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794 lines
27 KiB
794 lines
27 KiB
#include "ggml/ggml.h"
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#include "utils.h"
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <vector>
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// default hparams (GPT-2 117M)
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struct gpt2_hparams {
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int32_t n_vocab = 50257;
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int32_t n_ctx = 1024;
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int32_t n_embd = 768;
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int32_t n_head = 12;
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int32_t n_layer = 12;
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int32_t f16 = 1;
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};
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struct gpt2_layer {
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// normalization
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struct ggml_tensor * ln_1_g;
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struct ggml_tensor * ln_1_b;
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struct ggml_tensor * ln_2_g;
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struct ggml_tensor * ln_2_b;
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// attention
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struct ggml_tensor * c_attn_attn_w;
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struct ggml_tensor * c_attn_attn_b;
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struct ggml_tensor * c_attn_proj_w;
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struct ggml_tensor * c_attn_proj_b;
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// mlp
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struct ggml_tensor * c_mlp_fc_w;
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struct ggml_tensor * c_mlp_fc_b;
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struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
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struct ggml_tensor * c_mlp_proj_b;
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};
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struct gpt2_model {
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gpt2_hparams hparams;
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// normalization
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struct ggml_tensor * ln_f_g;
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struct ggml_tensor * ln_f_b;
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struct ggml_tensor * wte; // position embedding
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struct ggml_tensor * wpe; // token embedding
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std::vector<gpt2_layer> layers;
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// key + value memory
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struct ggml_tensor * memory_k;
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struct ggml_tensor * memory_v;
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//
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struct ggml_context * ctx;
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std::map<std::string, struct ggml_tensor *> tensors;
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};
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// load the model's weights from a file
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bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
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printf("%s: loading model from '%s'\n", __func__, fname.c_str());
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin) {
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
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return false;
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}
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// verify magic
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{
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uint32_t magic;
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fin.read((char *) &magic, sizeof(magic));
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if (magic != 0x67676d6c) {
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
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return false;
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}
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}
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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.f16, sizeof(hparams.f16));
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: f16 = %d\n", __func__, hparams.f16);
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}
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// load vocab
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{
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int32_t n_vocab = 0;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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if (n_vocab != model.hparams.n_vocab) {
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fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
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__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
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return false;
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}
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std::string word;
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for (int i = 0; i < n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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word.resize(len);
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fin.read((char *) word.data(), len);
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats
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// in order to save memory and also to speed up the computation
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const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_g
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ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
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ctx_size += n_vocab*n_embd*ggml_type_size(wtype); // wte
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ctx_size += n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
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ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype)); // c_attn_attn_w
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ctx_size += n_layer*( 3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype)); // c_attn_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_proj_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype)); // c_mlp_fc_w
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ctx_size += n_layer*( 4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype)); // c_mlp_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
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ctx_size += (6 + 12*n_layer)*256; // object overhead
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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// create the ggml context
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{
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struct ggml_init_params params = {
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.mem_size = ctx_size,
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.mem_buffer = NULL,
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};
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model.ctx = ggml_init(params);
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if (!model.ctx) {
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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return false;
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}
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}
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// prepare memory for the weights
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
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// map by name
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model.tensors["model/ln_f/g"] = model.ln_f_g;
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model.tensors["model/ln_f/b"] = model.ln_f_b;
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model.tensors["model/wte"] = model.wte;
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model.tensors["model/wpe"] = model.wpe;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, 3*n_embd, n_embd);
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layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
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layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
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layer.c_mlp_proj_w_trans = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
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model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
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model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
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model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w_trans;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
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}
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}
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// key + value memory
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_mem = n_layer*n_ctx;
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const int n_elements = n_embd*n_mem;
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model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
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const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
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printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
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}
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// load weights
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{
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size_t total_size = 0;
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while (true) {
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int32_t n_dims;
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int32_t length;
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int32_t ftype;
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fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
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fin.read(reinterpret_cast<char *>(&length), sizeof(length));
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fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
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if (fin.eof()) {
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break;
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}
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int32_t nelements = 1;
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int32_t ne[2] = { 1, 1 };
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for (int i = 0; i < n_dims; ++i) {
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fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
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nelements *= ne[i];
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}
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std::string name(length, 0);
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fin.read(&name[0], length);
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if (model.tensors.find(name.data()) == model.tensors.end()) {
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fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
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return false;
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}
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auto tensor = model.tensors[name.data()];
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if (ggml_nelements(tensor) != nelements) {
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fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
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return false;
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}
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if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
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fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
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__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
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return false;
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}
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const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
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if (nelements*bpe != ggml_nbytes(tensor)) {
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fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
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__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
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return false;
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}
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fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
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//printf("%24s - [%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);
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total_size += ggml_nbytes(tensor);
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}
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printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
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}
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fin.close();
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return true;
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}
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// evaluate the transformer
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//
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// - model: the model
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// - n_threads: number of threads to use
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// - n_past: the context size so far
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// - embd_inp: the embeddings of the tokens in the context
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// - embd_w: the predicted logits for the next token
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//
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bool gpt2_eval(
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const gpt2_model & model,
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const int n_threads,
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const int n_past,
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const std::vector<gpt_vocab::id> & embd_inp,
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std::vector<float> & embd_w,
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size_t & mem_per_token) {
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const int N = embd_inp.size();
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_head = hparams.n_head;
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const int n_vocab = hparams.n_vocab;
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static size_t buf_size = 256u*1024*1024;
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static void * buf = malloc(buf_size);
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if (mem_per_token > 0 && mem_per_token*N > buf_size) {
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const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
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//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
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// reallocate
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buf_size = buf_size_new;
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buf = realloc(buf, buf_size);
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if (buf == nullptr) {
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fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
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return false;
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}
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}
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struct ggml_init_params params = {
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.mem_size = buf_size,
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.mem_buffer = buf,
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = { .n_threads = n_threads };
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
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struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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for (int i = 0; i < N; ++i) {
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((int32_t *) position->data)[i] = n_past + i;
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}
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// wte + wpe
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struct ggml_tensor * inpL =
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ggml_add(ctx0,
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ggml_get_rows(ctx0, model.wte, embd),
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ggml_get_rows(ctx0, model.wpe, position));
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * cur;
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// norm
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{
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// [ 768, N]
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cur = ggml_norm(ctx0, inpL);
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// cur = ln_1_g*cur + ln_1_b
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// [ 768, N]
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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,
|
|
ggml_transpose(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(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(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
// [n_past + N, N, 12]
|
|
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()
|
|
// [n_past + N, 64, 12]
|
|
struct ggml_tensor * V_trans =
|
|
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);
|
|
|
|
// 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,
|
|
ggml_transpose(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,
|
|
ggml_transpose(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_trans,
|
|
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.wte
|
|
// [ 768, N] - inpL
|
|
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 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;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
|
|
gpt_params params;
|
|
params.model = "models/gpt-2-117M/ggml-model.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()) {
|
|
params.prompt = gpt_random_prompt(rng);
|
|
}
|
|
|
|
int64_t t_load_us = 0;
|
|
|
|
gpt_vocab vocab;
|
|
gpt2_model model;
|
|
|
|
// load the model
|
|
{
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!gpt2_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: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
|
|
printf("\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;
|
|
gpt2_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 (!gpt2_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);
|
|
|
|
// end of text token
|
|
if (embd.back() == 50256) {
|
|
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;
|
|
}
|