otherarch/gpt2_v3.cpp

#include "ggml_v3.h"
#include "otherarch.h"

#include "utils.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <iostream>
#include <algorithm>

#include "model_adapter.h"

#ifdef GGML_USE_CUDA
#include "ggml_v3-cuda.h"
#endif
#if defined(GGML_USE_CLBLAST)
#include "ggml_v3-opencl.h"
#endif


// load the model's weights from a file
ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab, FileFormat file_format, int gpulayers) {
    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 ModelLoadResult::FAIL;
    }

    // verify magic
    {
        uint32_t magic;
        fin.read((char *) &magic, sizeof(magic));
        if (magic != 0x67676d6c) {
            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
            return ModelLoadResult::FAIL;
        }
    }

    int32_t origmaxctx = model.hparams.n_ctx;

    // 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_V3_QNT_VERSION_FACTOR;

        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
        printf("%s: n_ctx   = %d (%d)\n", __func__, hparams.n_ctx,origmaxctx);
        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_V3_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 ModelLoadResult::FAIL;
        }

        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);
            }
        }
    }

    // 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_v3_type wtype = ggml_v3_ftype_to_ggml_v3_type((ggml_v3_ftype) (model.hparams.ftype));
    if (wtype == GGML_V3_TYPE_COUNT) {
        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
                __func__, fname.c_str(), model.hparams.ftype);
        return ModelLoadResult::FAIL;
    }

    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_v3_type_sizef(GGML_V3_TYPE_F32); // ln_f_g
        ctx_size += n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32); // ln_f_b

        ctx_size += n_vocab*n_embd*ggml_v3_type_sizef(wtype);         // wte
        ctx_size +=   n_ctx*n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32); // wpe
        ctx_size += n_vocab*n_embd*ggml_v3_type_sizef(wtype);         // lm_head

        ctx_size += n_layer*(n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // ln_1_g
        ctx_size += n_layer*(n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // ln_1_b

        ctx_size += n_layer*(n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // ln_2_g
        ctx_size += n_layer*(n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // ln_2_b

        ctx_size += n_layer*((n_embd + 2*kv_dim)*n_embd*ggml_v3_type_sizef(wtype));         // c_attn_attn_w // TODO:
        ctx_size += n_layer*(       (n_embd + 2*kv_dim)*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // c_attn_attn_b

        ctx_size += n_layer*(n_embd*n_embd*ggml_v3_type_sizef(wtype));           // c_attn_proj_w
        ctx_size += n_layer*(       n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32));   // c_attn_proj_b

        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v3_type_sizef(wtype));         // c_mlp_fc_w
        ctx_size += n_layer*(       4*n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // c_mlp_fc_b

        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v3_type_sizef(wtype));         // c_mlp_proj_w
        ctx_size += n_layer*(         n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F32)); // c_mlp_proj_b

        ctx_size += std::max(origmaxctx,n_ctx)*n_layer*n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F16); // memory_k
        ctx_size += std::max(origmaxctx,n_ctx)*n_layer*n_embd*ggml_v3_type_sizef(GGML_V3_TYPE_F16); // memory_v

        ctx_size += (6 + 12*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_v3_init_params params;
        params.mem_size   = ctx_size;
        params.mem_buffer = NULL;
        params.no_alloc   = false;

        model.ctx = ggml_v3_init(params);
        if (!model.ctx) {
            fprintf(stderr, "%s: ggml_v3_init() failed\n", __func__);
            return ModelLoadResult::FAIL;
        }
    }

    // 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_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32, n_embd);
        model.ln_f_b = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32, n_embd);

        model.wte     = ggml_v3_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
        model.wpe     = ggml_v3_new_tensor_2d(ctx, GGML_V3_TYPE_F32, n_embd, n_ctx);
        model.lm_head = ggml_v3_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_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32,   n_embd);
            layer.ln_1_b        = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32,   n_embd);

            layer.ln_2_g        = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32,   n_embd);
            layer.ln_2_b        = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32,   n_embd);

            layer.c_attn_attn_w = ggml_v3_new_tensor_2d(ctx, wtype,           n_embd, n_embd + 2*kv_dim);
            layer.c_attn_attn_b = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32, n_embd + 2*kv_dim);

            layer.c_attn_proj_w = ggml_v3_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
            layer.c_attn_proj_b = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32,   n_embd);

            layer.c_mlp_fc_w    = ggml_v3_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd); //TODO: 4*n_embd = config.n_inner
            layer.c_mlp_fc_b    = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F32, 4*n_embd);

            layer.c_mlp_proj_w  = ggml_v3_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
            layer.c_mlp_proj_b  = ggml_v3_new_tensor_1d(ctx, GGML_V3_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*std::max(origmaxctx,n_ctx);
        const int n_elements = n_embd*n_mem;

        model.memory_k = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F16, n_elements);
        model.memory_v = ggml_v3_new_tensor_1d(ctx, GGML_V3_TYPE_F16, n_elements);

        const size_t memory_size = ggml_v3_nbytes(model.memory_k) + ggml_v3_nbytes(model.memory_v);

        printf("%s: 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 ModelLoadResult::FAIL;
            }

            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 ModelLoadResult::FAIL;
            }
            if (ggml_v3_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file. got %d, expected %d\n",
                        __func__, name.data(), (int) ggml_v3_nelements(tensor), nelements);
                return ModelLoadResult::FAIL;
            }

            // for debugging
            if (0) {
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_v3_type_name(ggml_v3_type(ttype)), ggml_v3_nbytes(tensor)/1024.0/1024.0, ggml_v3_nbytes(tensor));
            }

            const size_t bpe = ggml_v3_type_size(ggml_v3_type(ttype));

            if ((nelements*bpe)/ggml_v3_blck_size(tensor->type) != ggml_v3_nbytes(tensor)) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
                        __func__, name.data(), ggml_v3_nbytes(tensor), nelements*bpe);
                return ModelLoadResult::FAIL;
            }

            fin.read(reinterpret_cast<char *>(tensor->data), ggml_v3_nbytes(tensor));

            // GPT-2 models share the WTE tensor as the LM head
            if (name == "model/wte" && has_lm_head == false) {
                memcpy(model.lm_head->data, tensor->data, ggml_v3_nbytes(tensor));
            }

            if (name == "model/lm_head") {
                has_lm_head = true;
            }

            total_size += ggml_v3_nbytes(tensor);
        }

        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
    }

    fin.close();

    //gpu offload
    #if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUDA)
    if(gpulayers>0)
    {
        const auto & hparams = model.hparams;
        size_t vram_total = 0;
        const int n_gpu = std::min(gpulayers, int(hparams.n_layer));
        #if defined(GGML_USE_CLBLAST)
        fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
        #else
        fprintf(stderr, "%s: [CUDA] offloading %d layers to GPU\n", __func__, n_gpu);
        #endif
        for (int i = 0; i < n_gpu; ++i) {
            const auto & layer = model.layers[i];
            layer.c_attn_attn_w->backend = GGML_V3_BACKEND_GPU;
            layer.c_attn_proj_w->backend = GGML_V3_BACKEND_GPU;
            layer.c_mlp_fc_w->backend = GGML_V3_BACKEND_GPU;
            layer.c_mlp_proj_w->backend = GGML_V3_BACKEND_GPU;
            #if defined(GGML_USE_CLBLAST)
            ggml_v3_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_v3_nbytes(layer.c_attn_attn_w);
            ggml_v3_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_v3_nbytes(layer.c_attn_proj_w);
            ggml_v3_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_v3_nbytes(layer.c_mlp_fc_w);
            ggml_v3_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_v3_nbytes(layer.c_mlp_proj_w);
            #else
            ggml_v3_cuda_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_v3_nbytes(layer.c_attn_attn_w);
            ggml_v3_cuda_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_v3_nbytes(layer.c_attn_proj_w);
            ggml_v3_cuda_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_v3_nbytes(layer.c_mlp_fc_w);
            ggml_v3_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_v3_nbytes(layer.c_mlp_proj_w);
            #endif
        }
        #if defined(GGML_USE_CLBLAST)
            fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
        #else
            fprintf(stderr, "%s: [CUDA] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
        #endif
    }
    #endif

    return ModelLoadResult::SUCCESS;
}

// 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 gpt2_eval(
        const gpt2_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,
              bool use_scratch) {
    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;

    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 = (n_embd>2400?512u:256u)*1024*1024*(hparams.n_ctx>8192?2:1);
    static size_t scr1_size = (n_embd>2400?512u:256u)*1024*1024;


    static void * scr0 = malloc(scr0_size);
    static void * scr1 = malloc(scr1_size);

    if (mem_per_token > 0 && (mem_per_token*N*2 + 64u*1024*1024) > buf_size) {
        const size_t buf_size_new = 320u*1024*1024 + 1.2*(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
        if (buf_size_new > buf_size)
        {
            buf_size = buf_size_new;
            buf = realloc(buf, buf_size);
            if (buf == nullptr)
            {
                fprintf(stderr, "%s: failed to allocate %zu bytes. Try reducing batch size.\n", __func__, buf_size);
                return false;
            }
        }
    }

    struct ggml_v3_init_params params;
    params.mem_size   = buf_size;
    params.mem_buffer = buf;
    params.no_alloc   = false;


    struct ggml_v3_context * ctx0 = ggml_v3_init(params);
    struct ggml_v3_cgraph * gf = ggml_v3_new_graph_custom(ctx0, 8192, false);

    struct ggml_v3_tensor * embd = ggml_v3_new_tensor_1d(ctx0, GGML_V3_TYPE_I32, N);
    memcpy(embd->data, embd_inp.data(), N*ggml_v3_element_size(embd));

    struct ggml_v3_tensor * position = ggml_v3_new_tensor_1d(ctx0, GGML_V3_TYPE_I32, N);
    for (int i = 0; i < N; ++i) {
        ((int32_t *) position->data)[i] = n_past + i;
    }

    // wte + wpe
    struct ggml_v3_tensor * inpL =
        ggml_v3_add(ctx0,
                ggml_v3_get_rows(ctx0, model.wte, embd),
                ggml_v3_get_rows(ctx0, model.wpe, position));

    for (int il = 0; il < n_layer; ++il) {
        struct ggml_v3_tensor * cur;

        if(use_scratch){
        ggml_v3_set_scratch(ctx0, { 0, scr0_size, scr0, });
        }

        // norm
        {
            // [ 768, N]
            cur = ggml_v3_norm(ctx0, inpL, default_norm_eps);

            // cur = ln_1_g*cur + ln_1_b
            // [ 768, N]
            cur = ggml_v3_add(ctx0,
                    ggml_v3_mul(ctx0,
                        ggml_v3_repeat(ctx0, model.layers[il].ln_1_g, cur),
                        cur),
                    ggml_v3_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_v3_mul_mat(ctx0,
                    model.layers[il].c_attn_attn_w,
                    cur);

            cur = ggml_v3_add(ctx0,
                    ggml_v3_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
                    cur);
        }

        // self-attention
        {
            struct ggml_v3_tensor * Qcur = ggml_v3_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
            struct ggml_v3_tensor * Kcur = ggml_v3_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
            struct ggml_v3_tensor * Vcur = ggml_v3_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_v3_tensor * k = ggml_v3_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_v3_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
                struct ggml_v3_tensor * v = ggml_v3_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_v3_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));

                ggml_v3_build_forward_expand(gf, ggml_v3_cpy(ctx0, Kcur, k));
                ggml_v3_build_forward_expand(gf, ggml_v3_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_v3_tensor * Q =
                ggml_v3_permute(ctx0,
                        ggml_v3_cpy(ctx0,
                            Qcur,
                            ggml_v3_new_tensor_3d(ctx0, GGML_V3_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_v3_tensor * K =
                ggml_v3_permute(ctx0,
                        ggml_v3_reshape_3d(ctx0,
                            ggml_v3_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_v3_element_size(model.memory_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_v3_tensor * V =
            //    ggml_v3_cpy(ctx0,
            //            ggml_v3_permute(ctx0,
            //                ggml_v3_reshape_3d(ctx0,
            //                    ggml_v3_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v3_element_size(model.memory_v)*n_embd),
            //                    n_embd/n_head, n_head, n_past + N),
            //                1, 2, 0, 3),
            //            ggml_v3_new_tensor_3d(ctx0, GGML_V3_TYPE_F32, n_past + N, n_embd/n_head, n_head));

            //struct ggml_v3_tensor * KQV = ggml_v3_flash_attn(ctx0, Q, K, V, true);

            // K * Q
            // [n_past + N, N, 12]
            struct ggml_v3_tensor * KQ = ggml_v3_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_v3_tensor * KQ_scaled =
                ggml_v3_scale_inplace(ctx0,
                        KQ,
                        1.0f/sqrt(float(n_embd)/n_head)
                        );

            // KQ_masked = mask_past(KQ_scaled)
            // [n_past + N, N, 12]
            struct ggml_v3_tensor * KQ_masked = ggml_v3_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);

            // KQ = soft_max(KQ_masked)
            // [n_past + N, N, 12]
            struct ggml_v3_tensor * KQ_soft_max = ggml_v3_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_v3_tensor * V_trans =
                ggml_v3_cpy(ctx0,
                        ggml_v3_permute(ctx0,
                            ggml_v3_reshape_3d(ctx0,
                                ggml_v3_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v3_element_size(model.memory_v)*n_embd),
                                n_embd/n_head, n_head, n_past + N),
                            1, 2, 0, 3),
                        ggml_v3_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));

            // KQV = transpose(V) * KQ_soft_max
            // [64, N, 12]
            struct ggml_v3_tensor * KQV = ggml_v3_mul_mat(ctx0, V_trans, KQ_soft_max);

            // KQV_merged = KQV.permute(0, 2, 1, 3)
            // [64, 12, N]
            struct ggml_v3_tensor * KQV_merged = ggml_v3_permute(ctx0, KQV, 0, 2, 1, 3);

            // cur = KQV_merged.contiguous().view(n_embd, N)
            // [768, N]
            cur = ggml_v3_cpy(ctx0,
                    KQV_merged,
                    ggml_v3_new_tensor_2d(ctx0, GGML_V3_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_v3_mul_mat(ctx0,
                    model.layers[il].c_attn_proj_w,
                    cur);

            cur = ggml_v3_add(ctx0,
                    ggml_v3_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
                    cur);
        }

        // add the input
        cur = ggml_v3_add(ctx0, cur, inpL);

        struct ggml_v3_tensor * inpFF = cur;

        if(use_scratch){
        ggml_v3_set_scratch(ctx0, { 0, scr1_size, scr1, });
        }

        // feed-forward network
        {
            // norm
            {
                cur = ggml_v3_norm(ctx0, inpFF, default_norm_eps);

                // cur = ln_2_g*cur + ln_2_b
                // [ 768, N]
                cur = ggml_v3_add(ctx0,
                        ggml_v3_mul(ctx0,
                            ggml_v3_repeat(ctx0, model.layers[il].ln_2_g, cur),
                            cur),
                        ggml_v3_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_v3_mul_mat(ctx0,
                    model.layers[il].c_mlp_fc_w,
                    cur);

            cur = ggml_v3_add(ctx0,
                    ggml_v3_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
                    cur);

            // GELU activation
            // [3072, N]
            cur = ggml_v3_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_v3_mul_mat(ctx0,
                    model.layers[il].c_mlp_proj_w,
                    cur);

            cur = ggml_v3_add(ctx0,
                    ggml_v3_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
                    cur);
        }

        // input for next layer
        inpL = ggml_v3_add(ctx0, cur, inpFF);
    }

    if(use_scratch){
    ggml_v3_set_scratch(ctx0, { 0, scr0_size, scr0, });
    }

    // norm
    {
        // [ 768, N]
        inpL = ggml_v3_norm(ctx0, inpL, default_norm_eps);

        // inpL = ln_f_g*inpL + ln_f_b
        // [ 768, N]
        inpL = ggml_v3_add(ctx0,
                ggml_v3_mul(ctx0,
                    ggml_v3_repeat(ctx0, model.ln_f_g, inpL),
                    inpL),
                ggml_v3_repeat(ctx0, model.ln_f_b, inpL));
    }

    if(use_scratch){
    ggml_v3_set_scratch(ctx0, { 0, 0, nullptr, });
    }

    // inpL = WTE * inpL
    // [ 768, 50257] - model.lm_head
    // [ 768, N]     - inpL
    inpL = ggml_v3_mul_mat(ctx0, model.lm_head, inpL);

    // logits -> probs
    //inpL = ggml_v3_soft_max_inplace(ctx0, inpL);

    // run the computation
    ggml_v3_build_forward_expand(gf, inpL);
    kcpp_graph_compute_helper(gf, n_threads);

    //if (n_past%100 == 0) {
    //    ggml_v3_graph_print   (&gf);
    //    ggml_v3_graph_dump_dot(&gf, NULL, "gpt-2.dot");
    //}

    //embd_w.resize(n_vocab*N);
    //memcpy(embd_w.data(), ggml_v3_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_v3_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);

    if (mem_per_token == 0) {
        mem_per_token = ggml_v3_used_mem(ctx0)/N;
    }
    //printf("used_mem = %zu MB\n", ggml_v3_used_mem(ctx0)/(1024*1024));

    ggml_v3_free(ctx0);

    return true;
}