mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-10 17:14:36 +00:00
515 lines
19 KiB
C++
515 lines
19 KiB
C++
#include "ggml.h"
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#include "otherarch.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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#include <iostream>
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#include "model_adapter.h"
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// load the model's weights from a file
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bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vocab) {
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printf("%s: loading model from '%s' - please wait ...\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.d_model, sizeof(hparams.d_model));
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fin.read((char *) &hparams.max_seq_len, sizeof(hparams.max_seq_len));
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fin.read((char *) &hparams.n_heads, sizeof(hparams.n_heads));
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fin.read((char *) &hparams.n_layers, sizeof(hparams.n_layers));
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
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fin.read((char *) &hparams.clip_qkv, sizeof(hparams.clip_qkv));
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fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
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hparams.n_ctx = std::min(hparams.max_seq_len, hparams.n_ctx);
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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printf("%s: d_model = %d\n", __func__, hparams.d_model);
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printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
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printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
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printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
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printf("%s: ftype = %d\n", __func__, hparams.ftype);
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printf("%s: qntvr = %d\n", __func__, qntvr);
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hparams.ftype %= GGML_QNT_VERSION_FACTOR;
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}
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// load vocab
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{
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const int32_t n_vocab = model.hparams.n_vocab;
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std::string word;
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std::vector<char> buf(128);
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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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buf.resize(len);
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fin.read((char *) buf.data(), len);
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word.assign(buf.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
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// floats or quantized in order to save memory and also to speed up the
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// computation
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ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype)(model.hparams.ftype));
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if (wtype == GGML_TYPE_COUNT) {
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fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", __func__, fname.c_str(),
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model.hparams.ftype);
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return false;
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}
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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const auto & hparams = model.hparams;
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const size_t n_ctx = hparams.n_ctx;
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{
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const size_t n_embd = hparams.d_model;
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const size_t n_layer = hparams.n_layers;
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const size_t n_vocab = hparams.n_vocab;
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ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // wte_weight
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ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // norm_f_weight
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ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_1_weight
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ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
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ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // attn_out_proj_weight
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ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_2_weight
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ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
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ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight
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ctx_size += (n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16)); // memory_k
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ctx_size += (n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16)); // memory_v
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ctx_size += (6 + 6 * n_layer) * 512; // 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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params.mem_size = ctx_size;
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params.mem_buffer = NULL;
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params.no_alloc = false;
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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 size_t n_embd = hparams.d_model;
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const size_t n_layer = hparams.n_layers;
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const size_t n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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model.wte_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.norm_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["transformer.wte.weight"] = model.wte_weight;
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model.tensors["transformer.norm_f.weight"] = model.norm_f_weight;
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for (int i = 0; i < (int) n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.norm_1_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_wqkv_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
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layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.norm_2_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ffn_up_proj = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
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layer.ffn_down_proj = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
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// map by name
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.weight"] = layer.norm_1_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.c_attn_wqkv_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_out_proj_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.weight"] = layer.norm_2_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.weight"] = layer.ffn_up_proj;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.weight"] = layer.ffn_down_proj;
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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 size_t n_embd = hparams.d_model;
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const size_t n_layer = hparams.n_layers;
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const int64_t n_mem = n_layer * n_ctx;
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const int64_t n_elements = n_embd * n_mem;
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model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 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 = %" PRId64 "\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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int n_tensors = 0;
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size_t total_size = 0;
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printf("%s: ", __func__);
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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 ttype;
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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 *>(&ttype), sizeof(ttype));
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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,
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"%s: tensor '%s' has wrong shape in model file: got [%5d, "
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"%5d], expected [%5d, %5d]\n",
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__func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
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return false;
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}
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// for debugging
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if (0) {
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printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
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ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
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}
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const size_t bpe = ggml_type_size(ggml_type(ttype));
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if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
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fprintf(stderr,
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"%s: tensor '%s' has wrong size in model file: got %zu, "
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"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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total_size += ggml_nbytes(tensor);
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if (++n_tensors % 8 == 0) {
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printf(".");
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fflush(stdout);
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}
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}
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printf(" done\n");
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printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
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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 mpt_eval(const mpt_model & model, const int n_threads, const int n_past,
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const std::vector<gpt_vocab::id> & embd_inp, std::vector<float> & embd_w, bool logits_all, 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.d_model;
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const int n_layer = hparams.n_layers;
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const int n_head = hparams.n_heads;
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const int n_vocab = hparams.n_vocab;
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const int n_ctx = hparams.n_ctx;
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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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// use 2 scratch buffers
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// TODO: very hacky solution - reimplement in a more elegant way
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static size_t scr0_size = (n_ctx>2048?1024u:512u)*1024*1024;
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static void * scr0 = malloc(scr0_size);
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static size_t scr1_size = (n_ctx>2048?1024u:512u)*1024*1024;
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static void * scr1 = malloc(scr1_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__,
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// 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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params.mem_size = buf_size;
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params.mem_buffer = buf;
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params.no_alloc = false;
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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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 * inpL = ggml_get_rows(ctx0, model.wte_weight, embd);
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * cur;
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ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
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// a = self.ln_1(x)
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{
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cur = ggml_norm(ctx0, inpL);
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cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_1_weight, cur), cur);
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}
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// self-attention
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// b, _, past_key_value = self.attn(a, past_key_value=past_key_value,
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// attn_bias=attn_bias, attention_mask=attention_mask,
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// is_causal=is_causal)
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{
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// compute QKV
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cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_wqkv_weight, cur);
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if (model.hparams.clip_qkv > 0.0f) {
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cur = ggml_clamp(ctx0, cur, -model.hparams.clip_qkv, model.hparams.clip_qkv);
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}
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struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);
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struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1 * sizeof(float) * n_embd);
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struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2 * sizeof(float) * n_embd);
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// store key and value to memory
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{
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struct ggml_tensor * k =
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ggml_view_1d(ctx0, model.memory_k, N * n_embd,
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(ggml_element_size(model.memory_k) * n_embd) * (il * n_ctx + n_past));
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struct ggml_tensor * v =
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ggml_view_1d(ctx0, model.memory_v, N * n_embd,
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(ggml_element_size(model.memory_v) * n_embd) * (il * n_ctx + n_past));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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}
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// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0,
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// 2, 1, 3) [64, N, 12]
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struct ggml_tensor * Q = ggml_permute(
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ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), 0, 2,
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1, 3);
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// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1,
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// 3) [64, n_past + N, 12]
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struct ggml_tensor * K =
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ggml_permute(ctx0,
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ggml_reshape_3d(ctx0,
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ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_embd,
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il * n_ctx * ggml_element_size(model.memory_k) * n_embd),
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n_embd / n_head, n_head, n_past + N),
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0, 2, 1, 3);
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// K * Q
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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// KQ_scaled = KQ / sqrt(n_embd/n_head)
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struct ggml_tensor * KQ_scaled =
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ggml_scale(ctx0, KQ, ggml_new_f32(ctx0, 1.0f / sqrt(float(n_embd) / n_head)));
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struct ggml_tensor * KQ_scaled_alibi =
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ggml_alibi(ctx0, KQ_scaled, n_past, n_head, model.hparams.alibi_bias_max);
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|
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
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|
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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|
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// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1,
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// 2, 0, 3).contiguous() [n_past + N, 64, 12]
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|
struct ggml_tensor * V_trans = ggml_cpy(
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|
ctx0,
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|
ggml_permute(ctx0,
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|
ggml_reshape_3d(ctx0,
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ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_embd,
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|
il * n_ctx * ggml_element_size(model.memory_v) * n_embd),
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|
n_embd / n_head, n_head, n_past + N),
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|
1, 2, 0, 3),
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|
ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd / n_head, n_head));
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|
|
|
// KQV = transpose(V) * KQ_soft_max
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|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
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|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
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|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
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|
cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
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|
|
|
// projection
|
|
{ cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_out_proj_weight, cur); }
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|
}
|
|
|
|
inpL = ggml_add(ctx0, inpL, cur);
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|
|
|
ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
|
|
|
|
// m = self.ln_2(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_2_weight, cur), cur);
|
|
}
|
|
|
|
// n = self.mlp(m)
|
|
{
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up_proj, cur);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// projection
|
|
// cur = proj_w*cur + proj_b
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down_proj, cur);
|
|
}
|
|
|
|
// x = x + n
|
|
inpL = ggml_add(ctx0, inpL, cur);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL);
|
|
// inpL = ln_f_g*inpL
|
|
inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm_f_weight, inpL), inpL);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
|
|
|
// output embedding weight tied to input embedding
|
|
inpL = ggml_mul_mat(ctx0, model.wte_weight, inpL);
|
|
|
|
// logits -> probs
|
|
// inpL = ggml_soft_max(ctx0, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
ggml_graph_compute(ctx0, &gf);
|
|
|
|
// std::cout << "Qcur" << std::endl;
|
|
// print_tensor(Qcur);
|
|
|
|
// if (n_past%100 == 0) {
|
|
// ggml_graph_print(&gf);
|
|
// ggml_graph_dump_dot(&gf, NULL, "mpt-model.dot");
|
|
// }
|
|
|
|
if (logits_all) {
|
|
// return result for all tokens
|
|
embd_w.resize(n_vocab *N);
|
|
memcpy(embd_w.data(), (float *)ggml_get_data(inpL) , sizeof(float) * n_vocab * N);
|
|
} else {
|
|
// 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;
|
|
}
|