From c5e1313e9cab53e035a6f990e6d9b34c57f1f17d Mon Sep 17 00:00:00 2001 From: BuildTools Date: Fri, 16 Aug 2024 15:15:29 -0700 Subject: [PATCH] feat(ui): add menubar - add basic menu bar showing Close and About areas - add program version in localizations.py - refactor functions out of AutoGGUF.py and move to ui_update.py --- src/AutoGGUF.py | 122 +++++++++------------------- src/localizations.py | 188 +++++++++++++++++++++++++++---------------- src/ui_update.py | 97 ++++++++++++++++++++++ 3 files changed, 254 insertions(+), 153 deletions(-) create mode 100644 src/ui_update.py diff --git a/src/AutoGGUF.py b/src/AutoGGUF.py index afef629..22bb0b5 100644 --- a/src/AutoGGUF.py +++ b/src/AutoGGUF.py @@ -5,6 +5,7 @@ import psutil import requests +from functools import partial from PySide6.QtCore import * from PySide6.QtGui import * from PySide6.QtWidgets import * @@ -19,6 +20,7 @@ from error_handling import show_error, handle_error from imports_and_globals import ensure_directory, open_file_safe, resource_path from localizations import * +from ui_update import * class AutoGGUF(QMainWindow): @@ -34,6 +36,19 @@ def __init__(self): ensure_directory(os.path.abspath("quantized_models")) ensure_directory(os.path.abspath("models")) + # References + self.update_base_model_visibility = partial(update_base_model_visibility, self) + self.update_assets = update_assets.__get__(self) + self.update_cuda_option = update_cuda_option.__get__(self) + self.update_cuda_backends = update_cuda_backends.__get__(self) + self.update_threads_spinbox = partial(update_threads_spinbox, self) + self.update_threads_slider = partial(update_threads_slider, self) + self.update_gpu_offload_spinbox = partial(update_gpu_offload_spinbox, self) + self.update_gpu_offload_slider = partial(update_gpu_offload_slider, self) + self.update_model_info = partial(update_model_info, self.logger, self) + self.update_system_info = partial(update_system_info, self) + self.update_download_progress = partial(update_download_progress, self) + # Create a central widget and main layout central_widget = QWidget() main_layout = QHBoxLayout(central_widget) @@ -52,6 +67,23 @@ def __init__(self): left_widget.setMinimumWidth(800) right_widget.setMinimumWidth(400) + menubar = QMenuBar(self) + self.layout().setMenuBar(menubar) + + # File menu + file_menu = menubar.addMenu("&File") + close_action = QAction("&Close", self) + close_action.setShortcut(QKeySequence.Quit) + close_action.triggered.connect(self.close) + file_menu.addAction(close_action) + + # Help menu + help_menu = menubar.addMenu("&Help") + about_action = QAction("&About", self) + about_action.setShortcut(QKeySequence("Ctrl+Q")) + about_action.triggered.connect(self.show_about) + help_menu.addAction(about_action) + left_layout = QVBoxLayout(left_widget) right_layout = QVBoxLayout(right_widget) @@ -679,9 +711,13 @@ def refresh_backends(self): self.backend_combo.setEnabled(False) self.logger.info(FOUND_VALID_BACKENDS.format(self.backend_combo.count())) - def update_base_model_visibility(self, index): - is_gguf = self.lora_output_type_combo.itemText(index) == "GGUF" - self.base_model_wrapper.setVisible(is_gguf) + def show_about(self): + about_text = ( + "AutoGGUF\n\n" + f"Version: {AUTOGGUF_VERSION}\n\n" + "A tool for managing and converting GGUF models." + ) + QMessageBox.about(self, "About AutoGGUF", about_text) def save_preset(self): self.logger.info(SAVING_PRESET) @@ -1174,20 +1210,6 @@ def refresh_releases(self): except requests.exceptions.RequestException as e: show_error(self.logger, ERROR_FETCHING_RELEASES.format(str(e))) - def update_assets(self): - self.logger.debug(UPDATING_ASSET_LIST) - self.asset_combo.clear() - release = self.release_combo.currentData() - if release: - if "assets" in release: - for asset in release["assets"]: - self.asset_combo.addItem(asset["name"], userData=asset) - else: - show_error( - self.logger, NO_ASSETS_FOUND_FOR_RELEASE.format(release["tag_name"]) - ) - self.update_cuda_option() - def download_llama_cpp(self): self.logger.info(STARTING_LLAMACPP_DOWNLOAD) asset = self.asset_combo.currentData() @@ -1209,45 +1231,6 @@ def download_llama_cpp(self): self.download_button.setEnabled(False) self.download_progress.setValue(0) - def update_cuda_option(self): - self.logger.debug(UPDATING_CUDA_OPTIONS) - asset = self.asset_combo.currentData() - - # Handle the case where asset is None - if asset is None: - self.logger.warning(NO_ASSET_SELECTED_FOR_CUDA_CHECK) - self.cuda_extract_checkbox.setVisible(False) - self.cuda_backend_label.setVisible(False) - self.backend_combo_cuda.setVisible(False) - return # Exit the function early - - is_cuda = asset and "cudart" in asset["name"].lower() - self.cuda_extract_checkbox.setVisible(is_cuda) - self.cuda_backend_label.setVisible(is_cuda) - self.backend_combo_cuda.setVisible(is_cuda) - if is_cuda: - self.update_cuda_backends() - - def update_cuda_backends(self): - self.logger.debug(UPDATING_CUDA_BACKENDS) - self.backend_combo_cuda.clear() - llama_bin = os.path.abspath("llama_bin") - if os.path.exists(llama_bin): - for item in os.listdir(llama_bin): - item_path = os.path.join(llama_bin, item) - if os.path.isdir(item_path) and "cudart-llama" not in item.lower(): - if "cu1" in item.lower(): # Only include CUDA-capable backends - self.backend_combo_cuda.addItem(item, userData=item_path) - - if self.backend_combo_cuda.count() == 0: - self.backend_combo_cuda.addItem(NO_SUITABLE_CUDA_BACKENDS) - self.backend_combo_cuda.setEnabled(False) - else: - self.backend_combo_cuda.setEnabled(True) - - def update_download_progress(self, progress): - self.download_progress.setValue(progress) - def download_finished(self, extract_dir): self.download_button.setEnabled(True) self.download_progress.setValue(100) @@ -1335,18 +1318,6 @@ def show_task_properties(self, item): model_info_dialog.exec() break - def update_threads_spinbox(self, value): - self.threads_spinbox.setValue(value) - - def update_threads_slider(self, value): - self.threads_slider.setValue(value) - - def update_gpu_offload_spinbox(self, value): - self.gpu_offload_spinbox.setValue(value) - - def update_gpu_offload_slider(self, value): - self.gpu_offload_slider.setValue(value) - def toggle_gpu_offload_auto(self, state): is_auto = state == Qt.CheckState.Checked self.gpu_offload_slider.setEnabled(not is_auto) @@ -1483,17 +1454,6 @@ def validate_quantization_inputs(self): if errors: raise ValueError("\n".join(errors)) - def update_system_info(self): - ram = psutil.virtual_memory() - cpu = psutil.cpu_percent() - self.ram_bar.setValue(int(ram.percent)) - self.ram_bar.setFormat( - RAM_USAGE_FORMAT.format( - ram.percent, ram.used // 1024 // 1024, ram.total // 1024 // 1024 - ) - ) - self.cpu_label.setText(CPU_USAGE_FORMAT.format(cpu)) - def add_kv_override(self, override_string=None): entry = KVOverrideEntry() entry.deleted.connect(self.remove_kv_override) @@ -1679,10 +1639,6 @@ def quantize_model(self): except Exception as e: show_error(self.logger, ERROR_STARTING_QUANTIZATION.format(str(e))) - def update_model_info(self, model_info): - self.logger.debug(UPDATING_MODEL_INFO.format(model_info)) - pass - def parse_progress(self, line, task_item): # Parses the output line for progress information and updates the task item. match = re.search(r"\[(\d+)/(\d+)\]", line) diff --git a/src/localizations.py b/src/localizations.py index 954bb4c..7c88132 100644 --- a/src/localizations.py +++ b/src/localizations.py @@ -1,5 +1,7 @@ import os +AUTOGGUF_VERSION = "v1.6.2" + class _Localization: def __init__(self): @@ -875,7 +877,9 @@ def __init__(self): self.DOWNLOAD_FINISHED_EXTRACTED_TO = "下载完成。已解压到：{0}" self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并解压到{0}" self.NO_SUITABLE_CUDA_BACKEND_FOUND = "未找到合适的CUDA后端进行提取" - self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并解压到{0}" + self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( + "llama.cpp二进制文件已下载并解压到{0}" + ) self.REFRESHING_LLAMACPP_RELEASES = "刷新llama.cpp版本" self.UPDATING_ASSET_LIST = "更新资源列表" self.UPDATING_CUDA_OPTIONS = "更新CUDA选项" @@ -939,7 +943,9 @@ def __init__(self): self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "对output.weight张量使用此类型" self.TOKEN_EMBEDDING_TYPE = "词元嵌入类型：" self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "对词元嵌入张量使用此类型" - self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "将生成与输入相同分片的量化模型" + self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = ( + "将生成与输入相同分片的量化模型" + ) self.OVERRIDE_MODEL_METADATA = "覆盖模型元数据" self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix生成的输入数据文件" self.MODEL_TO_BE_QUANTIZED = "要量化的模型" @@ -986,7 +992,9 @@ def __init__(self): self.MODEL_DIRECTORY_REQUIRED = "需要模型目录" self.HF_TO_GGUF_CONVERSION_COMMAND = "HF到GGUF转换命令：{}" self.CONVERTING_TO_GGUF = "将{}转换为GGUF" - self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = "启动HuggingFace到GGUF转换时出错：{}" + self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = ( + "启动HuggingFace到GGUF转换时出错：{}" + ) self.HF_TO_GGUF_CONVERSION_TASK_STARTED = "HuggingFace到GGUF转换任务已开始" @@ -1434,7 +1442,9 @@ def __init__(self): self.NO_MODEL_SELECTED = "कोई मॉडल चयनित नहीं" self.REFRESH_RELEASES = "रिलीज़ रीफ्रेश करें" self.NO_SUITABLE_CUDA_BACKENDS = "कोई उपयुक्त CUDA बैकएंड नहीं मिला" - self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं" + self.LLAMACPP_DOWNLOADED_EXTRACTED = ( + "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं" + ) self.CUDA_FILES_EXTRACTED = "CUDA फ़ाइलें निकाली गईं" self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = ( "निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला" @@ -1463,7 +1473,9 @@ def __init__(self): self.RESTARTING_TASK = "कार्य पुनः आरंभ हो रहा है: {0}" self.IN_PROGRESS = "प्रगति में" self.DOWNLOAD_FINISHED_EXTRACTED_TO = "डाउनलोड समाप्त। निकाला गया: {0}" - self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं" + self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = ( + "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं" + ) self.NO_SUITABLE_CUDA_BACKEND_FOUND = ( "निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला" ) @@ -1485,25 +1497,17 @@ def __init__(self): self.DELETING_TASK = "कार्य हटाया जा रहा है: {0}" self.LOADING_MODELS = "मॉडल लोड हो रहे हैं" self.LOADED_MODELS = "{0} मॉडल लोड किए गए" - self.BROWSING_FOR_MODELS_DIRECTORY = ( - "मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है" - ) + self.BROWSING_FOR_MODELS_DIRECTORY = "मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है" self.SELECT_MODELS_DIRECTORY = "मॉडल निर्देशिका चुनें" - self.BROWSING_FOR_OUTPUT_DIRECTORY = ( - "आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है" - ) + self.BROWSING_FOR_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है" self.SELECT_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका चुनें" - self.BROWSING_FOR_LOGS_DIRECTORY = ( - "लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है" - ) + self.BROWSING_FOR_LOGS_DIRECTORY = "लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है" self.SELECT_LOGS_DIRECTORY = "लॉग निर्देशिका चुनें" self.BROWSING_FOR_IMATRIX_FILE = "IMatrix फ़ाइल के लिए ब्राउज़ किया जा रहा है" self.SELECT_IMATRIX_FILE = "IMatrix फ़ाइल चुनें" self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} MB / {2} MB)" self.CPU_USAGE_FORMAT = "CPU उपयोग: {0:.1f}%" - self.VALIDATING_QUANTIZATION_INPUTS = ( - "क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं" - ) + self.VALIDATING_QUANTIZATION_INPUTS = "क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं" self.MODELS_PATH_REQUIRED = "मॉडल पथ आवश्यक है" self.OUTPUT_PATH_REQUIRED = "आउटपुट पथ आवश्यक है" self.LOGS_PATH_REQUIRED = "लॉग पथ आवश्यक है" @@ -1530,9 +1534,7 @@ def __init__(self): self.STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू हो रहा है" self.BACKEND_PATH_NOT_EXIST = "बैकएंड पथ मौजूद नहीं है: {0}" self.GENERATING_IMATRIX = "IMatrix उत्पन्न किया जा रहा है" - self.ERROR_STARTING_IMATRIX_GENERATION = ( - "IMatrix उत्पादन शुरू करने में त्रुटि: {0}" - ) + self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू करने में त्रुटि: {0}" self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix उत्पादन कार्य शुरू हुआ" self.ERROR_MESSAGE = "त्रुटि: {0}" self.TASK_ERROR = "कार्य त्रुटि: {0}" @@ -1542,14 +1544,14 @@ def __init__(self): self.ALLOWS_REQUANTIZING = ( "पहले से क्वांटाइज़ किए गए टेंसर को पुनः क्वांटाइज़ करने की अनुमति देता है" ) - self.LEAVE_OUTPUT_WEIGHT = ( - "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा" + self.LEAVE_OUTPUT_WEIGHT = "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा" + self.DISABLE_K_QUANT_MIXTURES = ( + "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें" ) - self.DISABLE_K_QUANT_MIXTURES = "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें" - self.USE_DATA_AS_IMPORTANCE_MATRIX = "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें" - self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( - "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें" + self.USE_DATA_AS_IMPORTANCE_MATRIX = ( + "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें" ) + self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें" self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग न करें" ) @@ -2006,7 +2008,9 @@ def __init__(self): self.RESTART = "再起動" self.DELETE = "削除" self.CONFIRM_DELETION = "このタスクを削除してもよろしいですか？" - self.TASK_RUNNING_WARNING = "一部のタスクはまだ実行中です。終了してもよろしいですか？" + self.TASK_RUNNING_WARNING = ( + "一部のタスクはまだ実行中です。終了してもよろしいですか？" + ) self.YES = "はい" self.NO = "いいえ" self.DOWNLOAD_COMPLETE = "ダウンロード完了" @@ -2019,11 +2023,11 @@ def __init__(self): self.NO_MODEL_SELECTED = "モデルが選択されていません" self.REFRESH_RELEASES = "リリースを更新" self.NO_SUITABLE_CUDA_BACKENDS = "適切なCUDAバックエンドが見つかりませんでした" - self.LLAMACPP_DOWNLOADED_EXTRACTED = ( - "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました" - ) + self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました" self.CUDA_FILES_EXTRACTED = "CUDAファイルはに抽出されました" - self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "抽出に適したCUDAバックエンドが見つかりませんでした" + self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = ( + "抽出に適したCUDAバックエンドが見つかりませんでした" + ) self.ERROR_FETCHING_RELEASES = "リリースの取得中にエラーが発生しました: {0}" self.CONFIRM_DELETION_TITLE = "削除の確認" self.LOG_FOR = "{0}のログ" @@ -2048,10 +2052,10 @@ def __init__(self): self.RESTARTING_TASK = "タスクを再起動しています: {0}" self.IN_PROGRESS = "処理中" self.DOWNLOAD_FINISHED_EXTRACTED_TO = "ダウンロードが完了しました。抽出先: {0}" - self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = ( - "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました" + self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました" + self.NO_SUITABLE_CUDA_BACKEND_FOUND = ( + "抽出に適したCUDAバックエンドが見つかりませんでした" ) - self.NO_SUITABLE_CUDA_BACKEND_FOUND = "抽出に適したCUDAバックエンドが見つかりませんでした" self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( "llama.cppバイナリがダウンロードされ、{0}に抽出されました" ) @@ -2101,24 +2105,42 @@ def __init__(self): self.STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始しています" self.BACKEND_PATH_NOT_EXIST = "バックエンドパスが存在しません: {0}" self.GENERATING_IMATRIX = "IMatrixを生成しています" - self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始中にエラーが発生しました: {0}" + self.ERROR_STARTING_IMATRIX_GENERATION = ( + "IMatrixの生成を開始中にエラーが発生しました: {0}" + ) self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix生成タスクが開始されました" self.ERROR_MESSAGE = "エラー: {0}" self.TASK_ERROR = "タスクエラー: {0}" self.APPLICATION_CLOSING = "アプリケーションを終了しています" self.APPLICATION_CLOSED = "アプリケーションが終了しました" self.SELECT_QUANTIZATION_TYPE = "量子化タイプを選択してください" - self.ALLOWS_REQUANTIZING = "すでに量子化されているテンソルの再量子化を許可します" + self.ALLOWS_REQUANTIZING = ( + "すでに量子化されているテンソルの再量子化を許可します" + ) self.LEAVE_OUTPUT_WEIGHT = "output.weightは（再）量子化されません" - self.DISABLE_K_QUANT_MIXTURES = "k-quant混合を無効にし、すべてのテンソルを同じタイプに量子化します" - self.USE_DATA_AS_IMPORTANCE_MATRIX = "量子化最適化の重要度マトリックスとしてファイル内のデータを使用します" - self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "これらのテンソルに重要度マトリックスを使用します" - self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "これらのテンソルに重要度マトリックスを使用しません" + self.DISABLE_K_QUANT_MIXTURES = ( + "k-quant混合を無効にし、すべてのテンソルを同じタイプに量子化します" + ) + self.USE_DATA_AS_IMPORTANCE_MATRIX = ( + "量子化最適化の重要度マトリックスとしてファイル内のデータを使用します" + ) + self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( + "これらのテンソルに重要度マトリックスを使用します" + ) + self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( + "これらのテンソルに重要度マトリックスを使用しません" + ) self.OUTPUT_TENSOR_TYPE = "出力テンソルタイプ:" - self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "output.weightテンソルにこのタイプを使用します" + self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = ( + "output.weightテンソルにこのタイプを使用します" + ) self.TOKEN_EMBEDDING_TYPE = "トークン埋め込みタイプ:" - self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "トークン埋め込みテンソルにこのタイプを使用します" - self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "入力と同じシャードで量子化されたモデルを生成します" + self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = ( + "トークン埋め込みテンソルにこのタイプを使用します" + ) + self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = ( + "入力と同じシャードで量子化されたモデルを生成します" + ) self.OVERRIDE_MODEL_METADATA = "モデルメタデータを上書きする" self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix生成用の入力データファイル" self.MODEL_TO_BE_QUANTIZED = "量子化されるモデル" @@ -2775,11 +2797,11 @@ def __init__(self): self.NO_MODEL_SELECTED = "모델이 선택되지 않았습니다" self.REFRESH_RELEASES = "릴리스 새로 고침" self.NO_SUITABLE_CUDA_BACKENDS = "적합한 CUDA 백엔드를 찾을 수 없습니다" - self.LLAMACPP_DOWNLOADED_EXTRACTED = ( - "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다." - ) + self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다." self.CUDA_FILES_EXTRACTED = "CUDA 파일이 에 추출되었습니다." - self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다." + self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = ( + "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다." + ) self.ERROR_FETCHING_RELEASES = "릴리스를 가져오는 중 오류가 발생했습니다: {0}" self.CONFIRM_DELETION_TITLE = "삭제 확인" self.LOG_FOR = "{0}에 대한 로그" @@ -2803,11 +2825,13 @@ def __init__(self): self.TASK_PRESET_SAVED_TO = "작업 프리셋이 {0}에 저장되었습니다." self.RESTARTING_TASK = "작업을 다시 시작하는 중입니다: {0}" self.IN_PROGRESS = "진행 중" - self.DOWNLOAD_FINISHED_EXTRACTED_TO = "다운로드가 완료되었습니다. 추출 위치: {0}" - self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = ( - "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다." + self.DOWNLOAD_FINISHED_EXTRACTED_TO = ( + "다운로드가 완료되었습니다. 추출 위치: {0}" + ) + self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다." + self.NO_SUITABLE_CUDA_BACKEND_FOUND = ( + "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다." ) - self.NO_SUITABLE_CUDA_BACKEND_FOUND = "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다." self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다." ) @@ -2844,10 +2868,14 @@ def __init__(self): self.INPUT_FILE_NOT_EXIST = "입력 파일 '{0}'이 존재하지 않습니다." self.QUANTIZING_MODEL_TO = "{0}을 {1}(으)로 양자화하는 중입니다." self.QUANTIZATION_TASK_STARTED = "{0}에 대한 양자화 작업이 시작되었습니다." - self.ERROR_STARTING_QUANTIZATION = "양자화를 시작하는 중 오류가 발생했습니다: {0}" + self.ERROR_STARTING_QUANTIZATION = ( + "양자화를 시작하는 중 오류가 발생했습니다: {0}" + ) self.UPDATING_MODEL_INFO = "모델 정보를 업데이트하는 중입니다: {0}" self.TASK_FINISHED = "작업이 완료되었습니다: {0}" - self.SHOWING_TASK_DETAILS_FOR = "다음에 대한 작업 세부 정보를 표시하는 중입니다: {0}" + self.SHOWING_TASK_DETAILS_FOR = ( + "다음에 대한 작업 세부 정보를 표시하는 중입니다: {0}" + ) self.BROWSING_FOR_IMATRIX_DATA_FILE = "IMatrix 데이터 파일을 찾아보는 중입니다." self.SELECT_DATA_FILE = "데이터 파일 선택" self.BROWSING_FOR_IMATRIX_MODEL_FILE = "IMatrix 모델 파일을 찾아보는 중입니다." @@ -2857,7 +2885,9 @@ def __init__(self): self.STARTING_IMATRIX_GENERATION = "IMatrix 생성을 시작하는 중입니다." self.BACKEND_PATH_NOT_EXIST = "백엔드 경로가 존재하지 않습니다: {0}" self.GENERATING_IMATRIX = "IMatrix를 생성하는 중입니다." - self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix 생성을 시작하는 중 오류가 발생했습니다: {0}" + self.ERROR_STARTING_IMATRIX_GENERATION = ( + "IMatrix 생성을 시작하는 중 오류가 발생했습니다: {0}" + ) self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix 생성 작업이 시작되었습니다." self.ERROR_MESSAGE = "오류: {0}" self.TASK_ERROR = "작업 오류: {0}" @@ -2866,14 +2896,26 @@ def __init__(self): self.SELECT_QUANTIZATION_TYPE = "양자화 유형을 선택하세요." self.ALLOWS_REQUANTIZING = "이미 양자화된 텐서의 재양자화를 허용합니다." self.LEAVE_OUTPUT_WEIGHT = "output.weight를 (재)양자화하지 않은 상태로 둡니다." - self.DISABLE_K_QUANT_MIXTURES = "k-양자 혼합을 비활성화하고 모든 텐서를 동일한 유형으로 양자화합니다." - self.USE_DATA_AS_IMPORTANCE_MATRIX = "양자 최적화를 위한 중요도 행렬로 파일의 데이터를 사용합니다." - self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "이러한 텐서에 중요도 행렬을 사용합니다." - self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "이러한 텐서에 중요도 행렬을 사용하지 않습니다." + self.DISABLE_K_QUANT_MIXTURES = ( + "k-양자 혼합을 비활성화하고 모든 텐서를 동일한 유형으로 양자화합니다." + ) + self.USE_DATA_AS_IMPORTANCE_MATRIX = ( + "양자 최적화를 위한 중요도 행렬로 파일의 데이터를 사용합니다." + ) + self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( + "이러한 텐서에 중요도 행렬을 사용합니다." + ) + self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( + "이러한 텐서에 중요도 행렬을 사용하지 않습니다." + ) self.OUTPUT_TENSOR_TYPE = "출력 텐서 유형:" - self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "output.weight 텐서에 이 유형을 사용합니다." + self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = ( + "output.weight 텐서에 이 유형을 사용합니다." + ) self.TOKEN_EMBEDDING_TYPE = "토큰 임베딩 유형:" - self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "토큰 임베딩 텐서에 이 유형을 사용합니다." + self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = ( + "토큰 임베딩 텐서에 이 유형을 사용합니다." + ) self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = ( "입력과 동일한 샤드에 양자화된 모델을 생성합니다." ) @@ -3828,9 +3870,7 @@ def __init__(self): self.STARTING_IMATRIX_GENERATION = "IMatrix জেনারেশন শুরু হচ্ছে" self.BACKEND_PATH_NOT_EXIST = "ব্যাকএন্ড পাথ বিদ্যমান নেই: {0}" self.GENERATING_IMATRIX = "IMatrix তৈরি করা হচ্ছে" - self.ERROR_STARTING_IMATRIX_GENERATION = ( - "IMatrix জেনারেশন শুরু করতে ত্রুটি: {0}" - ) + self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix জেনারেশন শুরু করতে ত্রুটি: {0}" self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix জেনারেশন টাস্ক শুরু হয়েছে" self.ERROR_MESSAGE = "ত্রুটি: {0}" self.TASK_ERROR = "টাস্ক ত্রুটি: {0}" @@ -3838,11 +3878,13 @@ def __init__(self): self.APPLICATION_CLOSED = "অ্যাপ্লিকেশন বন্ধ" self.SELECT_QUANTIZATION_TYPE = "কোয়ান্টাইজেশন ধরণ নির্বাচন করুন" self.ALLOWS_REQUANTIZING = "যে টেন্সরগুলি ইতিমধ্যে কোয়ান্টাইজ করা হয়েছে তাদের পুনরায় কোয়ান্টাইজ করার অনুমতি দেয়" - self.LEAVE_OUTPUT_WEIGHT = ( - "output.weight কে (পুনরায়) কোয়ান্টাইজ না করে রেখে দেবে" + self.LEAVE_OUTPUT_WEIGHT = "output.weight কে (পুনরায়) কোয়ান্টাইজ না করে রেখে দেবে" + self.DISABLE_K_QUANT_MIXTURES = ( + "k-কোয়ান্ট মিশ্রণগুলি অক্ষম করুন এবং সমস্ত টেন্সরকে একই ধরণের কোয়ান্টাইজ করুন" + ) + self.USE_DATA_AS_IMPORTANCE_MATRIX = ( + "কোয়ান্ট অপ্টিমাইজেশনের জন্য ফাইলের ডেটা গুরুত্বপূর্ণ ম্যাট্রিক্স হিসাবে ব্যবহার করুন" ) - self.DISABLE_K_QUANT_MIXTURES = "k-কোয়ান্ট মিশ্রণগুলি অক্ষম করুন এবং সমস্ত টেন্সরকে একই ধরণের কোয়ান্টাইজ করুন" - self.USE_DATA_AS_IMPORTANCE_MATRIX = "কোয়ান্ট অপ্টিমাইজেশনের জন্য ফাইলের ডেটা গুরুত্বপূর্ণ ম্যাট্রিক্স হিসাবে ব্যবহার করুন" self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( "এই টেন্সরগুলির জন্য গুরুত্বপূর্ণ ম্যাট্রিক্স ব্যবহার করুন" ) @@ -5946,7 +5988,9 @@ def __init__(self): "llama.cpp 二進位檔案已下載並解壓縮至 {0}\nCUDA 檔案已解壓縮至 {1}" ) self.NO_SUITABLE_CUDA_BACKEND_FOUND = "找不到合適的 CUDA 後端進行解壓縮" - self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp 二進位檔案已下載並解壓縮至 {0}" + self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( + "llama.cpp 二進位檔案已下載並解壓縮至 {0}" + ) self.REFRESHING_LLAMACPP_RELEASES = "正在重新整理 llama.cpp 版本" self.UPDATING_ASSET_LIST = "正在更新資源清單" self.UPDATING_CUDA_OPTIONS = "正在更新 CUDA 選項" @@ -6003,14 +6047,18 @@ def __init__(self): self.ALLOWS_REQUANTIZING = "允許重新量化已量化的張量" self.LEAVE_OUTPUT_WEIGHT = "將保留 output.weight 不被（重新）量化" self.DISABLE_K_QUANT_MIXTURES = "停用 k-quant 混合並將所有張量量化為相同類型" - self.USE_DATA_AS_IMPORTANCE_MATRIX = "使用檔案中的資料作為量化最佳化的重要性矩陣" + self.USE_DATA_AS_IMPORTANCE_MATRIX = ( + "使用檔案中的資料作為量化最佳化的重要性矩陣" + ) self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "對這些張量使用重要性矩陣" self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "不要對這些張量使用重要性矩陣" self.OUTPUT_TENSOR_TYPE = "輸出張量類型：" self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "對 output.weight 張量使用此類型" self.TOKEN_EMBEDDING_TYPE = "權杖嵌入類型：" self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "對權杖嵌入張量使用此類型" - self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "將在與輸入相同的分片中產生量化模型" + self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = ( + "將在與輸入相同的分片中產生量化模型" + ) self.OVERRIDE_MODEL_METADATA = "覆蓋模型中繼資料" self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix 產生的輸入資料檔案" self.MODEL_TO_BE_QUANTIZED = "要量化的模型" diff --git a/src/ui_update.py b/src/ui_update.py new file mode 100644 index 0000000..f1fbaf9 --- /dev/null +++ b/src/ui_update.py @@ -0,0 +1,97 @@ +from localizations import * +import psutil + + +def update_model_info(logger, self, model_info): + logger.debug(UPDATING_MODEL_INFO.format(model_info)) + pass + + +def update_system_info(self): + ram = psutil.virtual_memory() + cpu = psutil.cpu_percent() + self.ram_bar.setValue(int(ram.percent)) + self.ram_bar.setFormat( + RAM_USAGE_FORMAT.format( + ram.percent, ram.used // 1024 // 1024, ram.total // 1024 // 1024 + ) + ) + self.cpu_label.setText(CPU_USAGE_FORMAT.format(cpu)) + + +def update_download_progress(self, progress): + self.download_progress.setValue(progress) + + +def update_cuda_backends(self): + self.logger.debug(UPDATING_CUDA_BACKENDS) + self.backend_combo_cuda.clear() + llama_bin = os.path.abspath("llama_bin") + if os.path.exists(llama_bin): + for item in os.listdir(llama_bin): + item_path = os.path.join(llama_bin, item) + if os.path.isdir(item_path) and "cudart-llama" not in item.lower(): + if "cu1" in item.lower(): # Only include CUDA-capable backends + self.backend_combo_cuda.addItem(item, userData=item_path) + + if self.backend_combo_cuda.count() == 0: + self.backend_combo_cuda.addItem(NO_SUITABLE_CUDA_BACKENDS) + self.backend_combo_cuda.setEnabled(False) + else: + self.backend_combo_cuda.setEnabled(True) + + +def update_threads_spinbox(self, value): + self.threads_spinbox.setValue(value) + + +def update_threads_slider(self, value): + self.threads_slider.setValue(value) + + +def update_gpu_offload_spinbox(self, value): + self.gpu_offload_spinbox.setValue(value) + + +def update_gpu_offload_slider(self, value): + self.gpu_offload_slider.setValue(value) + + +def update_cuda_option(self): + self.logger.debug(UPDATING_CUDA_OPTIONS) + asset = self.asset_combo.currentData() + + # Handle the case where asset is None + if asset is None: + self.logger.warning(NO_ASSET_SELECTED_FOR_CUDA_CHECK) + self.cuda_extract_checkbox.setVisible(False) + self.cuda_backend_label.setVisible(False) + self.backend_combo_cuda.setVisible(False) + return # Exit the function early + + is_cuda = asset and "cudart" in asset["name"].lower() + self.cuda_extract_checkbox.setVisible(is_cuda) + self.cuda_backend_label.setVisible(is_cuda) + self.backend_combo_cuda.setVisible(is_cuda) + if is_cuda: + self.update_cuda_backends() + + +def update_assets(self): + self.logger.debug(UPDATING_ASSET_LIST) + self.asset_combo.clear() + release = self.release_combo.currentData() + if release: + if "assets" in release: + for asset in release["assets"]: + self.asset_combo.addItem(asset["name"], userData=asset) + else: + show_error( + self.logger, NO_ASSETS_FOUND_FOR_RELEASE.format(release["tag_name"]) + ) + self.update_cuda_option() + + +def update_base_model_visibility(self, index): + is_gguf = self.lora_output_type_combo.itemText(index) == "GGUF" + self.base_model_wrapper.setVisible(is_gguf)