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// https://github.com/ggerganov/ggml/blob/master/examples/gpt-2/main.cpp | |
// 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; | |
}; | |
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_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; | |
// 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; | |
}; | |
// load the model's weights from a file | |
bool gpt2_model_load(const std::string &fname, gpt2_model &model, | |
gpt_vocab &vocab) { | |
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; | |
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 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; | |
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 * (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 * 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 | |
} | |
// 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; | |
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.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); | |
} | |
// 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 (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 [%d, %d], " | |
"expected [%d, %d]\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)); | |
// 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); | |
} | |
} | |
fin.close(); | |
return true; | |
} | |
// evaluate the transformer | |
// | |
// - model: the model | |
// - n_threads: number of threads to use | |
// - n_past: the context size so far | |
// - embd_inp: the embeddings of the tokens in the context | |
// - embd_w: the predicted logits for the next token | |
// | |
bool 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) { | |
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); | |
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; | |
// 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); | |
// 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); | |
// 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; | |
// 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); | |
} | |
// 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)); | |
} | |
// 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\n", ggml_used_mem(ctx0)); | |
ggml_free(ctx0); | |
return true; | |
} | |
REGISTER_LLM(gpt2); | |