chore: import upstream snapshot with attribution

2026-07-13 12:42:51 +08:00
commit f73e710e38
276 changed files with 36598 additions and 0 deletions
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+# Sanity Checks for Generated Output
+
+This folder provides simple sanity checks on the output generated
+using WebLLM. To try it out, you can do the following steps under this folder
+
+```bash
+npm install
+npm start
+```
+
+Note if you would like to hack WebLLM core package.
+You can change web-llm dependencies as `"file:../.."`, and follow the build from source
+instruction in the project to build webllm locally. This option is only recommended
+if you would like to hack WebLLM core package.
@@ -0,0 +1,20 @@
+{
+  "name": "sanity_checks",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "start": "parcel sanity_checks.html --port 8889",
+    "build": "parcel build sanity_checks.html --dist-dir lib"
+  },
+  "devDependencies": {
+    "buffer": "^5.7.1",
+    "parcel": "^2.8.3",
+    "process": "^0.11.10",
+    "tslib": "^2.3.1",
+    "typescript": "^4.9.5",
+    "url": "^0.11.3"
+  },
+  "dependencies": {
+    "@mlc-ai/web-llm": "^0.2.84"
+  }
+}
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+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>GPU sampleTokenFromLogits Tests</title>
+    <style>
+      body {
+        font-family: Arial, sans-serif;
+        margin: 2em;
+      }
+      .label {
+        margin: 0.5em 0;
+        font-weight: bold;
+      }
+      .result {
+        margin: 0.5em 0 1.5em 0;
+        padding: 0.5em;
+        background: #f4f4f4;
+        border-radius: 4px;
+      }
+      button {
+        padding: 0.5em 1em;
+        font-size: 1em;
+      }
+    </style>
+  </head>
+  <body>
+    <h1>GPU sampleTokenFromLogits Tests</h1>
+    <button id="run-tests">Re-run All Tests</button>
+    <div class="label">Overall:</div>
+    <div id="gpu-test-label" class="result">Not started.</div>
+    <div class="label">Logit Processor:</div>
+    <div id="logit-processor-label" class="result"></div>
+    <div class="label">Logit Bias:</div>
+    <div id="logit-bias-label" class="result"></div>
+    <div class="label">Penalties:</div>
+    <div id="penalty-label" class="result"></div>
+    <div class="label">Logprobs:</div>
+    <div id="logprobs-label" class="result"></div>
+    <script type="module">
+      import "./sanity_checks.ts";
+      document.getElementById("run-tests").onclick = () => {
+        // Reload the module to rerun tests
+        window.location.reload();
+      };
+    </script>
+  </body>
+</html>
@@ -0,0 +1,184 @@
+import * as webllm from "@mlc-ai/web-llm";
+
+function setLabel(id: string, text: string) {
+  const label = document.getElementById(id);
+  if (label == null) return;
+  label.innerText = text;
+}
+
+async function createEngine(
+  modelId: string,
+  appConfig: webllm.AppConfig,
+  logitProcessorRegistry?: Map<string, webllm.LogitProcessor>,
+) {
+  return await webllm.CreateMLCEngine(modelId, {
+    appConfig,
+    logLevel: "ERROR",
+    logitProcessorRegistry,
+  });
+}
+
+async function deleteModel(modelId: string, appConfig: webllm.AppConfig) {
+  await webllm.deleteModelAllInfoInCache(modelId, appConfig);
+}
+
+async function testLogitProcessor(
+  modelId: string,
+  appConfig: webllm.AppConfig,
+) {
+  // Set up a logit processor that sets logits[0] = 100.0, rest -100.0
+  const logitProcessor = {
+    processLogits: (logits: Float32Array) => {
+      logits.fill(-100.0);
+      logits[0] = 100.0;
+      return logits;
+    },
+    processSampledToken: () => {},
+    resetState: () => {},
+  };
+  const logitProcessorRegistry: Map<string, webllm.LogitProcessor> = new Map();
+  logitProcessorRegistry.set(modelId, logitProcessor);
+  const engine: webllm.MLCEngineInterface = await createEngine(
+    modelId,
+    appConfig,
+    logitProcessorRegistry,
+  );
+
+  const prompt = "Test logit processor.";
+  const reply: webllm.ChatCompletion = await engine.chat.completions.create({
+    messages: [{ role: "user", content: prompt }],
+    temperature: 1.0,
+    max_tokens: 20,
+    logprobs: true,
+    top_logprobs: 1,
+  });
+  const logprobs = reply.choices[0]?.logprobs;
+  const logprobsAllZero = !!(
+    logprobs &&
+    Array.isArray(logprobs.content) &&
+    logprobs.content.every(
+      (lp: webllm.ChatCompletionTokenLogprob) =>
+        lp.top_logprobs[0].logprob === 0,
+    )
+  );
+
+  console.log(`[LogitProcessor] Logprobs all zero: ${logprobsAllZero}`);
+  setLabel("logit-processor-label", `Logprobs all zero: ${logprobsAllZero}`);
+  await deleteModel(modelId, appConfig);
+  return logprobsAllZero;
+}
+
+async function testLogitBias(modelId: string, appConfig: webllm.AppConfig) {
+  // Set logit_bias to strongly favor token 0
+  const prompt = "Test logit bias.";
+  const engine: webllm.MLCEngineInterface = await createEngine(
+    modelId,
+    appConfig,
+  );
+  const reply = await engine.chat.completions.create({
+    messages: [{ role: "user", content: prompt }],
+    temperature: 1.0,
+    max_tokens: 20,
+    logprobs: true,
+    top_logprobs: 1,
+    logit_bias: { "0": 100.0 },
+  });
+  const logprobs = reply.choices[0]?.logprobs;
+  const logprobsAllZero = !!(
+    logprobs &&
+    Array.isArray(logprobs.content) &&
+    logprobs.content.every(
+      (lp: webllm.ChatCompletionTokenLogprob) =>
+        lp.top_logprobs[0].logprob === 0,
+    )
+  );
+
+  console.log(`[LogitBias] Logprobs all zero: ${logprobsAllZero}`);
+  setLabel("logit-bias-label", `Logprobs all zero: ${logprobsAllZero}`);
+  await deleteModel(modelId, appConfig);
+  return logprobsAllZero;
+}
+
+async function testPenalties(modelId: string, appConfig: webllm.AppConfig) {
+  const prompt = "Test presence and frequency penalties.";
+  const engine: webllm.MLCEngineInterface = await createEngine(
+    modelId,
+    appConfig,
+  );
+  const reply = await engine.chat.completions.create({
+    messages: [{ role: "user", content: prompt }],
+    temperature: 1.0,
+    max_tokens: 256,
+    presence_penalty: 2.0,
+    frequency_penalty: 2.0,
+    logit_bias: { "0": 100.0 },
+    logprobs: true,
+  });
+  const logprobs = reply.choices[0]?.logprobs;
+  const logprobsNotAllZero = !logprobs?.content?.every(
+    (lp: webllm.ChatCompletionTokenLogprob) => lp.logprob === 0,
+  );
+  console.log(`[Penalties] Logprobs not all zero: ${logprobsNotAllZero}`);
+  setLabel("penalty-label", `Logprobs not all zero: ${logprobsNotAllZero}`);
+  await deleteModel(modelId, appConfig);
+  return logprobsNotAllZero;
+}
+
+async function testLogprobs(modelId: string, appConfig: webllm.AppConfig) {
+  // Test logprobs: check that logprobs are returned and sum to ~1 after exp
+  const prompt = "Test logprobs.";
+  const engine: webllm.MLCEngineInterface = await createEngine(
+    modelId,
+    appConfig,
+  );
+  const reply = await engine.chat.completions.create({
+    messages: [{ role: "user", content: prompt }],
+    temperature: 1.0,
+    max_tokens: 20,
+    logprobs: true,
+    top_logprobs: 5,
+  });
+  const logprobs = reply.choices[0]?.logprobs;
+
+  let logprobsAllCloseTo1 = true;
+  for (const lp of logprobs?.content || []) {
+    const expSum = lp.top_logprobs?.reduce(
+      (acc: number, val: webllm.TopLogprob) => acc + Math.exp(val.logprob),
+      0,
+    );
+    logprobsAllCloseTo1 &&= Math.abs(expSum - 1.0) < 0.1;
+  }
+  console.log(`[Logprobs] Logprobs all close to 1: ${logprobsAllCloseTo1}`);
+  setLabel("logprobs-label", `Logprobs all close to 1: ${logprobsAllCloseTo1}`);
+  await deleteModel(modelId, appConfig);
+  return logprobsAllCloseTo1;
+}
+
+async function main() {
+  const modelId = "Qwen3-0.6B-q0f32-MLC";
+  const appConfig = webllm.prebuiltAppConfig;
+  appConfig.cacheBackend = "indexeddb";
+  setLabel("gpu-test-label", "Running tests...");
+  let passed = 0,
+    total = 0;
+
+  if (await testLogitProcessor(modelId, appConfig)) passed++;
+  total++;
+  if (await testLogitBias(modelId, appConfig)) passed++;
+  total++;
+  if (await testPenalties(modelId, appConfig)) passed++;
+  total++;
+  if (await testLogprobs(modelId, appConfig)) passed++;
+  total++;
+
+  setLabel(
+    "gpu-test-label",
+    `GPU sampleTokenFromLogits tests: ${passed}/${total} passed.`,
+  );
+  setLabel(
+    "gpu-test-label",
+    `Tests complete. Model deleted. ${passed}/${total} passed.`,
+  );
+}
+
+main();