[Grammar] Integrate with XGrammar (#635)

This PR integrates with XGrammar: https://github.com/mlc-ai/xgrammar.

Prior to this PR, grammar is supported by the grammar portion of MLC-LLM
compiled into the model WASM. That portion is now a standalone project
XGrammar. Therefore, this PR adds `mlc-ai/web-xgrammar` as part of the
dependency and remove `src/grammar.ts`. We update `llm_chat.ts`
accordingly for xgrammar's APIs.

In addition, besides `json_schema`, we now also support requests with
EBNF-formatted strings by using the following in the chat completion
request. See `examples/json-schema`'s `ebnfGrammarExample()` for a full
example.

```typescript
    response_format: {
      type: "grammar",
      grammar: jsonGrammarStr,
    } as webllm.ResponseFormat,
```

We also add the following performance info:
- Add `grammar_init_ms` and `grammar_per_token_ms` to
`CompletionUsage.extra` when using grammar
- Add `time_to_first_token_s` (TTFT) and `time_per_output_token_s`
(TPOT), `e2e_latency_s` to `CompletionUsage.extra`

We also add `ignore_eos` to `Completion` and `ChatCompletion` requests,
which can be useful for benchmarking purposes.

examples/json-mode/src/json_mode.ts

@@ -12,7 +12,10 @@ async function main() {
   const initProgressCallback = (report: webllm.InitProgressReport) => {
     setLabel("init-label", report.text);
   };
-  const selectedModel = "Llama-3.1-8B-Instruct-q4f32_1-MLC";
+  // Pick any one of these models to start trying -- most models in WebLLM support grammar
+  const selectedModel = "Llama-3.2-3B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Qwen2.5-1.5B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Phi-3.5-mini-instruct-q4f16_1-MLC";
   const engine: webllm.MLCEngineInterface = await webllm.CreateMLCEngine(
     selectedModel,
     { initProgressCallback: initProgressCallback },

examples/json-schema/src/json_schema.ts

@@ -37,9 +37,14 @@ async function simpleStructuredTextExample() {
   const initProgressCallback = (report: webllm.InitProgressReport) => {
     setLabel("init-label", report.text);
   };
+
+  // Pick any one of these models to start trying -- most models in WebLLM support grammar
+  // const selectedModel = "Llama-3.2-3B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Qwen2.5-1.5B-Instruct-q4f16_1-MLC";
+  const selectedModel = "Phi-3.5-mini-instruct-q4f16_1-MLC";
   const engine: webllm.MLCEngineInterface = await webllm.CreateMLCEngine(
-    "Llama-3.1-8B-Instruct-q4f16_1-MLC",
-    { initProgressCallback: initProgressCallback },
+    selectedModel,
+    { initProgressCallback: initProgressCallback, logLevel: "INFO" },
   );
 
   // Note that you'd need to prompt the model to answer in JSON either in
@@ -106,9 +111,14 @@ async function harryPotterExample() {
     setLabel("init-label", report.text);
   };
 
+  // Pick any one of these models to start trying -- most models in WebLLM support grammar
+  const selectedModel = "Llama-3.2-3B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Qwen2.5-1.5B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Phi-3.5-mini-instruct-q4f16_1-MLC";
+
   const engine: webllm.MLCEngineInterface = await webllm.CreateMLCEngine(
-    "Llama-3.1-8B-Instruct-q4f16_1-MLC",
-    { initProgressCallback: initProgressCallback },
+    selectedModel,
+    { initProgressCallback: initProgressCallback, logLevel: "INFO" },
   );
 
   // Note that you'd need to prompt the model to answer in JSON either in
@@ -134,6 +144,7 @@ async function harryPotterExample() {
   console.log(reply);
   console.log("Output:\n" + (await engine.getMessage()));
   console.log(reply.usage);
+  console.log(reply.usage!.extra);
 }
 
 async function functionCallingExample() {
@@ -214,10 +225,64 @@ async function functionCallingExample() {
   console.log(reply.usage);
 }
 
+async function ebnfGrammarExample() {
+  // You can directly define an EBNFGrammar string with ResponseFormat.grammar
+  const jsonGrammarStr = String.raw`
+root ::= basic_array | basic_object
+basic_any ::= basic_number | basic_string | basic_boolean | basic_null | basic_array | basic_object
+basic_integer ::= ("0" | "-"? [1-9] [0-9]*) ".0"?
+basic_number ::= ("0" | "-"? [1-9] [0-9]*) ("." [0-9]+)? ([eE] [+-]? [0-9]+)?
+basic_string ::= (([\"] basic_string_1 [\"]))
+basic_string_1 ::= "" | [^"\\\x00-\x1F] basic_string_1 | "\\" escape basic_string_1
+escape ::= ["\\/bfnrt] | "u" [A-Fa-f0-9] [A-Fa-f0-9] [A-Fa-f0-9] [A-Fa-f0-9]
+basic_boolean ::= "true" | "false"
+basic_null ::= "null"
+basic_array ::= "[" ("" | ws basic_any (ws "," ws basic_any)*) ws "]"
+basic_object ::= "{" ("" | ws basic_string ws ":" ws basic_any ( ws "," ws basic_string ws ":" ws basic_any)*) ws "}"
+ws ::= [ \n\t]*
+`;
+
+  const initProgressCallback = (report: webllm.InitProgressReport) => {
+    setLabel("init-label", report.text);
+  };
+
+  // Pick any one of these models to start trying -- most models in WebLLM support grammar
+  const selectedModel = "Llama-3.2-3B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Qwen2.5-1.5B-Instruct-q4f16_1-MLC";
+  // const selectedModel = "Phi-3.5-mini-instruct-q4f16_1-MLC";
+  const engine: webllm.MLCEngineInterface = await webllm.CreateMLCEngine(
+    selectedModel,
+    { initProgressCallback: initProgressCallback, logLevel: "INFO" },
+  );
+
+  // Note that you'd need to prompt the model to answer in JSON either in
+  // user's message or the system prompt
+  const request: webllm.ChatCompletionRequest = {
+    stream: false, // works with streaming, logprobs, top_logprobs as well
+    messages: [
+      {
+        role: "user",
+        content: "Introduce yourself in JSON",
+      },
+    ],
+    max_tokens: 128,
+    response_format: {
+      type: "grammar",
+      grammar: jsonGrammarStr,
+    } as webllm.ResponseFormat,
+  };
+
+  const reply0 = await engine.chatCompletion(request);
+  console.log(reply0);
+  console.log("Output:\n" + (await engine.getMessage()));
+  console.log(reply0.usage);
+}
+
 async function main() {
   // await simpleStructuredTextExample();
-  // await harryPotterExample();
-  await functionCallingExample();
+  await harryPotterExample();
+  // await functionCallingExample();
+  // await ebnfGrammarExample();
 }
 
 main();

package.json

@@ -51,6 +51,7 @@
     "ts-jest": "^29.1.2",
     "tslib": "^2.3.1",
     "@mlc-ai/web-runtime": "0.18.0-dev2",
+    "@mlc-ai/web-xgrammar": "../xgrammar/web",
     "typescript": "^4.9.5"
   },
   "dependencies": {

src/config.ts

@@ -127,6 +127,7 @@ export interface MLCEngineConfig {
 export interface GenerationConfig {
   // Only used in MLC
   repetition_penalty?: number;
+  ignore_eos?: boolean;
   // Shared by MLC and OpenAI APIs
   top_p?: number | null;
   temperature?: number | null;

src/conversation.ts

@@ -257,10 +257,12 @@ export class Conversation {
   }
 
   getStopStr(): string[] {
-    if (this.config.stop_str.length > 0) {
-      return this.config.stop_str;
-    }
-    return [this.config.seps[this.config.seps.length - 1]];
+    // TODO(Charlie): Is this needed?
+    // if (this.config.stop_str.length > 0) {
+    //   return this.config.stop_str;
+    // }
+    // return [this.config.seps[this.config.seps.length - 1]];
+    return this.config.stop_str;
   }
 
   getStopTokens() {

src/engine.ts

@@ -465,20 +465,23 @@ export class MLCEngine implements MLCEngineInterface {
     pipeline: LLMChatPipeline,
     chatConfig: ChatConfig,
     genConfig: GenerationConfig,
+    timeReceived: number,
   ): AsyncGenerator<ChatCompletionChunk, void, void>;
   asyncGenerate(
     request: CompletionCreateParamsStreaming,
     model: string,
     pipeline: LLMChatPipeline,
     chatConfig: ChatConfig,
     genConfig: GenerationConfig,
+    timeReceived: number,
   ): AsyncGenerator<Completion, void, void>;
   async *asyncGenerate(
     request: ChatCompletionRequestStreaming | CompletionCreateParamsStreaming,
     model: string,
     pipeline: LLMChatPipeline,
     chatConfig: ChatConfig,
     genConfig: GenerationConfig,
+    timeReceived: number,
   ): AsyncGenerator<ChatCompletionChunk | Completion, void, void> {
     // Since it is an async generator, we need to do fine-grained try-catch to ensure lock is
     // released only when errors occur. Then release at the very end when no error occurs.
@@ -678,18 +681,39 @@ export class MLCEngine implements MLCEngineInterface {
 
     // 4. Usage chunk
     if (request.stream_options?.include_usage) {
+      const usedGrammar =
+        "response_format" in request &&
+        (request.response_format?.type === "grammar" ||
+          request.response_format?.type === "json_object");
       const completion_tokens = pipeline.getCurRoundDecodingTotalTokens();
       const prompt_tokens = pipeline.getCurRoundPrefillTotalTokens();
       const prefill_tokens_per_s = pipeline.getCurRoundPrefillTokensPerSec();
       const decode_tokens_per_s = pipeline.getCurRoundDecodingTokensPerSec();
+      const grammar_init_s = pipeline.getCurRoundGrammarInitTotalTime();
+      const prefill_time = pipeline.getCurRoundPrefillTotalTime();
+      const decode_time = pipeline.getCurRoundDecodingTotalTime();
+      const grammar_per_token_s =
+        pipeline.getCurRoundGrammarPerTokenTotalTime();
+      const defaultExtra = {
+        e2e_latency_s: (Date.now() - timeReceived) / 1000,
+        prefill_tokens_per_s: prefill_tokens_per_s,
+        decode_tokens_per_s: decode_tokens_per_s,
+        time_to_first_token_s: prefill_time,
+        time_per_output_token_s: decode_time / completion_tokens,
+      };
       const usage: CompletionUsage = {
         completion_tokens: completion_tokens,
         prompt_tokens: prompt_tokens,
         total_tokens: completion_tokens + prompt_tokens,
-        extra: {
-          prefill_tokens_per_s: prefill_tokens_per_s,
-          decode_tokens_per_s: decode_tokens_per_s,
-        },
+        extra: usedGrammar
+          ? {
+              ...defaultExtra,
+              ...{
+                grammar_init_s: grammar_init_s,
+                grammar_per_token_s: grammar_per_token_s / completion_tokens,
+              },
+            }
+          : defaultExtra,
       };
       if (isChatCompletion) {
         const usageChunk: ChatCompletionChunk = {
@@ -745,6 +769,7 @@ export class MLCEngine implements MLCEngineInterface {
   async chatCompletion(
     request: ChatCompletionRequest,
   ): Promise<AsyncIterable<ChatCompletionChunk> | ChatCompletion> {
+    const timeReceived = Date.now();
     // 0. Check model loaded and preprocess inputs
     const [selectedModelId, selectedPipeline, selectedChatConfig] =
       this.getLLMStates("ChatCompletionRequest", request.model);
@@ -766,6 +791,7 @@ export class MLCEngine implements MLCEngineInterface {
       logprobs: request.logprobs,
       top_logprobs: request.top_logprobs,
       response_format: request.response_format,
+      ignore_eos: request.ignore_eos,
     };
 
     // 0.5 Block wait until this pipeline finishes all previous requests
@@ -780,6 +806,7 @@ export class MLCEngine implements MLCEngineInterface {
         selectedPipeline,
         selectedChatConfig,
         genConfig,
+        timeReceived,
       );
     }
 
@@ -796,6 +823,8 @@ export class MLCEngine implements MLCEngineInterface {
       let prompt_tokens = 0;
       let prefill_time = 0;
       let decode_time = 0;
+      let grammar_init_s = 0;
+      let grammar_per_token_s = 0;
       for (let i = 0; i < n; i++) {
         let outputMessage: string;
         if (this.interruptSignal) {
@@ -852,8 +881,21 @@ export class MLCEngine implements MLCEngineInterface {
         prompt_tokens += selectedPipeline.getCurRoundPrefillTotalTokens();
         prefill_time += selectedPipeline.getCurRoundPrefillTotalTime();
         decode_time += selectedPipeline.getCurRoundDecodingTotalTime();
+        grammar_init_s += selectedPipeline.getCurRoundGrammarInitTotalTime();
+        grammar_per_token_s +=
+          selectedPipeline.getCurRoundGrammarPerTokenTotalTime();
       }
-
+      const usedGrammar =
+        "response_format" in request &&
+        (request.response_format?.type === "grammar" ||
+          request.response_format?.type === "json_object");
+      const defaultExtra = {
+        e2e_latency_s: (Date.now() - timeReceived) / 1000,
+        prefill_tokens_per_s: prompt_tokens / prefill_time,
+        decode_tokens_per_s: completion_tokens / decode_time,
+        time_to_first_token_s: prefill_time,
+        time_per_output_token_s: decode_time / completion_tokens,
+      };
       const response: ChatCompletion = {
         id: crypto.randomUUID(),
         choices: choices,
@@ -864,10 +906,15 @@ export class MLCEngine implements MLCEngineInterface {
           completion_tokens: completion_tokens,
           prompt_tokens: prompt_tokens,
           total_tokens: completion_tokens + prompt_tokens,
-          extra: {
-            prefill_tokens_per_s: prompt_tokens / prefill_time,
-            decode_tokens_per_s: completion_tokens / decode_time,
-          },
+          extra: usedGrammar
+            ? {
+                ...defaultExtra,
+                ...{
+                  grammar_init_s: grammar_init_s,
+                  grammar_per_token_s: grammar_per_token_s / completion_tokens,
+                },
+              }
+            : defaultExtra,
         } as CompletionUsage,
       };
 
@@ -901,6 +948,8 @@ export class MLCEngine implements MLCEngineInterface {
   async completion(
     request: CompletionCreateParams,
   ): Promise<AsyncIterable<Completion> | Completion> {
+    const timeReceived = Date.now();
+
     // 0. Check model loaded and preprocess inputs
     const [selectedModelId, selectedPipeline, selectedChatConfig] =
       this.getLLMStates("CompletionCreateParams", request.model);
@@ -915,6 +964,7 @@ export class MLCEngine implements MLCEngineInterface {
       logit_bias: request.logit_bias,
       logprobs: request.logprobs,
       top_logprobs: request.top_logprobs,
+      ignore_eos: request.ignore_eos,
     };
 
     // 0.5 Block wait until this pipeline finishes all previous requests
@@ -929,6 +979,7 @@ export class MLCEngine implements MLCEngineInterface {
         selectedPipeline,
         selectedChatConfig,
         genConfig,
+        timeReceived,
       );
     }
 
@@ -989,8 +1040,11 @@ export class MLCEngine implements MLCEngineInterface {
           prompt_tokens: prompt_tokens,
           total_tokens: completion_tokens + prompt_tokens,
           extra: {
+            e2e_latency_s: (Date.now() - timeReceived) / 1000,
             prefill_tokens_per_s: prompt_tokens / prefill_time,
             decode_tokens_per_s: completion_tokens / decode_time,
+            time_to_first_token_s: prefill_time,
+            time_per_output_token_s: decode_time / completion_tokens,
           },
         } as CompletionUsage,
       };