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vm: add dynamic opcode pricing #4087

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2 changes: 1 addition & 1 deletion cli/vm/cli_test.go
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Original file line number Diff line number Diff line change
Expand Up @@ -1323,7 +1323,7 @@ func TestLoadtx(t *testing.T) {
e.runProg(t,
"loadtx "+tx.Hash().StringLE(), // hash LE
"run",
"loadtx --gas 10000 "+tx.Hash().StringLE(), // with GAS
"loadtx --gas 100 "+tx.Hash().StringLE(), // with GAS
"run",
"loadtx 0x"+tx.Hash().StringLE(), // hash LE with 0x prefix
"run",
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1 change: 1 addition & 0 deletions go.mod
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@ require (
golang.org/x/term v0.35.0
golang.org/x/text v0.29.0
golang.org/x/tools v0.37.0
gonum.org/v1/gonum v0.16.0
gopkg.in/yaml.v3 v3.0.1
)

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132 changes: 132 additions & 0 deletions opcodefee/append.ipynb
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@@ -0,0 +1,132 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('opcode_results/APPEND.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def plot_target_vs_each_feature(X: pd.DataFrame, y: pd.Series):\n",
" y_vals = y.values.reshape(-1)\n",
" for col in X.columns:\n",
" x_vals = X[col].values\n",
" plt.figure(figsize=(8, 5))\n",
" plt.scatter(x_vals, y_vals)\n",
" plt.xlabel(col)\n",
" plt.ylabel(\"ns\")\n",
" plt.title(f\"ns vs {col}\")\n",
" plt.grid(True)\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def analytic_linear_regression(X: pd.DataFrame, y: pd.Series) -> np.ndarray:\n",
" X_mat = X.values\n",
" y_vec = y.values\n",
"\n",
" n = X_mat.shape[0]\n",
" X_design = np.hstack([X_mat, np.ones((n, 1))])\n",
"\n",
" beta, _, _, _ = np.linalg.lstsq(X_design, y_vec, rcond=None)\n",
"\n",
" return beta"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def myfunc(df_type: pd.DataFrame):\n",
" X = df_type[[\"refcount\"]]\n",
" y = df_type[\"ns\"]\n",
"\n",
" plot_target_vs_each_feature(X, y)\n",
" \n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.3, random_state=42\n",
" )\n",
" beta = analytic_linear_regression(X_train, y_train)\n",
" \n",
" X_test_mat = X_test.values\n",
" X_test_design = np.hstack([X_test_mat, np.ones((X_test_mat.shape[0], 1))])\n",
" y_pred = X_test_design @ beta\n",
" r2 = r2_score(y_test, y_pred)\n",
" \n",
" return beta, r2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"beta, r2 = myfunc(df[df[\"appendtype\"] == \"Array\"])\n",
"print(beta)\n",
"print(r2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"beta, r2 = myfunc(df[df[\"appendtype\"] == \"Struct\"])\n",
"print(beta)\n",
"print(r2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "opcode",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
121 changes: 121 additions & 0 deletions opcodefee/cat.ipynb
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@@ -0,0 +1,121 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('opcode_results/CAT.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def plot_target_vs_each_feature(X: pd.DataFrame, y: pd.Series):\n",
" y_vals = y.values.reshape(-1)\n",
" for col in X.columns:\n",
" x_vals = X[col].values\n",
" plt.figure(figsize=(8, 5))\n",
" plt.scatter(x_vals, y_vals)\n",
" plt.xlabel(col)\n",
" plt.ylabel(\"ns\")\n",
" plt.title(f\"ns vs {col}\")\n",
" plt.grid(True)\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def analytic_linear_regression(X: pd.DataFrame, y: pd.Series) -> np.ndarray:\n",
" X_mat = X.values\n",
" y_vec = y.values\n",
"\n",
" n = X_mat.shape[0]\n",
" X_design = np.hstack([X_mat, np.ones((n, 1))])\n",
"\n",
" beta, _, _, _ = np.linalg.lstsq(X_design, y_vec, rcond=None)\n",
"\n",
" return beta"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def myfunc(df_type: pd.DataFrame):\n",
" X = df_type[[\"numbytes\"]]\n",
" y = df_type[\"ns\"]\n",
"\n",
" plot_target_vs_each_feature(X, y)\n",
" \n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.3, random_state=42\n",
" )\n",
" beta = analytic_linear_regression(X_train, y_train)\n",
" \n",
" X_test_mat = X_test.values\n",
" X_test_design = np.hstack([X_test_mat, np.ones((X_test_mat.shape[0], 1))])\n",
" y_pred = X_test_design @ beta\n",
" r2 = r2_score(y_test, y_pred)\n",
" \n",
" return beta, r2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"beta, r2 = myfunc(df)\n",
"print(beta)\n",
"print(r2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "opcode",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
121 changes: 121 additions & 0 deletions opcodefee/clear.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,121 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('opcode_results/CLEAR.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def plot_target_vs_each_feature(X: pd.DataFrame, y: pd.Series):\n",
" y_vals = y.values.reshape(-1)\n",
" for col in X.columns:\n",
" x_vals = X[col].values\n",
" plt.figure(figsize=(8, 5))\n",
" plt.scatter(x_vals, y_vals)\n",
" plt.xlabel(col)\n",
" plt.ylabel(\"ns\")\n",
" plt.title(f\"ns vs {col}\")\n",
" plt.grid(True)\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def analytic_linear_regression(X: pd.DataFrame, y: pd.Series) -> np.ndarray:\n",
" X_mat = X.values\n",
" y_vec = y.values\n",
"\n",
" n = X_mat.shape[0]\n",
" X_design = np.hstack([X_mat, np.ones((n, 1))])\n",
"\n",
" beta, _, _, _ = np.linalg.lstsq(X_design, y_vec, rcond=None)\n",
"\n",
" return beta"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def myfunc(df_type: pd.DataFrame):\n",
" X = df_type[[\"refcount\", \"stacksize\"]]\n",
" y = df_type[\"ns\"]\n",
"\n",
" plot_target_vs_each_feature(X, y)\n",
" \n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.3, random_state=42\n",
" )\n",
" beta = analytic_linear_regression(X_train, y_train)\n",
" \n",
" X_test_mat = X_test.values\n",
" X_test_design = np.hstack([X_test_mat, np.ones((X_test_mat.shape[0], 1))])\n",
" y_pred = X_test_design @ beta\n",
" r2 = r2_score(y_test, y_pred)\n",
" \n",
" return beta, r2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"beta, r2 = myfunc(df)\n",
"print(beta)\n",
"print(r2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "opcode",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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