
+ has_lm_head = true;
+ }
+
+ total_size += ggml_nbytes(tensor);
+ }
+
+ ggml_allocr_free(alloc);
+ printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
+ }
+
+ fin.close();
+
+ return true;
+}
+
+// build the computation graph
+struct ggml_cgraph * gpt2_graph(
+ const gpt2_model & model,
+ struct ggml_allocr * allocr,
+ const gpt2_batch & batch) {
+ 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_head = hparams.n_head;
+
+ const auto & kv_cache = model.kv_cache;
+
+ const int32_t n_tokens = batch.n_tokens;
+ const int32_t n_kv = ggml_allocr_is_measure(allocr) ? n_ctx : kv_cache.n;
+ const int32_t kv_head = ggml_allocr_is_measure(allocr) ? n_ctx - n_tokens : kv_cache.head;
+
+ // since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
+ static size_t buf_size = ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead();
+ static std::vector<uint8_t> buf(buf_size);
+
+ struct ggml_init_params params = {
+ /*.mem_size =*/ buf_size,
+ /*.mem_buffer =*/ buf.data(),
+ /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
+ };
+
+ struct ggml_context * ctx0 = ggml_init(params);
+
+ struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+ struct ggml_tensor * inpL;
+ if (batch.token) {
+ struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ ggml_allocr_alloc(allocr, inp_tokens);
+ if (!ggml_allocr_is_measure(allocr)) {
+ ggml_backend_tensor_set(inp_tokens, batch.token, 0, n_tokens*ggml_element_size(inp_tokens));
+ }
+
+ struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ ggml_allocr_alloc(allocr, position);
+ if (!ggml_allocr_is_measure(allocr)) {
+ for (int i = 0; i < n_tokens; ++i) {
+ int32_t v = batch.pos[i];
+ ggml_backend_tensor_set(position, &v, i*sizeof(int32_t), sizeof(v));
+ }
+ }
+
+ // wte + wpe
+ inpL =
+ ggml_add(ctx0,
+ ggml_get_rows(ctx0, model.wte, inp_tokens),
+ ggml_get_rows(ctx0, model.wpe, position));
+ } else {
+ GGML_ASSERT(batch.embd);
+
+ inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+ ggml_allocr_alloc(allocr, inpL);
+ if (!ggml_allocr_is_measure(allocr)) {
+ ggml_backend_tensor_set(inpL, batch.embd, 0, n_tokens * n_embd * ggml_element_size(inpL));
+ }
+ }
+
+ struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+ ggml_allocr_alloc(allocr, KQ_scale);
+ if (!ggml_allocr_is_measure(allocr)) {
+ float s = 1.0f/sqrtf(float(n_embd)/n_head);
+ ggml_backend_tensor_set(KQ_scale, &s, 0, sizeof(s));
+ }
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+ ggml_set_name(KQ_mask, "KQ_mask");
+ ggml_allocr_alloc(allocr, KQ_mask);
+ if (!ggml_allocr_is_measure(allocr)) {
+ std::vector<float> data_buf(n_kv*n_tokens);
+ const float neg_inf_v = -INFINITY;
+
+ for (int h = 0; h < 1; ++h) {
+ int h_offset = h*(n_kv*n_tokens);
+ for (int j = 0; j < n_tokens; ++j) {
+ const gpt2_pos pos = batch.pos[j];
+ const gpt2_seq_id seq_id = batch.seq_id[j];
+
+ for (int i = 0; i < n_kv; ++i) {
+ if (!kv_cache.cells[i].has_seq_id(seq_id) || kv_cache.cells[i].pos > pos) {
+ data_buf[h_offset + j*n_kv + i] = neg_inf_v;
+ }
+ }
+ }
+ }
+
+ ggml_backend_tensor_set(KQ_mask, data_buf.data(), 0, data_buf.size() * sizeof(float));
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * cur;
+
+ // 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,
+ cur,
+ model.layers[il].ln_1_g),
+ model.layers[il].ln_1_b);
+ }
+
+ // attn
+ // [2304, 768] - model.layers[il].c_attn_attn_w
+ // [2304, 1] - model.layers[il].c_attn_attn_b
+ // [ 768, n_tokens] - cur (in)
+ // [2304, n_tokens] - cur (out)
+ //
+ // cur = attn_w*cur + attn_b
+ // [2304, n_tokens]
+ {
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].c_attn_attn_w,
+ cur);
+
+ cur = ggml_add(ctx0,
+ cur,
+ model.layers[il].c_attn_attn_b);
+ }
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
+ struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*n_embd);
+ struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*n_embd);
+
+ // store key and value to memory
+ if (n_tokens >= 1) {
+ struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_cache.k, n_tokens*n_embd, (ggml_element_size(model.kv_cache.k)*n_embd)*(il*n_ctx + kv_head));
+ struct ggml_tensor * v = ggml_view_1d(ctx0, model.kv_cache.v, n_tokens*n_embd, (ggml_element_size(model.kv_cache.v)*n_embd)*(il*n_ctx + kv_head));
+
+ 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_tokens)),
+ 0, 2, 1, 3);
+
+ // K = Kmem.view(n_embd/n_head, n_head, n_kv).permute(0, 2, 1, 3)
+ // [64, n_kv, 12]
+ struct ggml_tensor * K =
+ ggml_permute(ctx0,
+ ggml_reshape_3d(ctx0,
+ ggml_view_1d(ctx0, model.kv_cache.k, n_kv*n_embd, il*n_ctx*ggml_element_size(model.kv_cache.k)*n_embd),
+ n_embd/n_head, n_head, n_kv),
+ 0, 2, 1, 3);
+
+ // GG: flash attention
+ //struct ggml_tensor * V =
+ // ggml_cpy(ctx0,
+ // ggml_permute(ctx0,
+ // ggml_reshape_3d(ctx0,
+ // ggml_view_1d(ctx0, model.kv_cache.v, n_kv*n_embd, il*n_ctx*ggml_element_size(model.kv_cache.v)*n_embd),
+ // n_embd/n_head, n_head, n_kv),
+ // 1, 2, 0, 3),
+ // ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_embd/n_head, n_head));
+
+ //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
+
+ // K * Q
+ // [n_kv, n_tokens, 12]
+ struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+
+ // KQ_scaled = KQ / sqrt(n_embd/n_head)
+ // [n_kv, n_tokens, 12]
+ struct ggml_tensor * KQ_scaled =
+ ggml_scale(ctx0,
+ KQ,
+ KQ_scale);
+
+ // KQ_masked = mask_past(KQ_scaled)
+ // [n_kv, n_tokens, 12]
+ struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+
+ // KQ = soft_max(KQ_masked)
+ // [n_kv, N, 12]
+ struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+
+ // V_trans = Vmem.view(n_embd/n_head, n_head, n_kv).permute(1, 2, 0, 3).contiguous()
+ // [n_kv, 64, 12]
+ struct ggml_tensor * V_trans =
+ ggml_cpy(ctx0,
+ ggml_permute(ctx0,
+ ggml_reshape_3d(ctx0,
+ ggml_view_1d(ctx0, model.kv_cache.v, n_kv*n_embd, il*n_ctx*ggml_element_size(model.kv_cache.v)*n_embd),
+ n_embd/n_head, n_head, n_kv),
+ 1, 2, 0, 3),
+ ggml_new_tensor_3d(ctx0, model.kv_cache.v->type, n_kv, n_embd/n_head, n_head));
+
+ // KQV = transpose(V) * KQ_soft_max
+ // [64, n_tokens, 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_tokens]
+ struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+ // cur = KQV_merged.contiguous().view(n_embd, N)
+ // [768, n_tokens]
+ cur = ggml_cpy(ctx0,
+ KQV_merged,
+ ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens));
+ }
+
+ // 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,
+ cur,
+ model.layers[il].c_attn_proj_b);
+ }
+
+ // add the input
+ cur = ggml_add(ctx0, cur, inpL);
+
+ struct ggml_tensor * inpFF = cur;
+
+ // 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,
+ cur,
+ model.layers[il].ln_2_g),
+ model.layers[il].ln_2_b);
+ }
+
+ // 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,
+ cur,
+ model.layers[il].c_mlp_fc_b);
+
+ // 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,
+ cur,
+ model.layers[il].c_mlp_proj_b);
+ }
+
+ // input for next layer
+ inpL = ggml_add(ctx0, cur, inpFF);
+ }
+
+ // 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,
+ inpL,
+ model.ln_f_g),
+ model.ln_f_b);
+ }
+
+ // 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(ctx0, inpL);
+
+ ggml_build_forward_expand(gf, inpL);
+
+ ggml_free(ctx0);
+
+ return gf;
+}
+
+static void gpt2_kv_cache_seq_cp(
+ struct gpt2_kv_cache & cache,
+ gpt2_seq_id seq_id_src,
+ gpt2_seq_id seq_id_dst,
+ gpt2_pos p0,
+ gpt2_pos p1) {
+ if (p0 < 0) p0 = 0;
+ if (p1 < 0) p1 = std::numeric_limits<gpt2_pos>::max();
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+ cache.cells[i].seq_id.insert(seq_id_dst);
+ }
+ }
+}
+
+struct gpt2_batch gpt2_batch_init(int32_t n_tokens, int32_t embd) {
+ gpt2_batch batch;
+
+ if (embd) {
+ batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
+ } else {
+ batch.token = (gpt_vocab::id *) malloc(sizeof(gpt_vocab::id) * n_tokens);
+ }
+
+ batch.pos = (gpt2_pos *) malloc(sizeof(gpt2_pos) * n_tokens);
+ batch.seq_id = (gpt2_seq_id *) malloc(sizeof(gpt2_seq_id) * n_tokens);
+ batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
+
+ return batch;
+}
+
+void gpt2_batch_free(struct gpt2_batch batch) {
+ if (batch.token) free(batch.token);
+ if (batch.embd) free(batch.embd);
+ if (batch.pos) free(batch.pos);
+ if (batch.seq_id) free(batch.seq_id);
+ if (batch.logits) free(batch.logits);
+}
+
+// Positive return values does not mean a fatal error, but rather a warning.
+// 0 - success
+// < 0 - error
+int gpt2_decode(
+ struct gpt2_model & model,
+ struct ggml_allocr * allocr,
+ struct gpt2_batch batch,
+ int n_threads,
+ std::vector<float> & logits) {
+ const int32_t n_tokens = batch.n_tokens;
+ const auto & hparams = model.hparams;
+ const int n_vocab = hparams.n_vocab;
+
+ if (n_tokens == 0) {
+ printf("%s: n_tokens == 0", __func__);
+ return -1;
+ }
+
+ GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd));
+
+ auto & cache = model.kv_cache;
+
+ for (int i = 0; i < n_tokens; i++) {
+ cache.cells[cache.head + i].pos = batch.pos[i];
+ cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i]);
+ }
+
+ cache.n = cache.head + n_tokens;
+
+ // reset the allocator to free all the memory allocated during the previous inference
+ ggml_allocr_reset(allocr);
+
+ struct ggml_cgraph * gf = gpt2_graph(model, allocr, batch);
+
+ // allocate tensors
+ ggml_allocr_alloc_graph(allocr, gf);
+
+ // run the computation
+ if (ggml_backend_is_cpu(model.backend)) {
+ ggml_backend_cpu_set_n_threads(model.backend, n_threads);
+ }
+#ifdef GGML_USE_METAL
+ if (ggml_backend_is_metal(model.backend)) {
+ ggml_backend_metal_set_n_cb(model.backend, n_threads);
+ }
+#endif
+ ggml_backend_graph_compute(model.backend, gf);
+
+ //if (n_past%100 == 0) {
+ // ggml_graph_print (&gf);
+ // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+ //}
+
+ // in this case, the output tensor is the last one in the graph
+ struct ggml_tensor * inpL = gf->nodes[gf->n_nodes - 1];
+
+ if (batch.logits) {
+ // return logits for all tokens
+ logits.resize(n_vocab*n_tokens);
+ for (int32_t i = 0; i < n_tokens; i++) {
+ if (batch.logits[i] == 0) {
+ continue;
+ }
+ ggml_backend_tensor_get(inpL, logits.data() + n_vocab*i, n_vocab*i*sizeof(float), sizeof(float)*n_vocab);
+ }
+ } else {
+ // return result just for the last token
+ logits.resize(n_vocab);
+ ggml_backend_tensor_get(inpL, logits.data(), (n_vocab*(n_tokens-1))*sizeof(float), sizeof(float)*n_vocab);
+ }
+
+ // update the kv ring buffer
+ cache.head += n_tokens;
+
+ // ensure kv cache head points to a valid index.
+ if (cache.head >= cache.size) {
+ printf("%s: cache.head >= cache.size\n", __func__);
+ return -2;
+ }
+
+ return 0;
+}
+
+int main(int argc, char ** argv) {
+ ggml_time_init();
+
+ const int64_t t_main_start_us = ggml_time_us();
+
+ gpt_params params;
+
+ 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;
+ gpt2_model model;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!gpt2_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);
+ }
+
+ // tokenize the prompt
+ std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
+
+ // keep this buffer alive while evaluating the model
+ ggml_backend_buffer_t buf_compute;
+
+ // create a gpt2_batch
+ // we use this object to submit token data for decoding
+ const int n_parallel = params.n_parallel;
+ gpt2_batch batch = gpt2_batch_init(std::max(embd_inp.size(), (size_t)n_parallel), 0);
+
+ // evaluate the initial prompt
+ batch.n_tokens = embd_inp.size();
+
+ for (int32_t i = 0; i < batch.n_tokens; i++) {
+ batch.token[i] = embd_inp[i];
+ batch.pos[i] = i;
+ batch.seq_id[i] = 0;
+ batch.logits[i] = false;
+ }
+
+ // gpt2_decode will output logits only for the last token of the prompt
+ batch.logits[batch.n_tokens - 1] = true;
+
+ struct ggml_allocr * allocr = NULL;
+ // allocate the compute buffer
+ {
+ // alignment required by the backend
+ size_t align = ggml_backend_get_alignment(model.backend);
+ allocr = ggml_allocr_new_measure(align);
+
+ // create the worst case graph for memory usage estimation
+ struct ggml_cgraph * gf = gpt2_graph(model, allocr, batch);
+
+ // compute the required memory
+ size_t mem_size = ggml_allocr_alloc_graph(allocr, gf);
+
+ // recreate the allocator with the required memory
+ ggml_allocr_free(allocr);
+ buf_compute = ggml_backend_alloc_buffer(model.backend, mem_size);
+ allocr = ggml_allocr_new_from_buffer(buf_compute);
+
+ fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0/1024.0);
+ }
+
+ int64_t t_sample_us = 0;
+ int64_t t_predict_us = 0;
+
+ std::vector<float> logits;
+
+ if (gpt2_decode(model, allocr, batch, params.n_threads, logits) != 0) {
+ printf("%s: gpt2_decode() failed\n", __func__);
+ return 1;
+ }
+
+ // assign the system KV cache to all parallel sequences
+ // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
+ for (int32_t i = 1; i < n_parallel; ++i) {
+ gpt2_kv_cache_seq_cp(model.kv_cache, 0, i, 0, batch.n_tokens);
+ }
+
+ if (n_parallel > 1) {
+ printf("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
+ }
+
+ 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, first 8 tokens: ", __func__, embd_inp.size());
+ for (int i = 0; i < std::min(8, (int) embd_inp.size()); i++) {
+ printf("%d ", embd_inp[i]);
+ }
+ printf("\n\n");
+
+ std::vector<gpt_vocab::token> streams(n_parallel);
+
+ // remember the batch index of the last token for each parallel sequence
+ // we need this to determine which logits to sample from
+ std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
+
+ int n_cur = batch.n_tokens;
+ int n_len = batch.n_tokens + params.n_predict;
+ int n_decoded = 0;
+
+ const int n_vocab = model.hparams.n_vocab;
+ const int top_k = params.top_k;
+ const float top_p = params.top_p;
+ const float temp = params.temp;
+
+ while (n_cur < n_len) {
+ batch.n_tokens = 0;
+
+ for (int32_t i = 0; i < n_parallel; ++i) {
+ if (i_batch[i] < 0) {
+ // the stream has already finished
+ continue;
+ }
+
+ auto * logits_i = logits.data() + i_batch[i]*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_i, top_k, top_p, temp, rng);
+
+ t_sample_us += ggml_time_us() - t_start_sample_us;
+ }
+
+ // is it an end of stream? -> mark the stream as finished
+ if (id == 50256 || n_cur == n_len - 1) {
+ i_batch[i] = -1;
+ printf("\n");
+ if (n_parallel > 1) {
+ printf("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
+ }
+
+ continue;
+ }
+
+ auto& token = vocab.id_to_token[id];
+ if (n_parallel == 1) {
+ printf("%s", token.c_str());
+ fflush(stdout);
+ }
+
+ streams[i] += token;
+
+ // push this new token for next evaluation
+ batch.token [batch.n_tokens] = id;
+ batch.pos [batch.n_tokens] = n_cur;
+ batch.seq_id[batch.n_tokens] = i;
+ batch.logits[batch.n_tokens] = true;
+
+ i_batch[i] = batch.n_tokens;
+
+ batch.n_tokens += 1;
+
+ n_decoded += 1;
+ }
+
+ // all streams are finished
+ if (batch.n_tokens == 0) {
+ break;
+ }
+
+ n_cur += 1;
+
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ // evaluate the current batch with the transformer model
+ int ret_code = gpt2_decode(model, allocr, batch, params.n_threads, logits);
+ if (ret_code != 0) {
+ fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, ret_code);
+ return 1;
+ }
+
+ t_predict_us += ggml_time_us() - t_start_us;
+ }
+ }
+
+ if (n_parallel > 1) {
+ printf("\n");
+
+ for (int32_t i = 0; i < n_parallel; ++i) {
+ printf("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
+ }
+ }
+
+ // report timing
+ {
+ const int64_t t_main_end_us = ggml_time_us();
+
+ printf("\n\n");
+ printf("%s: n_decoded = %8d\n", __func__, n_decoded);
+ 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);
+ printf("%s: predict time = %8.2f ms\n", __func__, t_predict_us/1000.0f);
+ printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
+ }
+
+ gpt2_batch_free(batch);
+ ggml_free(model.ctx);
+
+ ggml_backend_buffer_free(model.buffer_w);
+ ggml_backend_buffer_free(model.kv_cache.buffer);
+ ggml_backend_buffer_free(buf_compute);
+ ggml_backend_free(model.backend);
+
+ return 0;
+}
overview / pkg / ggml / sources / ggml / commitdiff