int32_t n_vocab = 49280;

int32_t n_ctx = 2048;

int32_t n_embd = 2048;

int32_t n_head = 16;

int32_t n_layer = 24;

int32_t ftype = 1;

// 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;

starcoder_hparams hparams;

// normalization

struct ggml_tensor * ln_f_g;

struct ggml_tensor * ln_f_b;

struct ggml_tensor * wte; // position embedding

struct ggml_tensor * wpe; // token embedding

struct ggml_tensor * lm_head; // language model head

std::vector<starcoder_layer> layers;

// key + value memory

struct ggml_tensor * memory_k;

struct ggml_tensor * memory_v;

//

struct ggml_context * ctx;

std::map<std::string, struct ggml_tensor *> tensors;

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 != 0x67676d6c) {

fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());

return false;

}

}

// load hparams

{

auto & hparams = model.hparams;

fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));

fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));

fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));

fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));

fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));

fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));

const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;

printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);

printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);

printf("%s: n_embd = %d\n", __func__, hparams.n_embd);

printf("%s: n_head = %d\n", __func__, hparams.n_head);

printf("%s: n_layer = %d\n", __func__, hparams.n_layer);

printf("%s: ftype = %d\n", __func__, hparams.ftype);

printf("%s: qntvr = %d\n", __func__, qntvr);

hparams.ftype %= GGML_QNT_VERSION_FACTOR;

}

// load vocab

{

int32_t n_vocab = 0;

fin.read((char *) &n_vocab, sizeof(n_vocab));

if (n_vocab != model.hparams.n_vocab) {

fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",

__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);

return false;

}

std::string word;

std::vector<char> buf(128);

for (int i = 0; i < n_vocab; i++) {

uint32_t len;

fin.read((char *) &len, sizeof(len));

buf.resize(len);

fin.read((char *) buf.data(), len);

word.assign(buf.data(), len);

vocab.token_to_id[word] = i;

vocab.id_to_token[i] = word;

// if (i < 10) fprintf(stderr, "%.s: vocab[%d] = '%s'\n", __func__, i, word.c_str());

}

}

// for the big tensors, we have the option to store the data in 16-bit floats or quantized

// in order to save memory and also to speed up the computation

ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));

if (wtype == GGML_TYPE_COUNT) {

fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",

__func__, fname.c_str(), model.hparams.ftype);

return false;

}

auto & ctx = model.ctx;

size_t ctx_size = 0;

{

const auto & hparams = model.hparams;

const int n_embd = hparams.n_embd;

const int n_layer = hparams.n_layer;

const int n_ctx = hparams.n_ctx;

const int n_vocab = hparams.n_vocab;

const int head_dim = n_embd / hparams.n_head;

const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head

const int kv_dim = kv_heads * head_dim;

ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g

ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b

ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // wte

ctx_size += n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe

ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // lm_head

ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g

ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b

ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g

ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b

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

ctx_size += n_layer*( (n_embd + 2*kv_dim)*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b

ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w

ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b

ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w

ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b

ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w

ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b

ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k

ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v

ctx_size += (6 + 12*n_layer)*512; // object overhead

printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));

}

// 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_ctx = hparams.n_ctx;

const int n_vocab = hparams.n_vocab;

const int head_dim = n_embd / hparams.n_head;

const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head

const int kv_dim = kv_heads * head_dim;

model.layers.resize(n_layer);

model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);

model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

// map by name

model.tensors["model/ln_f/g"] = model.ln_f_g;

model.tensors["model/ln_f/b"] = model.ln_f_b;

model.tensors["model/wte"] = model.wte;

model.tensors["model/wpe"] = model.wpe;

model.tensors["model/lm_head"] = model.lm_head;

for (int i = 0; i < n_layer; ++i) {

auto & layer = model.layers[i];

layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd + 2*kv_dim);

layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd + 2*kv_dim);

layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); //TODO: 4*n_embd = config.n_inner

layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);

layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);

layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

// map by name

model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;

model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;

model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;

model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;

model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;

model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;

model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;

model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;

model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;

model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;

model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;

model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;

}

}

// key + value memory

{

const auto & hparams = model.hparams;

const int n_embd = hparams.n_embd;

const int n_layer = hparams.n_layer;

const int n_ctx = hparams.n_ctx;

const int n_mem = n_layer*n_ctx;

const int n_elements = n_embd*n_mem;

model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 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 = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);

}

// load weights

{

size_t total_size = 0;

bool has_lm_head = false;

while (true) {

int32_t n_dims;

int32_t length;

int32_t ttype;

fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));

fin.read(reinterpret_cast<char *>(&length), sizeof(length));

fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));

if (fin.eof()) {

break;

}

int32_t nelements = 1;

int32_t ne[2] = { 1, 1 };

for (int i = 0; i < n_dims; ++i) {

fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));

nelements *= ne[i];

}

std::string name(length, 0);

fin.read(&name[0], length);

if (model.tensors.find(name.data()) == model.tensors.end()) {

fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());

return false;

}

auto tensor = model.tensors[name.data()];

if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {

fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",

__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);

return false;

}

if (ggml_nelements(tensor) != nelements) {

fprintf(stderr, "%s: tensor '%s' has wrong size in model file. got %d, expected %d\n",

__func__, name.data(), (int) ggml_nelements(tensor), nelements);

return false;

}

// for debugging

if (0) {

printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));

}

const size_t bpe = ggml_type_size(ggml_type(ttype));

if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {

fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",

__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);

return false;

}

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

}

if (name == "model/lm_head") {

has_lm_head = true;

}

total_size += ggml_nbytes(tensor);

}

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

}

fin.close();

return true;

const starcoder_model & model,

const int n_threads,

const int n_past,

const std::vector<gpt_vocab::id> & embd_inp,

std::vector<float> & embd_w,

size_t & mem_per_token) {

const int N = embd_inp.size();

const auto & hparams = model.hparams;

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 = 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 = {};

gf.n_threads = n_threads;

struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);

memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));

struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);

for (int i = 0; i < N; ++i) {

((int32_t *) position->data)[i] = n_past + i;

}

// wte + wpe

struct ggml_tensor * inpL =

ggml_add(ctx0,

ggml_get_rows(ctx0, model.wte, embd),

ggml_get_rows(ctx0, model.wpe, position));

for (int il = 0; il < n_layer; ++il) {

struct ggml_tensor * cur;

ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });

// norm

{

// [ 768, N]

cur = ggml_norm(ctx0, inpL);

// cur = ln_1_g*cur + ln_1_b

// [ 768, N]

cur = ggml_add(ctx0,

ggml_mul(ctx0,

ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),

cur),

ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));

}

// attn

// [2304, 768] - model.layers[il].c_attn_attn_w

// [2304, 1] - model.layers[il].c_attn_attn_b

// [ 768, N] - cur (in)

// [2304, N] - cur (out)

//

// cur = attn_w*cur + attn_b

// [2304, N]

{

cur = ggml_mul_mat(ctx0,

model.layers[il].c_attn_attn_w,

cur);

cur = ggml_add(ctx0,

ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),

cur);

}

// self-attention

{

struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);

struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);

struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);

// store key and value to memory

if (N >= 1) {

struct ggml_tensor * k = ggml_view_1d(ctx0, 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_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));

ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));

ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));

}

// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)

// [64, N, 12]

struct ggml_tensor * Q =

ggml_permute(ctx0,

ggml_cpy(ctx0,

Qcur,

ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),

0, 2, 1, 3);

// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)

// [64, n_past + N, 12]

struct ggml_tensor * K =

ggml_permute(ctx0,

ggml_reshape_3d(ctx0,

ggml_view_1d(ctx0, 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); //TODO: need to be tiled

// GG: flash attention

//struct ggml_tensor * V =

// ggml_cpy(ctx0,

// ggml_permute(ctx0,

// ggml_reshape_3d(ctx0,

// ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),

// n_embd/n_head, n_head, n_past + N),

// 1, 2, 0, 3),

// ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));

//struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);

// K * Q

// [n_past + N, N, 12]

struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); //TODO: check if it broadcasts

// KQ_scaled = KQ / sqrt(n_embd/n_head)

// [n_past + N, N, 12]

struct ggml_tensor * KQ_scaled =

ggml_scale_inplace(ctx0,

KQ,

ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))

);

// KQ_masked = mask_past(KQ_scaled)

// [n_past + N, N, 12]

struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);

// KQ = soft_max(KQ_masked)

// [n_past + N, N, 12]

struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);

// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()

// [n_past + N, 64, 12]

struct ggml_tensor * V_trans =

ggml_cpy(ctx0,

ggml_permute(ctx0,

ggml_reshape_3d(ctx0,

ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),

n_embd/n_head, n_head, n_past + N),

1, 2, 0, 3),

ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));

// KQV = transpose(V) * KQ_soft_max

// [64, N, 12]

struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

// KQV_merged = KQV.permute(0, 2, 1, 3)

// [64, 12, N]

struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

// cur = KQV_merged.contiguous().view(n_embd, N)

// [768, N]

cur = ggml_cpy(ctx0,

KQV_merged,

ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));

}

// projection

// [ 768, 768] - model.layers[il].c_attn_proj_w

// [ 768, 1] - model.layers[il].c_attn_proj_b

// [ 768, N] - cur (in)

// [ 768, N] - cur (out)

//

// cur = proj_w*cur + proj_b

// [768, N]

{

cur = ggml_mul_mat(ctx0,

model.layers[il].c_attn_proj_w,

cur);

cur = ggml_add(ctx0,

ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),

cur);

}

// add the input

cur = ggml_add(ctx0, cur, inpL);

struct ggml_tensor * inpFF = cur;

ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });

// feed-forward network

{

// norm

{

cur = ggml_norm(ctx0, inpFF);

// cur = ln_2_g*cur + ln_2_b

// [ 768, N]

cur = ggml_add(ctx0,

ggml_mul(ctx0,

ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),

cur),

ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));

}

// fully connected

// [3072, 768] - model.layers[il].c_mlp_fc_w

// [3072, 1] - model.layers[il].c_mlp_fc_b

// [ 768, N] - cur (in)

// [3072, N] - cur (out)

//

// cur = fc_w*cur + fc_b

// [3072, N]

cur = ggml_mul_mat(ctx0,

model.layers[il].c_mlp_fc_w,

cur);

cur = ggml_add(ctx0,

ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),

cur);

// GELU activation

// [3072, N]

cur = ggml_gelu(ctx0, cur);

// projection

// [ 768, 3072] - model.layers[il].c_mlp_proj_w

// [ 768, 1] - model.layers[il].c_mlp_proj_b

// [3072, N] - cur (in)

// [ 768, N] - cur (out)

//

// cur = proj_w*cur + proj_b

// [768, N]

cur = ggml_mul_mat(ctx0,

model.layers[il].c_mlp_proj_w,

cur);

cur = ggml_add(ctx0,

ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),

cur);

}

// input for next layer

inpL = ggml_add(ctx0, cur, inpFF);

}

ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });

// norm

{

// [ 768, N]

inpL = ggml_norm(ctx0, inpL);

// inpL = ln_f_g*inpL + ln_f_b

// [ 768, N]

inpL = ggml_add(ctx0,

ggml_mul(ctx0,

ggml_repeat(ctx0, model.ln_f_g, inpL),

inpL),

ggml_repeat(ctx0, model.ln_f_b, inpL));

}

ggml_set_scratch(ctx0, { 0, 0, nullptr, });

// inpL = WTE * inpL

// [ 768, 50257] - model.lm_head

// [ 768, N] - inpL

inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);

// logits -> probs

//inpL = ggml_soft_max_inplace(ctx0, inpL);

// run the computation

ggml_build_forward_expand(&gf, inpL);

ggml_graph_compute (ctx0, &gf);

//if (n_past%100 == 0) {

// ggml_graph_print (&gf);

// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");

//}

//embd_w.resize(n_vocab*N);

//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);

// return result just for the last token

embd_w.resize(n_vocab);

memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);

if (mem_per_token == 0) {

mem_per_token = ggml_used_mem(ctx0)/N;

}

//printf("used_mem = %zu MB\n", ggml_used_mem(ctx0)/(1024*1024));

ggml_free(ctx0);

return true;

ggml_time_init();

const int64_t t_main_start_us = ggml_time_us();

gpt_params params;

params.model = "models/bigcode/gpt_bigcode-santacoder-ggml.bin";

if (gpt_params_parse(argc, argv, params) == false) {

return 1;

}

if (params.seed < 0) {

params.seed = time(NULL);

}

printf("%s: seed = %d\n", __func__, params.seed);

std::mt19937 rng(params.seed);

if (params.prompt.empty()) {

if( !isatty(STDIN_FILENO) ){

std::string line;

while( std::getline(std::cin, line) ){

params.prompt = params.prompt + "\n" + line;

}

} else {

params.prompt = gpt_random_prompt(rng);

}

}

int64_t t_load_us = 0;

gpt_vocab vocab;

starcoder_model model;

// load the model

{

const int64_t t_start_us = ggml_time_us();

if (!starcoder_model_load(params.model, model, vocab)) {

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;

}

int n_past = 0;

int64_t t_sample_us = 0;

int64_t t_predict_us = 0;

std::vector<float> logits;

// tokenize the prompt

std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);

params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());

printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());

printf("%s: number of tokens in prompt = %zu, 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");

// submit the input prompt token-by-token

// this reduces the memory usage during inference, at the cost of a bit of speed at the beginning

std::vector<gpt_vocab::id> embd;

// determine the required inference memory per token:

size_t mem_per_token = 0;

starcoder_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);

for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {

// predict

if (embd.size() > 0) {

const int64_t t_start_us = ggml_time_us();

if (!starcoder_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {

printf("Failed to predict\n");

return 1;

}

t_predict_us += ggml_time_us() - t_start_us;

}

n_past += embd.size();

embd.clear();

if (i >= embd_inp.size()) {

// sample next token

const int top_k = params.top_k;

const float top_p = params.top_p;

const float temp = params.temp;

const int n_vocab = model.hparams.n_vocab;

gpt_vocab::id id = 0;

{

const int64_t t_start_sample_us = ggml_time_us();

id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);

t_sample_us += ggml_time_us() - t_start_sample_us;

}

// add it to the context

embd.push_back(id);

} else {

// if here, it means we are still processing the input prompt

for (int k = i; k < embd_inp.size(); k++) {

embd.push_back(embd_inp[k]);

if (embd.size() >= params.n_batch) {

break;

}

}

i += embd.size() - 1;

}

// display text

for (auto id : embd) {

printf("%s", vocab.id_to_token[id].c_str());

}

fflush(stdout);

// check if model is santacoder

if (model.hparams.n_layer <= 30 && embd.back() == 49152) {

break;

}

// check if model is starcoder

else if (embd.back() == 0) { //TODO: this is only for starcoder

break;

}

}

// report timing

{

const int64_t t_main_end_us = ggml_time_us();

printf("\n\n");

printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);

printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);

printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);

printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);

printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);

}

ggml_free(model.ctx);

return 0;