added support for gptneox models

include/turbopilot/gptneox.hpp

@@ -0,0 +1,87 @@
+#ifndef __TURBOPILOT_GPTNEOX_H
+#define __TURBOPILOT_GPTNEOX_H
+
+#include <turbopilot/model.hpp>
+
+#include <vector>
+#include <map>
+
+// default hparams (StableLM 3B)
+struct gpt_neox_hparams {
+    int32_t n_vocab = 50257;
+    int32_t n_ctx   = 4096;
+    int32_t n_embd  = 4096;
+    int32_t n_head  = 32;
+    int32_t n_layer = 16;
+    int32_t n_rot   = 32; // rotary_pct * (n_embd / n_head)
+    int32_t par_res = 1; // 1 = true, 0 = false
+    int32_t ftype   = 1;
+};
+
+struct gpt_neox_layer {
+    // pre normalization
+    struct ggml_tensor * ln_1_g;
+    struct ggml_tensor * ln_1_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;
+
+    // post normalization
+    struct ggml_tensor * ln_2_g;
+    struct ggml_tensor * ln_2_b;
+
+    // ff
+    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 gpt_neox_model {
+    gpt_neox_hparams hparams;
+
+    // normalization
+    struct ggml_tensor * ln_f_g;
+    struct ggml_tensor * ln_f_b;
+
+    struct ggml_tensor * wte; // position embedding
+
+    struct ggml_tensor * lmh_g; // language model head
+    //struct ggml_tensor * lmh_b; // language model bias
+
+    std::vector<gpt_neox_layer> layers;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    //
+    struct ggml_context * ctx;
+    std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+
+class GPTNEOXModel : public TurbopilotModel {
+
+public:
+    GPTNEOXModel(ModelConfig config, std::mt19937 &rng) : TurbopilotModel(config, rng){
+        this->model = new gpt_neox_model{};
+        this->vocab = new gpt_vocab{};
+    }
+    virtual ~GPTNEOXModel();
+    bool load_model(std::string path);
+    virtual std::stringstream predict(std::string prompt, int max_length, bool include_prompt);
+
+private:
+    gpt_neox_model *model = NULL;
+    gpt_vocab *vocab = NULL;
+
+
+};
+
+#endif // __TURBOPILOT_GPTNEOX_H

src/CMakeLists.txt

@@ -6,11 +6,13 @@ include_directories(${Boost_INCLUDE_DIRS})
 add_executable(${TURBOPILOT_TARGET} 
   main.cpp 
   gptj.cpp
+  gptneox.cpp
   common.cpp
   server.cpp
   starcoder.cpp
   ../include/turbopilot/model.hpp
   ../include/turbopilot/gptj.hpp
+  ../include/turbopilot/gptneox.hpp
   ../include/turbopilot/starcoder.hpp
   )
 

src/gptneox.cpp

@@ -0,0 +1,707 @@
+#include <turbopilot/gptneox.hpp>
+#include <spdlog/spdlog.h>
+
+#include <ggml/ggml.h>
+
+#include <cinttypes>
+
+#include <iostream>
+#include <fstream>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+
+// feed-forward network
+ggml_tensor * gpt_neox_ff(
+        const gpt_neox_layer &layer,
+        ggml_context * ctx0,
+        ggml_tensor * inp) {
+    ggml_tensor * cur = ggml_norm(ctx0, inp);
+
+    cur = ggml_add(ctx0,
+        ggml_mul(ctx0,
+            ggml_repeat(ctx0, layer.ln_2_g, cur),
+            cur),
+        ggml_repeat(ctx0, layer.ln_2_b, cur));
+
+    cur = ggml_mul_mat(ctx0,
+            layer.c_mlp_fc_w,
+            cur);
+
+    cur = ggml_add(ctx0,
+            ggml_repeat(ctx0, layer.c_mlp_fc_b, cur),
+            cur);
+
+    // GELU activation
+    cur = ggml_gelu(ctx0, cur);
+
+    // projection
+    // cur = proj_w*cur + proj_b
+    cur = ggml_mul_mat(ctx0,
+            layer.c_mlp_proj_w,
+            cur);
+
+    cur = ggml_add(ctx0,
+            ggml_repeat(ctx0, layer.c_mlp_proj_b, cur),
+            cur);
+    return cur;
+}
+
+
+// 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 gpt_neox_eval(
+        const gpt_neox_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;
+
+    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;
+    const int n_rot   = hparams.n_rot;
+
+    static size_t buf_size = 256u*1024*1024;
+    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;
+    static void * scr0 = malloc(scr0_size);
+
+    static size_t scr1_size = 256u*1024*1024;
+    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));
+
+    // wte
+    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * cur;
+
+        ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
+
+        // self-attention
+        {
+            {
+                cur = ggml_norm(ctx0, inpL);
+
+                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));
+            }
+
+            // compute QKV
+            {
+                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);
+            }
+
+            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head));
+            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head));
+            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head));
+
+            // using mode = 2 for GPT-NeoX mode
+            Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2, 0);
+            Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2, 0);
+
+            // store key and value to memory
+            {
+                Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd, N));
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd,
+                        (   n_ctx)*ggml_element_size(model.memory_v),
+                        (il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v));
+
+                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)
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        Qcur,
+                        0, 2, 1, 3);
+
+            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
+            struct ggml_tensor * K =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        0, 2, 1, 3);
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+
+            // KQ_scaled = KQ / sqrt(n_embd/n_head)
+            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)
+            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
+
+            // KQ = soft_max(KQ_masked)
+            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()
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, model.memory_v,
+                        n_past + N, n_embd/n_head, n_head,
+                        n_ctx*ggml_element_size(model.memory_v),
+                        n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head,
+                        il*n_ctx*ggml_element_size(model.memory_v)*n_embd);
+
+            // KQV = transpose(V) * KQ_soft_max
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            // cur = KQV_merged.contiguous().view(n_embd, N)
+            cur = ggml_cpy(ctx0,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+
+            // projection
+            {
+                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);
+            }
+        }
+
+        ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
+
+        if (hparams.par_res == 0) {
+            struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL);
+
+            cur = gpt_neox_ff(model.layers[il], ctx0, inpFF);
+
+            // input for next layer
+            inpL = ggml_add(ctx0, cur, inpFF);
+        } else {
+            struct ggml_tensor * inpFF = cur;
+
+            // this is independent of the self-attention result, so it could be done in parallel to the self-attention
+            // note here we pass inpL instead of cur
+            cur = gpt_neox_ff(model.layers[il], ctx0, inpL);
+
+            // layer input + FF
+            cur  = ggml_add(ctx0, cur, inpFF);
+
+            // input for next layer
+            inpL = ggml_add(ctx0, cur, inpL);
+        }
+    }
+
+    ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
+
+    // norm
+    {
+        inpL = ggml_norm(ctx0, inpL);
+
+        // inpL = ln_f_g*inpL + ln_f_b
+        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, });
+
+    // lm_head
+    {
+        inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);
+
+        //inpL = ggml_add(ctx0,
+        //        ggml_repeat(ctx0, model.lmh_b, inpL),
+        //        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 for just 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\n", ggml_used_mem(ctx0));
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+
+GPTNEOXModel::~GPTNEOXModel(){
+    ggml_free(model->ctx);
+    free(model);
+    free(vocab);
+}
+
+bool GPTNEOXModel::load_model(std::string fname) {
+    printf("%s: loading model from '%s' - please wait ...\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.n_rot,   sizeof(hparams.n_rot));
+        fin.read((char *) &hparams.par_res, sizeof(hparams.par_res));
+        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: n_rot   = %d\n", __func__, hparams.n_rot);
+        printf("%s: par_res = %d\n", __func__, hparams.par_res);
+        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
+        printf("%s: qntvr   = %d\n", __func__, qntvr);
+
+        hparams.ftype %= GGML_QNT_VERSION_FACTOR;
+    }
+
+    // load vocab
+    {
+        const int32_t n_vocab = model->hparams.n_vocab;
+
+        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 ctx_size = 0;
+
+    {
+        const auto & hparams = model->hparams;
+
+        const size_t n_embd  = hparams.n_embd;
+        const size_t n_layer = hparams.n_layer;
+        const size_t n_ctx   = hparams.n_ctx;
+        const size_t n_vocab = hparams.n_vocab;
+
+        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_embd*n_vocab*ggml_type_sizef(wtype); // wte
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype);           // lmh_g
+        //ctx_size +=        n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b
+
+        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*(3*n_embd*n_embd*ggml_type_sizef(wtype));         // c_attn_attn_w
+        ctx_size += n_layer*(       3*n_embd*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*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_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*(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 + 16*n_layer)*1024; // 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   =*/ false,
+        };
+
+        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_vocab = hparams.n_vocab;
+
+        model->layers.resize(n_layer);
+
+        model->wte    = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+
+        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->lmh_g  = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        //model->lmh_b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);
+
+        // map by name
+        model->tensors["gpt_neox.embed_in.weight"] = model->wte;
+
+        model->tensors["gpt_neox.final_layer_norm.weight"] = model->ln_f_g;
+        model->tensors["gpt_neox.final_layer_norm.bias"]   = model->ln_f_b;
+
+        model->tensors["embed_out.weight"] = model->lmh_g;
+        //model->tensors["lm_head.bias"]   = model->lmh_b;
+
+        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.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.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_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["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"] = layer.ln_1_g;
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"]   = layer.ln_1_b;
+
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.c_attn_attn_w;
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias"]   = layer.c_attn_attn_b;
+
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"] = layer.c_attn_proj_w;
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"]   = layer.c_attn_proj_b;
+
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.ln_2_g;
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"]   = layer.ln_2_b;
+
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"] = layer.c_mlp_fc_w;
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"]   = layer.c_mlp_fc_b;
+
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"] = layer.c_mlp_proj_w;
+            model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"]   = 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 int64_t n_mem      = n_layer*n_ctx;
+        const int64_t n_elements = n_embd*n_mem;
+
+        model->memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+        model->memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+
+        const size_t memory_size = ggml_nbytes(model->memory_k) + ggml_nbytes(model->memory_v);
+
+        printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
+    }
+
+    // load weights
+    {
+        int n_tensors = 0;
+        size_t total_size = 0;
+
+        printf("%s: ", __func__);
+
+        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 (ggml_nelements(tensor) != nelements) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                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 [%5d, %5d], expected [%5d, %5d]\n",
+                        __func__, name.data(), (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.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;
+            }
+
+            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+            total_size += ggml_nbytes(tensor);
+            if (++n_tensors % 8 == 0) {
+                printf(".");
+                fflush(stdout);
+            }
+        }
+
+        printf(" done\n");
+
+        printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
+    }
+
+    fin.close();
+
+    return true;
+}
+
+std::stringstream GPTNEOXModel::predict(std::string prompt, int max_length, bool include_prompt) {
+
+    std::stringstream result;
+    // tokenize the prompt
+    std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize((*vocab), prompt);
+
+    auto END_TOKEN_ID = vocab->token_to_id["<|endoftext|>"];
+
+    int n_past = 0;
+
+    int64_t t_sample_us  = 0;
+    int64_t t_predict_us = 0;
+
+    int n_predict = std::min(max_length, model->hparams.n_ctx - (int) embd_inp.size());
+
+    spdlog::debug("{}: number of tokens in prompt = {}", __func__, embd_inp.size());
+
+    std::vector<gpt_vocab::id> embd;
+
+    // determine the required inference memory per token:
+    size_t mem_per_token = 0;
+
+    std::vector<float> logits;
+
+    gpt_neox_eval((*model), config.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+
+    for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
+        // predict
+        if (embd.size() > 0) {
+            const int64_t t_start_us = ggml_time_us();
+
+            if (!gpt_neox_eval((*model), config.n_threads, n_past, embd, logits, mem_per_token)) {
+                throw std::runtime_error("Failed to predict");
+            }
+
+            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 = config.top_k;
+            const float top_p = config.top_p;
+            const float temp  = config.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);
+
+            // do not actually add endoftext char to the end string
+            if(id != END_TOKEN_ID){            
+                result << vocab->id_to_token[id].c_str();
+            }
+            
+        } 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(include_prompt){
+                    result << vocab->id_to_token[embd_inp[k]].c_str();
+                }
+
+                if (embd.size() > config.n_batch) {
+                    break;
+                }
+            }
+            i += embd.size() - 1;
+        }
+        
+
+        // end of text token
+        //if (embd.back() == 50256) {
+        if(embd.back() == END_TOKEN_ID){     
+            break;
+        }
+    }
+
+    return result;
+}

src/main.cpp

@@ -11,6 +11,7 @@
 #include "turbopilot/model.hpp"
 #include "turbopilot/starcoder.hpp"
 #include "turbopilot/gptj.hpp"
+#include "turbopilot/gptneox.hpp"
 #include "turbopilot/server.hpp"
 
 int main(int argc, char **argv)
@@ -23,7 +24,7 @@ int main(int argc, char **argv)
         .required();
 
     program.add_argument("-m", "--model-type")
-        .help("The type of model to load. Can be codegen,starcoder,wizardcoder")
+        .help("The type of model to load. Can be codegen,starcoder,wizardcoder,stablecode")
         .default_value("codegen");
 
     program.add_argument("-t", "--threads")
@@ -76,6 +77,9 @@ int main(int argc, char **argv)
     }else if(model_type.compare("starcoder") == 0 || model_type.compare("wizardcoder") == 0){
         spdlog::info("Initializing Starcoder/Wizardcoder type model for '{}' model type", model_type);
         model = new StarcoderModel(config, rng);
+    }else if(model_type.compare("stablecode") == 0){
+        spdlog::info("Initializing StableLM type model for '{}' model type", model_type);
+        model = new GPTNEOXModel(config, rng);
     }else{
         spdlog::error("Invalid model type: {}", model_type);
     }