Model Explorer
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Select a Family and Task
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+
+
+
+ Family
+ + + + +
+
+ Task
+ + + + +
+
+
+
+ Current model: (none)
+ +
+
+
+
+ Model prompt template
+ +
+ Prompt
+
+
+ Prompt {prompt}
+
+ Model input
+ + +
+
+
+
+ Model output
+Provided input
+ +Generated output
+ +
+
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+
diff --git a/server/model-ui.js b/server/model-ui.js
new file mode 100644
index 0000000..4a4ddc7
--- /dev/null
+++ b/server/model-ui.js
@@ -0,0 +1,167 @@
+window.addEventListener("load", function () {
+ const getFamily = () =>
+ document.querySelector("[type=radio][name=model-family]:checked").value;
+ const getTask = () =>
+ document.querySelector("[type=radio][name=task]:checked").value;
+
+ const testCasesEl = document.getElementById("testCases");
+ const promptsEl = document.getElementById("prompts");
+ const modelPromptEl = document.getElementById("modelPrompt");
+ const modelTestCaseEl = document.getElementById("modelTestCase");
+ const modelInfoEl = document.getElementById("modelInfo");
+ const modelInputEl = document.getElementById("modelInput");
+ const modelInputDisplayEl = document.getElementById("modelInputDisplay");
+ const modelOutputEl = document.getElementById("modelOutput");
+
+ modelInputDisplay.value = "";
+ modelOutputEl.value = "";
+
+ const appTab1 = document.getElementById("appTab1");
+ const appTab2 = document.getElementById("appTab2");
+ const appTab3 = document.getElementById("appTab3");
+
+ appTab1.checked = true;
+
+ const clearChildren = el => {
+ while(el.firstChild) {
+ el.removeChild(el.firstChild);
+ }
+ };
+
+ const popup = (header, text) => {
+ document.getElementById("modalPopup").checked = true;
+ document.getElementById("popupHeader").textContent = header;
+ document.getElementById("popupContent").textContent = text;
+ };
+ const cancelPopup = () => {
+ document.getElementById("modalPopup").checked = false;
+ };
+
+ const updateModelInput = () => {
+ modelInputEl.value = modelPromptEl.value.replace("{prompt}", modelTestCase.value);
+ };
+ modelPromptEl.addEventListener("change", updateModelInput);
+ modelTestCaseEl.addEventListener("change", updateModelInput);
+
+ const createStackElement = params => {
+ let { name, value, label, change, checked } = params;
+ let stackElement = document.createElement("label");
+ stackElement.className = "stack";
+ let input = document.createElement("input");
+ input.type = "radio";
+ input.name = name;
+ input.value = value;
+ input.checked = checked;
+ input.addEventListener("change", change);
+ let span = document.createElement("span");
+ span.className = "button toggle";
+ span.textContent = label;
+ stackElement.appendChild(input);
+ stackElement.appendChild(span);
+ return stackElement;
+ };
+
+ // if refresh during popup, it will carry on
+ cancelPopup();
+
+ document.getElementById("loadCases").addEventListener("click", async () => {
+ popup("Loading...", "Waiting for response from server");
+ const requestData = {
+ family: getFamily(),
+ task: getTask(),
+ };
+ const response = await fetch("/load_cases", {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json"
+ },
+ body: JSON.stringify(requestData),
+ });
+
+ if(!response.ok) {
+ popup("Error!", `Bad network response: ${response.statusText}`);
+ return;
+ }
+
+ const responseData = await response.json();
+
+ let { prompts, test_cases: testCases } = responseData;
+
+ // make stack for prompts
+ clearChildren(promptsEl);
+ const handlePromptChange = function () {
+ modelPromptEl.value = prompts[this.value];
+ updateModelInput();
+ };
+ prompts.forEach((prompt, idx) => {
+ let stackElement = createStackElement({
+ name: "prompt",
+ value: idx,
+ label: `prompt${idx}`,
+ change: handlePromptChange,
+ checked: idx === 0,
+ });
+ promptsEl.appendChild(stackElement);
+ });
+
+ // make stack for test cases
+ clearChildren(testCasesEl);
+ const handleTestCaseChange = function () {
+ modelTestCase.value = testCases[this.value];
+ updateModelInput();
+ };
+ testCases.forEach((testCase, idx) => {
+ let stackElement = createStackElement({
+ name: "testCase",
+ value: idx,
+ label: (idx + 1).toString().padStart(3, 0) + ": " + testCases[idx],
+ change: handleTestCaseChange,
+ checked: idx === 0,
+ });
+ testCasesEl.appendChild(stackElement);
+ });
+
+ for(let el of document.querySelectorAll(".if-model-loaded")) {
+ el.classList.remove("hidden");
+ }
+
+ modelInfoEl.textContent = `CodeGen1-multi-${requestData.family}, ${requestData.task}`;
+ handlePromptChange.call({ value: 0 });
+ handleTestCaseChange.call({ value: 0 });
+ cancelPopup();
+ appTab2.checked = true;
+ });
+
+ document.getElementById("runModel").addEventListener("click", async () => {
+ modelInputDisplayEl.value = modelInputEl.value;
+ modelOutputEl.value = "";
+ appTab3.checked = true;
+ const requestData = {
+ family: getFamily(),
+ task: getTask(),
+ input: modelInputEl.value,
+ };
+ const response = await fetch("/run_single", {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json"
+ },
+ body: JSON.stringify(requestData),
+ });
+
+ if(!response.ok) {
+ popup("Error!", `Bad network response: ${response.statusText}`);
+ return;
+ }
+
+ const responseData = await response.json();
+
+ console.log(responseData);
+ if(responseData.output === "") {
+ modelOutputEl.value = "!! MODEL DID NOT PRODUCE ANY TOKENS IN RESPONSE TO PROMPTING (check server console) !!";
+ }
+ else {
+ modelOutputEl.value = responseData.output;
+ }
+ });
+});
diff --git a/server/server.py b/server/server.py
new file mode 100644
index 0000000..42a9891
--- /dev/null
+++ b/server/server.py
@@ -0,0 +1,82 @@
+import os
+import sys
+from flask import Flask, request, send_from_directory, jsonify
+import random
+# add parent directory to visibile import path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
+sys.path.append(parent_dir)
+from model_wrapper import ModelFamily
+from run_battery import *
+
+app = Flask(__name__)
+
+@app.route("/")
+def serve_html():
+ return send_from_directory(".", "model-ui.html")
+@app.route("/model-ui.js")
+def serve_js():
+ return send_from_directory(".", "model-ui.js")
+
+
+@app.route("/load_cases", methods=["POST"])
+def load_cases():
+ data = request.get_json()
+ if not data:
+ return jsonify({
+ "error": "Invalid input"
+ }), 400
+
+ #family = data["family"]
+ task = data["task"]
+
+ try:
+ config = getattr(BatteryConfigs, task)
+ except:
+ return jsonify({
+ "error": "Could not find task " + task
+ }), 400
+
+ runner = BatteryRunner.of(config)
+ runner.load_cases()
+
+ response = {
+ "prompts": runner.prompts,
+ "test_cases": runner.battery,
+ }
+
+ return jsonify(response)
+
+# cache the model for consecutive runs
+model = None
+@app.route("/run_single", methods=["POST"])
+def run_single():
+ global model
+ data = request.get_json()
+ if not data:
+ return jsonify({
+ "error": "Invalid input"
+ }), 400
+
+ family = data["family"]
+ model_input = data["input"]
+
+ model_name = ModelFamily.CodeGen1.multi[family]
+ if model and model.name != model_name:
+ model.free()
+ model = None
+
+ if model is None:
+ model = Model(model_name)
+ model.configure(time=True)
+
+ output = model.generate_until(model_input, stops=["\n"])
+ del model.inputs
+
+ return jsonify({
+ "output": output,
+ "time_taken": "idk yet"
+ })
+
+if __name__ == "__main__":
+ app.run(port=3337)
\ No newline at end of file
diff --git a/tabulate-results.ipynb b/tabulate-results.ipynb
index 09674d4..5c70ed5 100644
--- a/tabulate-results.ipynb
+++ b/tabulate-results.ipynb
@@ -52,20 +52,21 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"id": "3208f992-2269-4aa9-8b68-5f2b182a9af6",
"metadata": {},
"outputs": [],
"source": [
"to_tabulate = [\n",
" BatteryConfigs.Bugs2Fix,\n",
+ " BatteryConfigs.Bugs2FixChecklist,\n",
" BatteryConfigs.Code2Code,\n",
"]"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 10,
"id": "62848b4b-5708-4163-871f-6eaefd044737",
"metadata": {},
"outputs": [
@@ -73,27 +74,41 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Metric & Prompt & 350M & 2.7B & 6.1B & 16.1B & RMSD & MAE \\\\ \\hline\n",
+ "\\hline Metric & Prompt & 350M & 2.7B & 6.1B & 16.1B & RMSD & MAE \\\\ \\hline \\hline\n",
"EM & prompt0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
"EM & prompt1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
- "BLEU & prompt0 & 0.4882 & 0.5006 & 0.4571 & \\textbf{0.5607} & 0.0252 & 0.0218 \\\\\n",
+ "EM & prompt2 & 0 & 0 & 0 & \\textbf{0.0100} & 0.0015 & 0.0012 \\\\ \\hline\n",
+ "BLEU & prompt0 & 0.4882 & 0.5006 & 0.4571 & 0.5607 & 0.0252 & 0.0218 \\\\\n",
"BLEU & prompt1 & 0.1416 & 0.1801 & 0.2297 & 0.1747 & 0.0307 & 0.0256 \\\\\n",
- "CodeBLEU (Java) & prompt0 & 0.6139 & 0.6458 & 0.5699 & \\textbf{0.6492} & 0.0303 & 0.0247 \\\\\n",
- "CodeBLEU (Java) & prompt1 & 0.1867 & 0.2634 & 0.3111 & 0.2642 & 0.0404 & 0.0369 \\\\\n",
+ "BLEU & prompt2 & 0.4920 & \\textbf{0.5989} & 0.4642 & 0.5689 & 0.0526 & 0.0449 \\\\ \\hline\n",
+ "CodeBLEU (Java) & prompt0 & 0.6134 & 0.6464 & 0.5699 & 0.6492 & 0.0304 & 0.0247 \\\\\n",
+ "CodeBLEU (Java) & prompt1 & 0.1862 & 0.2634 & 0.3111 & 0.2642 & 0.0406 & 0.0371 \\\\\n",
+ "CodeBLEU (Java) & prompt2 & 0.5918 & \\textbf{0.6909} & 0.5499 & 0.6595 & 0.0538 & 0.0455 \\\\ \\hline\n",
"------------------------------\n",
- "Metric & Prompt & 350M & 2.7B & 6.1B & 16.1B & RMSD & MAE \\\\ \\hline\n",
+ "\\hline Metric & Prompt & 350M & 2.7B & 6.1B & 16.1B & RMSD & MAE \\\\ \\hline \\hline\n",
+ "EM & prompt0 & 0.0100 & 0 & 0.0100 & 0.0100 & 0.0041 & 0.0033 \\\\\n",
+ "EM & prompt1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ "EM & prompt2 & 0 & 0 & \\textbf{0.0200} & 0.0100 & 0.0073 & 0.0063 \\\\ \\hline\n",
+ "BLEU & prompt0 & 0.5737 & 0.6004 & 0.5456 & 0.6042 & 0.0219 & 0.0175 \\\\\n",
+ "BLEU & prompt1 & 0.1605 & 0.1759 & 0.3572 & 0.0145 & \\textbf{0.1037} & 0.0890 \\\\\n",
+ "BLEU & prompt2 & 0.5509 & \\textbf{0.6197} & 0.5399 & 0.6149 & 0.0326 & 0.0278 \\\\ \\hline\n",
+ "CodeBLEU (Java) & prompt0 & 0.6532 & 0.6766 & 0.6131 & 0.6867 & 0.0263 & 0.0220 \\\\\n",
+ "CodeBLEU (Java) & prompt1 & 0.1853 & 0.2495 & 0.4194 & 0.0521 & \\textbf{0.1145} & 0.0952 \\\\\n",
+ "CodeBLEU (Java) & prompt2 & 0.6154 & \\textbf{0.6989} & 0.6321 & 0.6983 & 0.0315 & 0.0266 \\\\ \\hline\n",
+ "------------------------------\n",
+ "\\hline Metric & Prompt & 350M & 2.7B & 6.1B & 16.1B & RMSD & MAE \\\\ \\hline \\hline\n",
"EM & prompt0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
"EM & prompt1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
"EM & prompt2 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
- "EM & prompt3 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ "EM & prompt3 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \\hline\n",
"BLEU & prompt0 & 0.1474 & 0.1273 & 0.1394 & \\textbf{0.1491} & 0.0078 & 0.0063 \\\\\n",
"BLEU & prompt1 & 0.1212 & 0.0719 & 0.0693 & 0.1429 & 0.0269 & 0.0254 \\\\\n",
"BLEU & prompt2 & 0.0886 & 0.0364 & 0.0083 & 0.0247 & 0.0245 & 0.0227 \\\\\n",
- "BLEU & prompt3 & 0.0157 & 0.0204 & 0.0209 & 0.0269 & 0.0012 & 0.0009 \\\\\n",
+ "BLEU & prompt3 & 0.0157 & 0.0204 & 0.0209 & 0.0269 & 0.0012 & 0.0009 \\\\ \\hline\n",
"CodeBLEU (C$^\\sharp$) & prompt0 & 0.2291 & 0.2362 & 0.2614 & \\textbf{0.2682} & 0.0076 & 0.0066 \\\\\n",
"CodeBLEU (C$^\\sharp$) & prompt1 & 0.1940 & 0.1594 & 0.1805 & 0.2655 & 0.0201 & 0.0190 \\\\\n",
"CodeBLEU (C$^\\sharp$) & prompt2 & 0.1817 & 0.0942 & 0.0325 & 0.0650 & 0.0446 & 0.0405 \\\\\n",
- "CodeBLEU (C$^\\sharp$) & prompt3 & 0.0793 & 0.0888 & 0.0899 & 0.1058 & 0.0022 & 0.0017 \\\\\n",
+ "CodeBLEU (C$^\\sharp$) & prompt3 & 0.0793 & 0.0888 & 0.0899 & 0.1058 & 0.0022 & 0.0017 \\\\ \\hline\n",
"------------------------------\n"
]
}
@@ -122,10 +137,12 @@
" metric = Metric.from_shortname(metric_shortname)\n",
"\n",
" best_result = None\n",
+ " prompt_count = 0\n",
" for prompt_name, results in data.items():\n",
" max_per = max(results)\n",
" if best_result is None or max_per > best_result:\n",
" best_result = max_per\n",
+ " prompt_count += 1\n",
" \n",
" for prompt_name, results in data.items():\n",
" \n",
@@ -154,8 +171,11 @@
" *emergence_evaluations,\n",
" ])\n",
"\n",
+ " print(\"\\\\hline \", end=\"\")\n",
" for idx, row in enumerate(table):\n",
" row_display = \" & \".join(row) + \" \\\\\\\\\"\n",
+ " if idx % prompt_count == 0:\n",
+ " row_display += \" \\\\hline\"\n",
" if idx == 0:\n",
" row_display += \" \\\\hline\"\n",
" \n",
diff --git a/testing.ipynb b/testing.ipynb
index 28cefc4..e8aa1c9 100644
--- a/testing.ipynb
+++ b/testing.ipynb
@@ -8,10 +8,101 @@
"Generic testing document."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## render testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from render_output import OutputRenderer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import metric"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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