mirror of https://github.com/nomic-ai/gpt4all
Initial commit.
Adam Treat
1 year ago
commit
ff2fdecce1
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CMakeLists.txt.user
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[submodule "ggml"]
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path = ggml
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url = https://github.com/manyoso/ggml.git
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cmake_minimum_required(VERSION 3.16)
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project(gpt4all-chat VERSION 0.1 LANGUAGES CXX)
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set(CMAKE_AUTOMOC ON)
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set(CMAKE_AUTORCC ON)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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find_package(Qt6 6.2 COMPONENTS Quick REQUIRED)
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set(GGML_BUILD_EXAMPLES ON CACHE BOOL "ggml: build examples" FORCE)
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add_subdirectory(ggml)
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qt_add_executable(chat
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main.cpp
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gptj.h gptj.cpp
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llm.h llm.cpp
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)
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qt_add_qml_module(chat
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URI gpt4all-chat
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VERSION 1.0
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QML_FILES main.qml
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RESOURCES icons/send_message.svg icons/stop_generating.svg icons/regenerate.svg
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)
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set_target_properties(chat PROPERTIES
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MACOSX_BUNDLE_GUI_IDENTIFIER my.example.com
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MACOSX_BUNDLE_BUNDLE_VERSION ${PROJECT_VERSION}
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MACOSX_BUNDLE_SHORT_VERSION_STRING ${PROJECT_VERSION_MAJOR}.${PROJECT_VERSION_MINOR}
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MACOSX_BUNDLE TRUE
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WIN32_EXECUTABLE TRUE
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)
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target_compile_definitions(chat
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PRIVATE $<$<OR:$<CONFIG:Debug>,$<CONFIG:RelWithDebInfo>>:QT_QML_DEBUG>)
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target_link_libraries(chat
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PRIVATE Qt6::Quick Qt6::Svg)
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target_link_libraries(chat
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PRIVATE ggml ggml_utils)
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@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2023 Adam Treat
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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Subproject commit c9f702ac573a2be4a1b9926979084941f95d0e33
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#include "gptj.h"
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#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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#include <iostream>
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#include <unistd.h>
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// default hparams (GPT-J 6B)
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struct gptj_hparams {
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int32_t n_vocab = 50400;
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int32_t n_ctx = 2048;
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int32_t n_embd = 4096;
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int32_t n_head = 16;
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int32_t n_layer = 28;
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int32_t n_rot = 64;
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int32_t f16 = 1;
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};
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struct gptj_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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// attention
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struct ggml_tensor * c_attn_q_proj_w;
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struct ggml_tensor * c_attn_k_proj_w;
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struct ggml_tensor * c_attn_v_proj_w;
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struct ggml_tensor * c_attn_proj_w;
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// ff
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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;
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struct ggml_tensor * c_mlp_proj_b;
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};
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struct gptj_model {
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gptj_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 * lmh_g; // language model head
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struct ggml_tensor * lmh_b; // language model bias
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std::vector<gptj_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 stream
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bool gptj_model_load(const std::string &fname, std::istream &fin, gptj_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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// 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.n_rot, sizeof(hparams.n_rot));
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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: n_rot = %d\n", __func__, hparams.n_rot);
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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 or quantized
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// in order to save memory and also to speed up the computation
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ggml_type wtype = GGML_TYPE_COUNT;
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switch (model.hparams.f16) {
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case 0: wtype = GGML_TYPE_F32; break;
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case 1: wtype = GGML_TYPE_F16; break;
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case 2: wtype = GGML_TYPE_Q4_0; break;
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case 3: wtype = GGML_TYPE_Q4_1; break;
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default:
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{
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fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
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__func__, fname.c_str(), model.hparams.f16);
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return false;
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}
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}
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const ggml_type wtype2 = 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_sizef(GGML_TYPE_F32); // ln_f_g
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
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ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // wte
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ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // lmh_g
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ctx_size += n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
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ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
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ctx_size += (5 + 10*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.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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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.lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);
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// map by name
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model.tensors["transformer.wte.weight"] = model.wte;
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model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
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model.tensors["transformer.ln_f.bias"] = model.ln_f_b;
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model.tensors["lm_head.weight"] = model.lmh_g;
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model.tensors["lm_head.bias"] = model.lmh_b;
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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.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 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_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*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 = 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["transformer.h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g;
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model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"] = layer.ln_1_b;
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model.tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
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model.tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
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model.tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
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model.tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
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model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"] = layer.c_mlp_fc_w;
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model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"] = layer.c_mlp_fc_b;
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model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
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model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"] = 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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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 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]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], 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, gptj_model & model, gpt_vocab & vocab) {
|
||||
|
||||
auto fin = std::ifstream(fname, std::ios::binary);
|
||||
if (!fin) {
|
||||
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
|
||||
return false;
|
||||
}
|
||||
|
||||
bool loaded = gptj_model_load(fname, fin, model, vocab);
|
||||
fin.close();
|
||||
return loaded;
|
||||
}
|
||||
|
||||
// evaluate the transformer
|
||||
//
|
||||
// - model: the model
|
||||
// - n_threads: number of threads to use
|
||||
// - n_past: the context size so far
|
||||
// - embd_inp: the embeddings of the tokens in the context
|
||||
// - embd_w: the predicted logits for the next token
|
||||
//
|
||||
// The GPT-J model requires about 16MB of memory per input token.
|
||||
//
|
||||
bool gptj_eval(
|
||||
const gptj_model & model,
|
||||
const int n_threads,
|
||||
const int n_past,
|
||||
const std::vector<gpt_vocab::id> & embd_inp,
|
||||
std::vector<float> & embd_w,
|
||||
size_t & mem_per_token) {
|
||||
const int N = embd_inp.size();
|
||||
|
||||
const auto & hparams = model.hparams;
|
||||
|
||||
const int n_embd = hparams.n_embd;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_ctx = hparams.n_ctx;
|
||||
const int n_head = hparams.n_head;
|
||||
const int n_vocab = hparams.n_vocab;
|
||||
const int n_rot = hparams.n_rot;
|
||||
|
||||
const int d_key = n_embd/n_head;
|
||||
|
||||
static size_t buf_size = 256u*1024*1024;
|
||||
static void * buf = malloc(buf_size);
|
||||
|
||||
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
|
||||
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
|
||||
//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
|
||||
|
||||
// reallocate
|
||||
buf_size = buf_size_new;
|
||||
buf = realloc(buf, buf_size);
|
||||
if (buf == nullptr) {
|
||||
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_init_params params = {
|
||||
.mem_size = buf_size,
|
||||
.mem_buffer = buf,
|
||||
};
|
||||
|
||||
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.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)
|
||||
struct ggml_tensor * Q =
|
||||
ggml_permute(ctx0,
|
||||
ggml_rope(ctx0,
|
||||
ggml_cpy(ctx0,
|
||||
Qcur,
|
||||
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
|
||||
n_past, n_rot, 0),
|
||||
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_rope(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),
|
||||
n_past, n_rot, 1),
|
||||
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))
|
||||
);
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, 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.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
1, 2, 0, 3),
|
||||
ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
|
||||
|
||||
// KQV = transpose(V) * KQ_soft_max
|
||||
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);
|
||||
|
||||
// GELU activation
|
||||
cur = ggml_gelu(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
|
||||
{
|
||||
inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);
|
||||
|
||||
inpL = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.lmh_b, inpL),
|
||||
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;
|
||||
}
|
||||
|
||||
struct GPTJPrivate {
|
||||
const std::string modelPath;
|
||||
bool modelLoaded;
|
||||
gpt_vocab vocab;
|
||||
gptj_model model;
|
||||
int64_t t_main_start_us = 0;
|
||||
int64_t t_load_us = 0;
|
||||
int64_t n_threads = 0;
|
||||
std::mt19937 rng;
|
||||
};
|
||||
|
||||
GPTJ::GPTJ()
|
||||
: d_ptr(new GPTJPrivate) {
|
||||
|
||||
d_ptr->modelLoaded = false;
|
||||
}
|
||||
|
||||
bool GPTJ::loadModel(const std::string &modelPath, std::istream &fin) {
|
||||
d_ptr->t_main_start_us = ggml_time_us();
|
||||
std::mt19937 rng(time(NULL));
|
||||
d_ptr->rng = rng;
|
||||
|
||||
// load the model
|
||||
{
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
if (!gptj_model_load(modelPath, fin, d_ptr->model, d_ptr->vocab)) {
|
||||
std::cerr << "GPT-J ERROR: failed to load model from" << modelPath;
|
||||
return false;
|
||||
}
|
||||
|
||||
d_ptr->t_load_us = ggml_time_us() - t_start_us;
|
||||
}
|
||||
|
||||
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
||||
d_ptr->modelLoaded = true;
|
||||
return true;
|
||||
}
|
||||
|
||||
GPTJ::~GPTJ()
|
||||
{
|
||||
ggml_free(d_ptr->model.ctx);
|
||||
}
|
||||
|
||||
bool GPTJ::isModelLoaded() const
|
||||
{
|
||||
return d_ptr->modelLoaded;
|
||||
}
|
||||
|
||||
void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::string&)> response,
|
||||
int32_t n_predict, int32_t top_k, float top_p, float temp,
|
||||
int32_t n_batch) {
|
||||
|
||||
if (!isModelLoaded()) {
|
||||
std::cerr << "GPT-J ERROR: prompt won't work with an unloaded model!\n";
|
||||
return;
|
||||
}
|
||||
|
||||
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(d_ptr->vocab, prompt);
|
||||
|
||||
n_predict = std::min(n_predict, d_ptr->model.hparams.n_ctx - (int) embd_inp.size());
|
||||
|
||||
std::vector<gpt_vocab::id> embd;
|
||||
|
||||
// determine the required inference memory per token:
|
||||
size_t mem_per_token = 0;
|
||||
gptj_eval(d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
|
||||
|
||||
for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
|
||||
// predict
|
||||
if (embd.size() > 0) {
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, n_past, embd, logits, mem_per_token)) {
|
||||
std::cerr << "GPT-J ERROR: Failed to predict\n";
|
||||
return;
|
||||
}
|
||||
|
||||
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 n_vocab = d_ptr->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(d_ptr->vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, d_ptr->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() > n_batch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
i += embd.size() - 1;
|
||||
}
|
||||
|
||||
// display text
|
||||
for (auto id : embd) {
|
||||
if (!response(d_ptr->vocab.id_to_token[id]))
|
||||
goto stop_generating;
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (embd.back() == 50256) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
stop_generating:
|
||||
#if 1
|
||||
// report timing
|
||||
{
|
||||
const int64_t t_main_end_us = ggml_time_us();
|
||||
|
||||
std::cout << "GPT-J INFO: mem per token = " << mem_per_token << " bytes\n";
|
||||
std::cout << "GPT-J INFO: load time = " << d_ptr->t_load_us/1000.0f << " ms\n";
|
||||
std::cout << "GPT-J INFO: sample time = " << t_sample_us/1000.0f << " ms\n";
|
||||
std::cout << "GPT-J INFO: predict time = " << t_predict_us/1000.0f << " ms / " << t_predict_us/1000.0f/n_past << " ms per token\n";
|
||||
std::cout << "GPT-J INFO: total time = " << (t_main_end_us - d_ptr->t_main_start_us)/1000.0f << " ms\n";
|
||||
fflush(stdout);
|
||||
fflush(stderr);
|
||||
}
|
||||
#endif
|
||||
|
||||
return;
|
||||
}
|
||||
@ -0,0 +1,24 @@
|
||||
#ifndef GPTJ_H
|
||||
#define GPTJ_H
|
||||
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
#include <functional>
|
||||
|
||||
class GPTJPrivate;
|
||||
class GPTJ {
|
||||
public:
|
||||
GPTJ();
|
||||
~GPTJ();
|
||||
|
||||
bool loadModel(const std::string &modelPath, std::istream &fin);
|
||||
bool isModelLoaded() const;
|
||||
void prompt(const std::string &prompt, std::function<bool(const std::string&)> response,
|
||||
int32_t n_predict = 200, int32_t top_k = 40, float top_p = 0.9f, float temp = 0.9f,
|
||||
int32_t n_batch = 9);
|
||||
|
||||
private:
|
||||
GPTJPrivate *d_ptr;
|
||||
};
|
||||
|
||||
#endif // GPTJ_H
|
||||
@ -0,0 +1 @@
|
||||
<svg stroke="#7d7d8e" fill="none" stroke-width="1.5" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-3 w-3" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><polyline points="1 4 1 10 7 10"></polyline><polyline points="23 20 23 14 17 14"></polyline><path d="M20.49 9A9 9 0 0 0 5.64 5.64L1 10m22 4l-4.64 4.36A9 9 0 0 1 3.51 15"></path></svg>
|
||||
|
After Width: | Height: | Size: 380 B |
@ -0,0 +1 @@
|
||||
<svg stroke="#7d7d8e" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-4 w-4 mr-1" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><line x1="22" y1="2" x2="11" y2="13"></line><polygon points="22 2 15 22 11 13 2 9 22 2"></polygon></svg>
|
||||
|
After Width: | Height: | Size: 304 B |
@ -0,0 +1 @@
|
||||
<svg stroke="#7d7d8e" fill="none" stroke-width="1.5" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-3 w-3" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><rect x="3" y="3" width="18" height="18" rx="2" ry="2"></rect></svg>
|
||||
|
After Width: | Height: | Size: 265 B |
@ -0,0 +1,132 @@
|
||||
#include "llm.h"
|
||||
|
||||
#include <QCoreApplication>
|
||||
#include <QDir>
|
||||
#include <QFile>
|
||||
#include <QResource>
|
||||
|
||||
class MyLLM: public LLM { };
|
||||
Q_GLOBAL_STATIC(MyLLM, llmInstance)
|
||||
LLM *LLM::globalInstance()
|
||||
{
|
||||
return llmInstance();
|
||||
}
|
||||
|
||||
GPTJObject::GPTJObject()
|
||||
: QObject{nullptr}
|
||||
, m_gptj(new GPTJ)
|
||||
{
|
||||
moveToThread(&m_llmThread);
|
||||
connect(&m_llmThread, &QThread::started, this, &GPTJObject::loadModel);
|
||||
m_llmThread.setObjectName("llm thread");
|
||||
m_llmThread.start();
|
||||
}
|
||||
|
||||
bool GPTJObject::loadModel()
|
||||
{
|
||||
if (isModelLoaded())
|
||||
return true;
|
||||
|
||||
QString modelName("ggml-model-q4_0.bin");
|
||||
QFile file(QCoreApplication::applicationDirPath() + QDir::separator() + modelName);
|
||||
if (file.open(QIODevice::ReadOnly)) {
|
||||
|
||||
QByteArray data = file.readAll();
|
||||
std::istringstream iss(data.toStdString());
|
||||
|
||||
m_gptj->loadModel(modelName.toStdString(), iss);
|
||||
emit isModelLoadedChanged();
|
||||
}
|
||||
|
||||
return m_gptj;
|
||||
}
|
||||
|
||||
bool GPTJObject::isModelLoaded() const
|
||||
{
|
||||
return m_gptj->isModelLoaded();
|
||||
}
|
||||
|
||||
void GPTJObject::resetResponse()
|
||||
{
|
||||
m_response = std::string();
|
||||
}
|
||||
|
||||
QString GPTJObject::response() const
|
||||
{
|
||||
return QString::fromStdString(m_response);
|
||||
}
|
||||
|
||||
bool GPTJObject::handleResponse(const std::string &response)
|
||||
{
|
||||
#if 0
|
||||
printf("%s", response.c_str());
|
||||
fflush(stdout);
|
||||
#endif
|
||||
m_response.append(response);
|
||||
emit responseChanged();
|
||||
return !m_stopGenerating;
|
||||
}
|
||||
|
||||
bool GPTJObject::prompt(const QString &prompt)
|
||||
{
|
||||
if (!isModelLoaded())
|
||||
return false;
|
||||
|
||||
m_stopGenerating = false;
|
||||
auto func = std::bind(&GPTJObject::handleResponse, this, std::placeholders::_1);
|
||||
emit responseStarted();
|
||||
m_gptj->prompt(prompt.toStdString(), func);
|
||||
emit responseStopped();
|
||||
return true;
|
||||
}
|
||||
|
||||
LLM::LLM()
|
||||
: QObject{nullptr}
|
||||
, m_gptj(new GPTJObject)
|
||||
, m_responseInProgress(false)
|
||||
{
|
||||
connect(m_gptj, &GPTJObject::isModelLoadedChanged, this, &LLM::isModelLoadedChanged, Qt::QueuedConnection);
|
||||
connect(m_gptj, &GPTJObject::responseChanged, this, &LLM::responseChanged, Qt::QueuedConnection);
|
||||
connect(m_gptj, &GPTJObject::responseStarted, this, &LLM::responseStarted, Qt::QueuedConnection);
|
||||
connect(m_gptj, &GPTJObject::responseStopped, this, &LLM::responseStopped, Qt::QueuedConnection);
|
||||
|
||||
connect(this, &LLM::promptRequested, m_gptj, &GPTJObject::prompt, Qt::QueuedConnection);
|
||||
connect(this, &LLM::resetResponseRequested, m_gptj, &GPTJObject::resetResponse, Qt::BlockingQueuedConnection);
|
||||
}
|
||||
|
||||
bool LLM::isModelLoaded() const
|
||||
{
|
||||
return m_gptj->isModelLoaded();
|
||||
}
|
||||
|
||||
void LLM::prompt(const QString &prompt)
|
||||
{
|
||||
emit promptRequested(prompt);
|
||||
}
|
||||
|
||||
void LLM::resetResponse()
|
||||
{
|
||||
emit resetResponseRequested(); // blocking queued connection
|
||||
}
|
||||
|
||||
void LLM::stopGenerating()
|
||||
{
|
||||
m_gptj->stopGenerating();
|
||||
}
|
||||
|
||||
QString LLM::response() const
|
||||
{
|
||||
return m_gptj->response();
|
||||
}
|
||||
|
||||
void LLM::responseStarted()
|
||||
{
|
||||
m_responseInProgress = true;
|
||||
emit responseInProgressChanged();
|
||||
}
|
||||
|
||||
void LLM::responseStopped()
|
||||
{
|
||||
m_responseInProgress = false;
|
||||
emit responseInProgressChanged();
|
||||
}
|
||||
@ -0,0 +1,84 @@
|
||||
#ifndef LLM_H
|
||||
#define LLM_H
|
||||
|
||||
#include <QObject>
|
||||
#include <QThread>
|
||||
#include "gptj.h"
|
||||
|
||||
class GPTJObject : public QObject
|
||||
{
|
||||
Q_OBJECT
|
||||
Q_PROPERTY(bool isModelLoaded READ isModelLoaded NOTIFY isModelLoadedChanged)
|
||||
Q_PROPERTY(QString response READ response NOTIFY responseChanged)
|
||||
|
||||
public:
|
||||
|
||||
GPTJObject();
|
||||
|
||||
bool loadModel();
|
||||
bool isModelLoaded() const;
|
||||
void resetResponse();
|
||||
void stopGenerating() { m_stopGenerating = true; }
|
||||
|
||||
QString response() const;
|
||||
|
||||
public Q_SLOTS:
|
||||
bool prompt(const QString &prompt);
|
||||
|
||||
Q_SIGNALS:
|
||||
void isModelLoadedChanged();
|
||||
void responseChanged();
|
||||
void responseStarted();
|
||||
void responseStopped();
|
||||
|
||||
private:
|
||||
bool handleResponse(const std::string &response);
|
||||
|
||||
private:
|
||||
GPTJ *m_gptj;
|
||||
std::stringstream m_debug;
|
||||
std::string m_response;
|
||||
QThread m_llmThread;
|
||||
std::atomic<bool> m_stopGenerating;
|
||||
};
|
||||
|
||||
class LLM : public QObject
|
||||
{
|
||||
Q_OBJECT
|
||||
Q_PROPERTY(bool isModelLoaded READ isModelLoaded NOTIFY isModelLoadedChanged)
|
||||
Q_PROPERTY(QString response READ response NOTIFY responseChanged)
|
||||
Q_PROPERTY(bool responseInProgress READ responseInProgress NOTIFY responseInProgressChanged)
|
||||
public:
|
||||
|
||||
static LLM *globalInstance();
|
||||
|
||||
Q_INVOKABLE bool isModelLoaded() const;
|
||||
Q_INVOKABLE void prompt(const QString &prompt);
|
||||
Q_INVOKABLE void resetResponse();
|
||||
Q_INVOKABLE void stopGenerating();
|
||||
|
||||
QString response() const;
|
||||
bool responseInProgress() const { return m_responseInProgress; }
|
||||
|
||||
Q_SIGNALS:
|
||||
void isModelLoadedChanged();
|
||||
void responseChanged();
|
||||
void responseInProgressChanged();
|
||||
void promptRequested(const QString &prompt);
|
||||
void resetResponseRequested();
|
||||
|
||||
private Q_SLOTS:
|
||||
void responseStarted();
|
||||
void responseStopped();
|
||||
|
||||
private:
|
||||
GPTJObject *m_gptj;
|
||||
bool m_responseInProgress;
|
||||
|
||||
private:
|
||||
explicit LLM();
|
||||
~LLM() {}
|
||||
friend class MyLLM;
|
||||
};
|
||||
|
||||
#endif // LLM_H
|
||||
@ -0,0 +1,31 @@
|
||||
#include <QGuiApplication>
|
||||
#include <QQmlApplicationEngine>
|
||||
#include <QQmlContext>
|
||||
|
||||
#include <QDirIterator>
|
||||
|
||||
#include "llm.h"
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
QGuiApplication app(argc, argv);
|
||||
QQmlApplicationEngine engine;
|
||||
qmlRegisterSingletonInstance("llm", 1, 0, "LLM", LLM::globalInstance());
|
||||
const QUrl url(u"qrc:/gpt4all-chat/main.qml"_qs);
|
||||
|
||||
QObject::connect(&engine, &QQmlApplicationEngine::objectCreated,
|
||||
&app, [url](QObject *obj, const QUrl &objUrl) {
|
||||
if (!obj && url == objUrl)
|
||||
QCoreApplication::exit(-1);
|
||||
}, Qt::QueuedConnection);
|
||||
engine.load(url);
|
||||
|
||||
#if 1
|
||||
QDirIterator it("qrc:", QDirIterator::Subdirectories);
|
||||
while (it.hasNext()) {
|
||||
qDebug() << it.next();
|
||||
}
|
||||
#endif
|
||||
|
||||
return app.exec();
|
||||
}
|
||||
@ -0,0 +1,233 @@
|
||||
import QtQuick
|
||||
import QtQuick.Controls
|
||||
import llm
|
||||
|
||||
Window {
|
||||
id: window
|
||||
width: 1280
|
||||
height: 720
|
||||
visible: true
|
||||
title: qsTr("GPT4All Chat")
|
||||
|
||||
Rectangle {
|
||||
id: conversationList
|
||||
width: 300
|
||||
anchors.left: parent.left
|
||||
anchors.top: parent.top
|
||||
anchors.bottom: parent.bottom
|
||||
color: "#202123"
|
||||
|
||||
Button {
|
||||
id: newChat
|
||||
text: qsTr("New chat")
|
||||
anchors.top: parent.top
|
||||
anchors.left: parent.left
|
||||
anchors.right: parent.right
|
||||
anchors.margins: 15
|
||||
padding: 15
|
||||
background: Rectangle {
|
||||
opacity: .5
|
||||
border.color: "#7d7d8e"
|
||||
border.width: 1
|
||||
radius: 10
|
||||
color: "#343541"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Rectangle {
|
||||
id: conversation
|
||||
color: "#343541"
|
||||
anchors.left: conversationList.right
|
||||
anchors.right: parent.right
|
||||
anchors.bottom: parent.bottom
|
||||
anchors.top: parent.top
|
||||
|
||||
ScrollView {
|
||||
id: scrollView
|
||||
anchors.left: parent.left
|
||||
anchors.right: parent.right
|
||||
anchors.top: parent.top
|
||||
anchors.bottom: textInput.top
|
||||
anchors.bottomMargin: 30
|
||||
ScrollBar.vertical.policy: ScrollBar.AlwaysOn
|
||||
|
||||
ListModel {
|
||||
id: chatModel
|
||||
}
|
||||
|
||||
Rectangle {
|
||||
anchors.fill: parent
|
||||
color: "#444654"
|
||||
|
||||
ListView {
|
||||
id: listView
|
||||
anchors.fill: parent
|
||||
header: TextField {
|
||||
id: modelName
|
||||
width: parent.width
|
||||
color: "#d1d5db"
|
||||
padding: 20
|
||||
font.pixelSize: 24
|
||||
text: "Model: GPT-J-6B-4bit"
|
||||
background: Rectangle {
|
||||
color: "#444654"
|
||||
}
|
||||
focus: false
|
||||
horizontalAlignment: TextInput.AlignHCenter
|
||||
}
|
||||
|
||||
model: chatModel
|
||||
delegate: TextArea {
|
||||
text: currentResponse ? LLM.response : value
|
||||
width: parent.width
|
||||
color: "#d1d5db"
|
||||
wrapMode: Text.WordWrap
|
||||
focus: false
|
||||
padding: 20
|
||||
font.pixelSize: 24
|
||||
cursorVisible: currentResponse ? LLM.responseInProgress : false
|
||||
cursorPosition: text.length
|
||||
background: Rectangle {
|
||||
color: name === qsTr("Response: ") ? "#444654" : "#343541"
|
||||
}
|
||||
|
||||
leftPadding: 100
|
||||
|
||||
Rectangle {
|
||||
anchors.left: parent.left
|
||||
anchors.top: parent.top
|
||||
anchors.leftMargin: 20
|
||||
anchors.topMargin: 20
|
||||
width: 30
|
||||
height: 30
|
||||
radius: 5
|
||||
color: name === qsTr("Response: ") ? "#10a37f" : "#ec86bf"
|
||||
|
||||
Text {
|
||||
anchors.centerIn: parent
|
||||
text: name === qsTr("Response: ") ? "R" : "P"
|
||||
color: "white"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
property bool shouldAutoScroll: true
|
||||
property bool isAutoScrolling: false
|
||||
|
||||
Connections {
|
||||
target: LLM
|
||||
function onResponseChanged() {
|
||||
if (listView.shouldAutoScroll) {
|
||||
listView.isAutoScrolling = true
|
||||
listView.positionViewAtEnd()
|
||||
listView.isAutoScrolling = false
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
onContentYChanged: {
|
||||
if (!isAutoScrolling)
|
||||
shouldAutoScroll = atYEnd
|
||||
}
|
||||
|
||||
Component.onCompleted: {
|
||||
shouldAutoScroll = true
|
||||
positionViewAtEnd()
|
||||
}
|
||||
|
||||
footer: Item {
|
||||
id: bottomPadding
|
||||
width: parent.width
|
||||
height: 60
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Button {
|
||||
Image {
|
||||
anchors.verticalCenter: parent.verticalCenter
|
||||
anchors.left: parent.left
|
||||
anchors.leftMargin: 15
|
||||
source: LLM.responseInProgress ? "qrc:/gpt4all-chat/icons/stop_generating.svg" : "qrc:/gpt4all-chat/icons/regenerate.svg"
|
||||
}
|
||||
text: LLM.responseInProgress ? qsTr(" Stop generating") : qsTr(" Regenerate response")
|
||||
onClicked: {
|
||||
if (LLM.responseInProgress)
|
||||
LLM.stopGenerating()
|
||||
else {
|
||||
LLM.resetResponse()
|
||||
if (chatModel.count) {
|
||||
var listElement = chatModel.get(chatModel.count - 1)
|
||||
if (listElement.name === qsTr("Response: ")) {
|
||||
listElement.currentResponse = true
|
||||
listElement.value = LLM.response
|
||||
LLM.prompt(listElement.prompt)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
anchors.bottom: textInput.top
|
||||
anchors.horizontalCenter: textInput.horizontalCenter
|
||||
anchors.bottomMargin: 40
|
||||
padding: 15
|
||||
background: Rectangle {
|
||||
opacity: .5
|
||||
border.color: "#7d7d8e"
|
||||
border.width: 1
|
||||
radius: 10
|
||||
color: "#343541"
|
||||
}
|
||||
}
|
||||
|
||||
TextField {
|
||||
id: textInput
|
||||
anchors.left: parent.left
|
||||
anchors.right: parent.right
|
||||
anchors.bottom: parent.bottom
|
||||
anchors.margins: 30
|
||||
color: "#dadadc"
|
||||
padding: 20
|
||||
font.pixelSize: 24
|
||||
placeholderText: qsTr("Send a message...")
|
||||
placeholderTextColor: "#7d7d8e"
|
||||
background: Rectangle {
|
||||
color: "#40414f"
|
||||
radius: 10
|
||||
}
|
||||
onAccepted: {
|
||||
LLM.stopGenerating()
|
||||
|
||||
if (chatModel.count) {
|
||||
var listElement = chatModel.get(chatModel.count - 1)
|
||||
listElement.currentResponse = false
|
||||
listElement.value = LLM.response
|
||||
}
|
||||
chatModel.append({"name": qsTr("Prompt: "), "currentResponse": false, "value": textInput.text})
|
||||
chatModel.append({"name": qsTr("Response: "), "currentResponse": true, "value": "", "prompt": textInput.text})
|
||||
|
||||
LLM.resetResponse()
|
||||
LLM.prompt(textInput.text)
|
||||
textInput.text = ""
|
||||
}
|
||||
|
||||
Button {
|
||||
anchors.right: textInput.right
|
||||
anchors.verticalCenter: textInput.verticalCenter
|
||||
anchors.rightMargin: 15
|
||||
width: 30
|
||||
height: 30
|
||||
|
||||
background: Image {
|
||||
anchors.centerIn: parent
|
||||
source: "qrc:/gpt4all-chat/icons/send_message.svg"
|
||||
}
|
||||
|
||||
onClicked: {
|
||||
textInput.onAccepted()
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue