diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000000000000000000000000000000000000..621a1b944ed2c79eff837b1676dcd2e9d6f97d75 --- /dev/null +++ b/README.en.md @@ -0,0 +1,36 @@ +# ai4c-models + +#### Description +demo AI4Compiler models saved for record + +#### Software Architecture +Software architecture description + +#### Installation + +1. xxxx +2. xxxx +3. xxxx + +#### Instructions + +1. xxxx +2. xxxx +3. xxxx + +#### Contribution + +1. Fork the repository +2. Create Feat_xxx branch +3. Commit your code +4. Create Pull Request + + +#### Gitee Feature + +1. You can use Readme\_XXX.md to support different languages, such as Readme\_en.md, Readme\_zh.md +2. Gitee blog [blog.gitee.com](https://blog.gitee.com) +3. Explore open source project [https://gitee.com/explore](https://gitee.com/explore) +4. The most valuable open source project [GVP](https://gitee.com/gvp) +5. The manual of Gitee [https://gitee.com/help](https://gitee.com/help) +6. The most popular members [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..88946157a1a509f0c5531ac16e53a21b0f040630 --- /dev/null +++ b/README.md @@ -0,0 +1,37 @@ +# ai4c-models + +#### 介绍 +demo AI4Compiler models saved for record + +#### 软件架构 +软件架构说明 + + +#### 安装教程 + +1. xxxx +2. xxxx +3. xxxx + +#### 使用说明 + +1. xxxx +2. xxxx +3. xxxx + +#### 参与贡献 + +1. Fork 本仓库 +2. 新建 Feat_xxx 分支 +3. 提交代码 +4. 新建 Pull Request + + +#### 特技 + +1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md +2. Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com) +3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目 +4. [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目 +5. Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) +6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) diff --git a/dataset_pipeline.ipynb b/dataset_pipeline.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7e7320858e55346efa90eb0c003d5f2692460b83 --- /dev/null +++ b/dataset_pipeline.ipynb @@ -0,0 +1,869 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import tree\n", + "from sklearn.neural_network import MLPClassifier\n", + "import matplotlib.pyplot as plt\n", + "from skl2onnx import convert_sklearn\n", + "from onnxmltools.convert.common.data_types import FloatTensorType\n", + "from sklearn import metrics\n", + "import onnxruntime as rt\n", + "from xgboost import XGBClassifier, to_graphviz\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.compose import ColumnTransformer\n", + "from onnxmltools.convert.common.data_types import FloatTensorType, StringTensorType" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "random.seed(1)\n", + "digits = [f\"{i:d}\" for i in range(0, 10)] + ['a', 'b', 'c', 'd', 'e', 'f']\n", + "data_format = [f\"{i:s}{j:s}\" for i in digits for j in digits]\n", + "\n", + "# Create dataset (later we could randomly generate each row)\n", + "data_list_num = 14 # number of repetitive hexadecimal datasets \n", + "col_num = 4 # number of numeric-data columns\n", + "data_len = 10 # choose `data_len` datasets out of `data_list_num` datasets\n", + "func_num = 200 # number of functions\n", + "data = []\n", + "for i in range(0, data_list_num):\n", + " data.append(sorted(data_format, key=lambda x: random.random()))\n", + "\n", + "data_list_indices = [random.sample(range(0, data_list_num), data_len)\n", + " for _ in range(col_num)]\n", + "df_data = [[] for _ in range(col_num)]\n", + "for i in range(0, col_num):\n", + " for j in range(0, data_len):\n", + " df_data[i].extend(data[data_list_indices[i][j]])\n", + "df = pd.DataFrame(np.array(df_data).T, columns=[\"A\", \"B\", \"C\", \"D\"])\n", + "\n", + "# Convert hexadecimal string to decimal\n", + "def hex2dec(x):\n", + " return int(x, 16)\n", + "\n", + "df_new = df.map(hex2dec)\n", + "\n", + "# Add output column\n", + "df_new[\"Func\"] = [f\"func{i % func_num:d}\" for i in range(len(df))]\n", + "df_new[\"Output\"] = np.where((df.A == \"06\") & (df.B > \"50\") & (df_new.Func > \"func2\"), 1, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C D Func Output\n", + "661 6 169 98 242 func61 1\n", + "1290 6 192 230 94 func90 1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_new[(df_new.A == 6) & (df_new.B > 80) & (df_new.Func > \"func2\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_true = df_new[df_new[\"Output\"] == 1]\n", + "df_false = df_new[df_new[\"Output\"] == 0]\n", + "X_train, X_test, y_train, y_test = train_test_split(df_false[[\"A\", \"B\", \"C\", \"D\", \"Func\"]],\n", + " df_false[\"Output\"],\n", + " random_state=42,\n", + " test_size=0.3)\n", + "X_train = pd.concat([X_train, df_true[[\"A\", \"B\", \"C\", \"D\", \"Func\"]]])\n", + "y_train = pd.concat([y_train, df_true[\"Output\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "numeric_features = [\"A\", \"B\"]\n", + "numeric_transformer = Pipeline(\n", + " steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n", + ")\n", + "\n", + "categorical_features = [\"C\", \"D\", \"Func\"]\n", + "categorical_transformer = Pipeline(\n", + " steps=[(\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))]\n", + ")\n", + "\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " (\"num\", numeric_transformer, numeric_features),\n", + " (\"cat\", categorical_transformer, categorical_features),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('imputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('scaler',\n", + " StandardScaler())]),\n", + " ['A', 'B']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore'))]),\n", + " ['C', 'D', 'Func'])])),\n", + " ('classifier', MLPClassifier())])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_mlp = Pipeline(\n", + " steps=[(\"preprocessor\", preprocessor), (\"classifier\", MLPClassifier())]\n", + ")\n", + "model_mlp.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_pred_mlp = model_mlp.predict(X_test)\n", + "metrics.f1_score(y_test.values.tolist(), test_pred_mlp, average=\"macro\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "onnx_pipeline = convert_sklearn(\n", + " model_mlp,\n", + " initial_types=[(\"A\", FloatTensorType(shape=[None, 1])),\n", + " (\"B\", FloatTensorType(shape=[None, 1])),\n", + " (\"C\", FloatTensorType(shape=[None, 1])),\n", + " (\"D\", FloatTensorType(shape=[None, 1])),\n", + " (\"Func\", StringTensorType(shape=[None, 1]))],\n", + " target_opset={\"\": 12, \"ai.onnx.ml\": 2}\n", + ")\n", + "\n", + "with open(\"mlp_pipeline.onnx\", \"wb\") as f:\n", + " f.write(onnx_pipeline.SerializeToString())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_mlp_onnx = rt.InferenceSession(\"mlp_pipeline.onnx\")\n", + "test_pred_onnx = model_mlp_onnx.run(None, {\"A\": X_test[\"A\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"B\": X_test[\"B\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"C\": X_test[\"C\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"D\": X_test[\"D\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"Func\": X_test[\"Func\"].to_numpy().reshape((-1, 1)).astype(np.str_)})[0]\n", + "metrics.f1_score(y_test.values.tolist(), test_pred_onnx, average=\"macro\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export onnx model as a json file (write scheme for rebuilding onnx model to onnx file in AI4C spec file.)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Method 1 for generating json file\n", + "from google.protobuf.json_format import MessageToJson, Parse\n", + "import onnx\n", + "import json\n", + "\n", + "onnx_model = onnx.load(\"mlp_pipeline.onnx\")\n", + "# Save model to a json file\n", + "onnx_model_message = MessageToJson(onnx_model)\n", + "with open(\"mlp_pipeline.json\", \"w\") as f:\n", + " f.write(onnx_model_message)\n", + "\n", + "# Rebuild onnx file from json file.\n", + "with open(\"mlp_pipeline.json\", \"r\") as f:\n", + " onnx_json = json.loads(f.read())\n", + "onnx_str = json.dumps(onnx_json)\n", + "convert_model = Parse(onnx_str, onnx.ModelProto())\n", + "onnx.save(convert_model, \"mlp_pipeline_json.onnx\")\n", + "\n", + "# Validate newly-generated onnx file.\n", + "model_mlp_onnx_json = rt.InferenceSession(\"mlp_pipeline_json.onnx\")\n", + "\n", + "test_pred_onnx_json = model_mlp_onnx_json.run(None, {\"A\": X_test[\"A\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"B\": X_test[\"B\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"C\": X_test[\"C\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"D\": X_test[\"D\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"Func\": X_test[\"Func\"].to_numpy().reshape((-1, 1)).astype(np.str_)})[0]\n", + "metrics.f1_score(y_test.values.tolist(), test_pred_onnx_json, average=\"macro\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Method 2 for generating json file\n", + "import onnx\n", + "onnx_model = onnx.load(\"mlp_pipeline.onnx\")\n", + "# Save model to a json file.\n", + "onnx.save_model(onnx_model, \"mlp_pipeline.json\", format=\"json\")\n", + "\n", + "# Rebuild onnx file from json file.\n", + "model_onnx_json = onnx.load(\"mlp_pipeline.json\", format=\"json\")\n", + "onnx.save(model_onnx_json, \"mlp_pipeline_json.onnx\")\n", + "\n", + "# Validate newly-generated onnx file.\n", + "model_mlp_onnx_json = rt.InferenceSession(\"mlp_pipeline_json.onnx\")\n", + "\n", + "test_pred_onnx_json = model_mlp_onnx_json.run(None, {\"A\": X_test[\"A\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"B\": X_test[\"B\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"C\": X_test[\"C\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"D\": X_test[\"D\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"Func\": X_test[\"Func\"].to_numpy().reshape((-1, 1)).astype(np.str_)})[0]\n", + "metrics.f1_score(y_test.values.tolist(), test_pred_onnx_json, average=\"macro\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export onnx model as a textproto file (from protobuf)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import onnx\n", + "onnx_model = onnx.load(\"mlp_pipeline.onnx\")\n", + "onnx.save_model(onnx_model, \"mlp_pipeline.textproto\", format=\"textproto\")\n", + "model_textproto = onnx.load(\"mlp_pipeline.textproto\", format=\"textproto\")\n", + "onnx.save(model_textproto, \"mlp_pipeline_textproto.onnx\")\n", + "model_mlp_textproto = rt.InferenceSession(\"mlp_pipeline_textproto.onnx\")\n", + "\n", + "test_pred_onnx_textproto = model_mlp_textproto.run(None, {\"A\": X_test[\"A\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"B\": X_test[\"B\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"C\": X_test[\"C\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"D\": X_test[\"D\"].to_numpy().reshape((-1, 1)).astype(np.float32),\n", + " \"Func\": X_test[\"Func\"].to_numpy().reshape((-1, 1)).astype(np.str_)})[0]\n", + "metrics.f1_score(y_test.values.tolist(), test_pred_onnx_textproto, average=\"macro\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gcc.spec b/gcc.spec index 7cda4fb6a4e2d85b8f1cd0c2674568f0b1011914..015e59cad46728b57ba40c0ed8c986829e0bbb2a 100644 --- a/gcc.spec +++ b/gcc.spec @@ -2,7 +2,7 @@ %global gcc_major 12 # Note, gcc_release must be integer, if you want to add suffixes to # %%{release}, append them after %%{gcc_release} on Release: line. -%global gcc_release 24 +%global gcc_release 25 %global _unpackaged_files_terminate_build 0 %global _performance_build 1 @@ -3060,6 +3060,10 @@ end %doc rpm.doc/changelogs/libcc1/ChangeLog* %changelog +* Tue May 28 2024 Zhengchenhui - 12.3.1-25 +- Type: SPEC +- DESC: test. + * Fri Apr 26 2024 Zhenyu Zhao - 12.3.1-24 - Type: BUGFIX - DESC: Update the configure file for BOLT diff --git a/mlp_pipeline.json b/mlp_pipeline.json new file mode 100644 index 0000000000000000000000000000000000000000..d0b51bb22c3d5d7b46000c6c28ad2354195944d2 --- /dev/null +++ b/mlp_pipeline.json @@ -0,0 +1,72982 @@ +{ + "ir_version": "7", + "producer_name": "skl2onnx", + "producer_version": "1.16.0", + "domain": "ai.onnx", + "model_version": "0", + "doc_string": "", + "graph": { + "node": [ + { + "input": [ + "C" + ], + "output": [ + "Ccast" + ], + "name": "Cast", + "op_type": "Cast", + "attribute": [ + { + "name": "to", + "i": "7", + "type": "INT" + } + ], + "domain": "" + }, + { + "input": [ + "D" + ], + "output": [ + "Dcast" + ], + "name": "Cast1", + "op_type": "Cast", + "attribute": [ + { + "name": "to", + "i": "7", + "type": "INT" + } + ], + "domain": "" + }, + { + "input": [ + "A", + "B" + ], + "output": [ + "merged_columns" + ], + "name": "Concat", + "op_type": 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