|
|
|
@ -20,17 +20,34 @@ static const size_t MB = 1024*1024;
|
|
|
|
|
|
|
|
|
|
struct mpt_hparams {
|
|
|
|
|
// FIXME: for mpt
|
|
|
|
|
int32_t n_vocab = 50400;
|
|
|
|
|
int32_t n_vocab = 50432;
|
|
|
|
|
int32_t n_ctx = 2048;
|
|
|
|
|
int32_t n_embd = 4096;
|
|
|
|
|
int32_t n_head = 16;
|
|
|
|
|
int32_t n_layer = 28;
|
|
|
|
|
int32_t n_head = 32;
|
|
|
|
|
int32_t n_layer = 32;
|
|
|
|
|
// this isn't used should we remove?
|
|
|
|
|
int32_t n_rot = 64;
|
|
|
|
|
int32_t f16 = 1;
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
struct mpt_layer {
|
|
|
|
|
// FIXME
|
|
|
|
|
// normalization
|
|
|
|
|
struct ggml_tensor * ln_1_g;
|
|
|
|
|
struct ggml_tensor * ln_1_b;
|
|
|
|
|
|
|
|
|
|
// attention
|
|
|
|
|
struct ggml_tensor * c_attn_q_proj_w;
|
|
|
|
|
struct ggml_tensor * c_attn_k_proj_w;
|
|
|
|
|
struct ggml_tensor * c_attn_v_proj_w;
|
|
|
|
|
|
|
|
|
|
struct ggml_tensor * c_attn_proj_w;
|
|
|
|
|
|
|
|
|
|
// 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 mpt_buffer {
|
|
|
|
@ -70,11 +87,20 @@ struct mpt_kv_cache {
|
|
|
|
|
struct mpt_model {
|
|
|
|
|
mpt_hparams hparams;
|
|
|
|
|
|
|
|
|
|
// normalization
|
|
|
|
|
struct ggml_tensor * ln_f_g;
|
|
|
|
|
struct ggml_tensor * ln_f_b;
|
|
|
|
|
|
|
|
|
|
struct ggml_tensor * wte; // position embedding
|
|
|
|
|
|
|
|
|
|
// mpt does weight tying
|
|
|
|
|
|
|
|
|
|
std::vector<mpt_layer> layers;
|
|
|
|
|
|
|
|
|
|
struct mpt_kv_cache kv_self;
|
|
|
|
|
struct ggml_context * ctx;
|
|
|
|
|
std::map<std::string, struct ggml_tensor *> tensors;
|
|
|
|
|
|
|
|
|
|
// FIXME
|
|
|
|
|
|
|
|
|
|
mpt_buffer buf;
|
|
|
|
|
|
|
|
|
@ -117,15 +143,320 @@ static bool kv_cache_init(
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
struct mpt_vocab {
|
|
|
|
|
// FIXME
|
|
|
|
|
using id = int32_t;
|
|
|
|
|
using token = std::string;
|
|
|
|
|
|
|
|
|
|
std::map<token, id> token_to_id;
|
|
|
|
|
std::map<id, token> id_to_token;
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
// load the model's weights from a stream
|
|
|
|
|
bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & model, mpt_vocab & vocab) {
|
|
|
|
|
// FIXME
|
|
|
|
|
return false;
|
|
|
|
|
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
|
|
|
|
|
|
|
|
|
|
// verify magic
|
|
|
|
|
// TODO: Do we really need this?
|
|
|
|
|
// {
|
|
|
|
|
// 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.n_rot, sizeof(hparams.n_rot));
|
|
|
|
|
fin.read((char *) &hparams.f16, sizeof(hparams.f16));
|
|
|
|
|
|
|
|
|
|
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: f16 = %d\n", __func__, hparams.f16);
|
|
|
|
|
}
|
|
|
|
|
// 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;
|
|
|
|
|
for (int i = 0; i < n_vocab; i++) {
|
|
|
|
|
uint32_t len;
|
|
|
|
|
fin.read((char *) &len, sizeof(len));
|
|
|
|
|
|
|
|
|
|
word.resize(len);
|
|
|
|
|
fin.read((char *) word.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_TYPE_COUNT;
|
|
|
|
|
switch (model.hparams.f16) {
|
|
|
|
|
case 0: wtype = GGML_TYPE_F32; break;
|
|
|
|
|
case 1: wtype = GGML_TYPE_F16; break;
|
|
|
|
|
case 2: wtype = GGML_TYPE_Q4_0; break;
|
|
|
|
|
case 3: wtype = GGML_TYPE_Q4_1; break;
|
|
|
|
|
default:
|
|
|
|
|
{
|
|
|
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
|
|
|
|
|
__func__, fname.c_str(), model.hparams.f16);
|
|
|
|
|
return false;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
const ggml_type wtype2 = GGML_TYPE_F32;
|
|
|
|
|
|
|
|
|
|
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_embd*n_vocab*ggml_type_sizef(wtype); // wte
|
|
|
|
|
|
|
|
|
|
// with weight tying i don't think we need this
|
|
|
|
|
// 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*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
|
|
|
|
|
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
|
|
|
|
|
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w
|
|
|
|
|
|
|
|
|
|
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
// TODO: what is this??
|
|
|
|
|
ctx_size += (5 + 10*n_layer)*256; // 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,
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
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.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);
|
|
|
|
|
|
|
|
|
|
// we don't need this because of weight tying
|
|
|
|
|
// 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["transformer.wte.weight"] = model.wte;
|
|
|
|
|
|
|
|
|
|
model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
|
|
|
|
|
model.tensors["transformer.ln_f.bias"] = model.ln_f_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_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
|
|
|
layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
|
|
|
layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
|
|
|
|
|
|
|
|
layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 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["transformer.h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g;
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"] = layer.ln_1_b;
|
|
|
|
|
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
|
|
|
|
|
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
|
|
|
|
|
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"] = layer.c_mlp_fc_w;
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"] = layer.c_mlp_fc_b;
|
|
|
|
|
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
|
|
|
|
|
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.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 int n_mem = n_layer*n_ctx;
|
|
|
|
|
const int n_elements = n_embd*n_mem;
|
|
|
|
|
|
|
|
|
|
if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F32, model.hparams.n_ctx)) {
|
|
|
|
|
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
|
|
|
|
ggml_free(ctx);
|
|
|
|
|
return false;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
|
|
|
|
|
printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
|
|
|
}
|
|
|
|
|
// 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 ftype;
|
|
|
|
|
|
|
|
|
|
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
|
|
|
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
|
|
|
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
|
|
|
|
|
|
|
|
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 [%lu, %lu], expected [%d, %d]\n",
|
|
|
|
|
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
|
|
|
|
return false;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
if (0) {
|
|
|
|
|
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
|
|
|
|
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
size_t bpe = 0;
|
|
|
|
|
|
|
|
|
|
switch (ftype) {
|
|
|
|
|
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
|
|
|
|
|
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
|
|
|
|
|
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
|
|
|
|
|
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
|
|
|
|
|
default:
|
|
|
|
|
{
|
|
|
|
|
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
|
|
|
|
return false;
|
|
|
|
|
}
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
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));
|
|
|
|
|
|
|
|
|
|
//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
|
|
|
|
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);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
return true;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
// load the model's weights from a file path
|
|
|
|
|
bool gptj_model_load(const std::string & fname, mpt_model & model, mpt_vocab & vocab) {
|
|
|
|
|
|
|
|
|
@ -147,8 +478,223 @@ bool mpt_eval(
|
|
|
|
|
const std::vector<int> & embd_inp,
|
|
|
|
|
std::vector<float> & embd_w,
|
|
|
|
|
size_t & mem_per_token) {
|
|
|
|
|
// FIXME
|
|
|
|
|
return false;
|
|
|
|
|
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;
|
|
|
|
|
|
|
|
|
|
const int d_key = n_embd/n_head;
|
|
|
|
|
|
|
|
|
|
static size_t buf_size = 1024u*MB;
|
|
|
|
|
if (!model.buf.addr || model.buf.size < buf_size)
|
|
|
|
|
model.buf.resize(buf_size);
|
|
|
|
|
|
|
|
|
|
if (mem_per_token > 0 && mem_per_token*N > model.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__, model.buf.size, buf_size_new);
|
|
|
|
|
|
|
|
|
|
// reallocate
|
|
|
|
|
model.buf.resize(buf_size_new);
|
|
|
|
|
if (model.buf.addr == nullptr) {
|
|
|
|
|
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.buf.size);
|
|
|
|
|
return false;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
struct ggml_init_params params = {
|
|
|
|
|
.mem_size = model.buf.size,
|
|
|
|
|
.mem_buffer = model.buf.addr,
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
|
struct ggml_cgraph 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));
|
|
|
|
|
|
|
|
|
|
// wte
|
|
|
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
|
|
|
|
|
|
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
|
|
|
|
|
|
// norm
|
|
|
|
|
{
|
|
|
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
|
|
|
|
|
|
// cur = ln_1_g*cur + ln_1_b
|
|
|
|
|
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));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
struct ggml_tensor * inpSA = cur;
|
|
|
|
|
|
|
|
|
|
// self-attention
|
|
|
|
|
{
|
|
|
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur);
|
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur);
|
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur);
|
|
|
|
|
|
|
|
|
|
// store key and value to memory
|
|
|
|
|
if (N >= 1) {
|
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
|
|
|
struct ggml_tensor * v = ggml_view_1d(ctx0, model.kv_self.v, N*n_embd, (ggml_element_size(model.kv_self.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));
|
|
|
|
|
}
|
|
|
|
|
// we need to replace rope with alibi
|
|
|
|
|
|
|
|
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
|
|
|
|
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)
|
|
|
|
|
struct ggml_tensor * K =
|
|
|
|
|
ggml_permute(ctx0,
|
|
|
|
|
ggml_reshape_3d(ctx0,
|
|
|
|
|
ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.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(ctx0,
|
|
|
|
|
KQ,
|
|
|
|
|
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
|
|
|
|
|
);
|
|
|
|
|
|
|
|
|
|
struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, n_past, n_head);
|
|
|
|
|
|
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
|
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
|
|
|
|
|
|
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
|
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(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_trans =
|
|
|
|
|
ggml_cpy(ctx0,
|
|
|
|
|
ggml_permute(ctx0,
|
|
|
|
|
ggml_reshape_3d(ctx0,
|
|
|
|
|
ggml_view_1d(ctx0, model.kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd),
|
|
|
|
|
n_embd/n_head, n_head, n_past + N),
|
|
|
|
|
1, 2, 0, 3),
|
|
|
|
|
ggml_new_tensor_3d(ctx0, model.kv_self.v->type, n_past + N, n_embd/n_head, n_head));
|
|
|
|
|
|
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, 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 (no bias)
|
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
|
|
|
model.layers[il].c_attn_proj_w,
|
|
|
|
|
cur);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
|
|
|
|
|
|
// feed-forward network
|
|
|
|
|
// this is independent of the self-attention result, so it could be done in parallel to the self-attention
|
|
|
|
|
{
|
|
|
|
|
// note here we pass inpSA instead of cur
|
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
|
|
|
model.layers[il].c_mlp_fc_w,
|
|
|
|
|
inpSA);
|
|
|
|
|
|
|
|
|
|
cur = ggml_add(ctx0,
|
|
|
|
|
ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
|
|
|
|
|
cur);
|
|
|
|
|
|
|
|
|
|
// RELU activation
|
|
|
|
|
cur = ggml_relu(ctx0, cur);
|
|
|
|
|
|
|
|
|
|
// projection
|
|
|
|
|
// cur = proj_w*cur + proj_b
|
|
|
|
|
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);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// self-attention + FF
|
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
|
|
|
|
|
|
// input for next layer
|
|
|
|
|
inpL = ggml_add(ctx0, cur, inpL);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// 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));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// lm_head with weight tying
|
|
|
|
|
{
|
|
|
|
|
inpL = ggml_mul_mat(ctx0, model.wte, inpL);
|
|
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// logits -> probs
|
|
|
|
|
//inpL = ggml_soft_max(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 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;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
std::vector<int> mpt_tokenize(const mpt_vocab & vocab, const std::string & text) {
|
|
|
|
@ -161,20 +707,6 @@ const std::string mpt_token_to_str(const mpt_vocab & vocab, int token) {
|
|
|
|
|
return std::string();
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
int mpt_sample_top_k_top_p(
|
|
|
|
|
const mpt_vocab & vocab,
|
|
|
|
|
const int32_t * last_n_tokens_data,
|
|
|
|
|
int last_n_tokens_size,
|
|
|
|
|
const std::vector<float> logits,
|
|
|
|
|
int top_k,
|
|
|
|
|
double top_p,
|
|
|
|
|
double temp,
|
|
|
|
|
float repeat_penalty,
|
|
|
|
|
std::mt19937 & rng)
|
|
|
|
|
{
|
|
|
|
|
// FIXME
|
|
|
|
|
return 0;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
#define MPT_MAX_RNG_STATE 64*1024
|
|
|
|
|
|
|
|
|
@ -237,6 +769,103 @@ size_t mpt_copy_state_data(const mpt_model &model, const std::mt19937 &rng, uint
|
|
|
|
|
return written;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
mpt_vocab::id mpt_sample_top_k_top_p(
|
|
|
|
|
const mpt_vocab & vocab,
|
|
|
|
|
const int32_t * last_n_tokens_data,
|
|
|
|
|
int last_n_tokens_size,
|
|
|
|
|
const std::vector<float> logits,
|
|
|
|
|
int top_k,
|
|
|
|
|
double top_p,
|
|
|
|
|
double temp,
|
|
|
|
|
float repeat_penalty,
|
|
|
|
|
std::mt19937 & rng) {
|
|
|
|
|
int n_logits = vocab.id_to_token.size();
|
|
|
|
|
|
|
|
|
|
const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
|
|
|
|
|
const auto * plogits = logits.data() + logits.size() - n_logits;
|
|
|
|
|
|
|
|
|
|
std::vector<std::pair<double, mpt_vocab::id>> logits_id;
|
|
|
|
|
logits_id.reserve(n_logits);
|
|
|
|
|
|
|
|
|
|
{
|
|
|
|
|
const float scale = 1.0f/temp;
|
|
|
|
|
for (int i = 0; i < n_logits; ++i) {
|
|
|
|
|
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
|
|
|
|
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
|
|
|
|
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
|
|
|
|
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
|
|
|
|
if (plogits[i] < 0.0f) {
|
|
|
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
|
|
|
|
} else {
|
|
|
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
|
|
|
|
}
|
|
|
|
|
} else {
|
|
|
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// find the top K tokens
|
|
|
|
|
std::partial_sort(
|
|
|
|
|
logits_id.begin(),
|
|
|
|
|
logits_id.begin() + top_k, logits_id.end(),
|
|
|
|
|
[](const std::pair<double, mpt_vocab::id> & a, const std::pair<double, mpt_vocab::id> & b) {
|
|
|
|
|
return a.first > b.first;
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
logits_id.resize(top_k);
|
|
|
|
|
|
|
|
|
|
double maxl = -INFINITY;
|
|
|
|
|
for (const auto & kv : logits_id) {
|
|
|
|
|
maxl = std::max(maxl, kv.first);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// compute probs for the top K tokens
|
|
|
|
|
std::vector<double> probs;
|
|
|
|
|
probs.reserve(logits_id.size());
|
|
|
|
|
|
|
|
|
|
double sum = 0.0;
|
|
|
|
|
for (const auto & kv : logits_id) {
|
|
|
|
|
double p = exp(kv.first - maxl);
|
|
|
|
|
probs.push_back(p);
|
|
|
|
|
sum += p;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// normalize the probs
|
|
|
|
|
for (auto & p : probs) {
|
|
|
|
|
p /= sum;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
if (top_p < 1.0f) {
|
|
|
|
|
double cumsum = 0.0f;
|
|
|
|
|
for (int i = 0; i < top_k; i++) {
|
|
|
|
|
cumsum += probs[i];
|
|
|
|
|
if (cumsum >= top_p) {
|
|
|
|
|
top_k = i + 1;
|
|
|
|
|
probs.resize(top_k);
|
|
|
|
|
logits_id.resize(top_k);
|
|
|
|
|
break;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
cumsum = 1.0/cumsum;
|
|
|
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
|
|
|
probs[i] *= cumsum;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//printf("\n");
|
|
|
|
|
//for (int i = 0; i < (int) probs.size(); i++) {
|
|
|
|
|
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
|
|
|
|
|
//}
|
|
|
|
|
//exit(0);
|
|
|
|
|
|
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
|
|
|
int idx = dist(rng);
|
|
|
|
|
|
|
|
|
|
return logits_id[idx].second;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src)
|
|
|
|
|
{
|
|
|
|
|
const uint8_t * in = src;
|
|
|
|
|