Merge 9a9b12b1be into 50413d2d67

scripts/chat_web.py

@@ -16,10 +16,41 @@ python -m scripts.chat_web --num-gpus 4
 To chat, open the URL printed in the console. (If on cloud box, make sure to use public IP)
 
 Endpoints:
-  GET  /           - Chat UI
-  POST /chat/completions - Chat API (streaming only)
-  GET  /health     - Health check with worker pool status
-  GET  /stats      - Worker pool statistics and GPU utilization
+  GET  /                      - Chat UI
+  POST /chat/completions      - Chat API (streaming only, custom format)
+  POST /v1/chat/completions   - OpenAI-compatible chat completions (streaming and non-streaming)
+  GET  /v1/models             - List available models (OpenAI-compatible)
+  GET  /health                - Health check with worker pool status
+  GET  /stats                 - Worker pool statistics and GPU utilization
+
+OpenAI API Compatibility:
+  - Supports both streaming and non-streaming responses
+  - Compatible with OpenAI Python SDK and other OpenAI-compatible clients
+  - Implements standard OpenAI request/response format
+
+Example usage with OpenAI SDK:
+  ```python
+  from openai import OpenAI
+  
+  client = OpenAI(
+      api_key="not-needed",
+      base_url="http://localhost:8000/v1"
+  )
+  
+  response = client.chat.completions.create(
+      model="nanochat",
+      messages=[
+          {"role": "user", "content": "Hello!"}
+      ],
+      temperature=0.8,
+      max_tokens=512,
+      stream=True
+  )
+  
+  for chunk in response:
+      if chunk.choices[0].delta.content:
+          print(chunk.choices[0].delta.content, end="", flush=True)
+  ```
 
 Abuse Prevention:
   - Maximum 500 messages per request
@@ -37,12 +68,13 @@ import torch
 import asyncio
 import logging
 import random
+import time
 from contextlib import asynccontextmanager
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
-from pydantic import BaseModel
-from typing import List, Optional, AsyncGenerator
+from pydantic import BaseModel, Field
+from typing import List, Optional, AsyncGenerator, Literal
 from dataclasses import dataclass
 from contextlib import nullcontext
 from nanochat.common import compute_init, autodetect_device_type
@@ -156,6 +188,50 @@ class ChatRequest(BaseModel):
     temperature: Optional[float] = None
     max_tokens: Optional[int] = None
     top_k: Optional[int] = None
+    model: str = Field(default="nanochat")  # For OpenAI compatibility
+    stream: Optional[bool] = False  # For OpenAI compatibility
+    top_p: Optional[float] = None  # Ignored, for compatibility
+
+# OpenAI API Models
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: str
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+    index: int
+    delta: dict
+    finish_reason: Optional[str] = None
+
+class Usage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: Literal["chat.completion"] = "chat.completion"
+    created: int
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: Usage
+
+class ChatCompletionStreamResponse(BaseModel):
+    id: str
+    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
+    created: int
+    model: str
+    choices: List[ChatCompletionResponseStreamChoice]
+
+class Model(BaseModel):
+    id: str
+    object: Literal["model"] = "model"
+    created: int
+    owned_by: str
+
+class ModelList(BaseModel):
+    object: Literal["list"] = "list"
+    data: List[Model]
 
 def validate_chat_request(request: ChatRequest):
     """Validate chat request to prevent abuse."""
@@ -189,11 +265,12 @@ def validate_chat_request(request: ChatRequest):
         )
 
     # Validate role values
+    valid_roles = ["user", "assistant", "system"]
     for i, message in enumerate(request.messages):
-        if message.role not in ["user", "assistant"]:
+        if message.role not in valid_roles:
             raise HTTPException(
                 status_code=400,
-                detail=f"Message {i} has invalid role. Must be 'user', 'assistant', or 'system'"
+                detail=f"Message {i} has invalid role '{message.role}'. Must be one of: {', '.join(valid_roles)}"
             )
 
     # Validate temperature
@@ -265,8 +342,8 @@ async def generate_stream(
     temperature=None,
     max_new_tokens=None,
     top_k=None
-) -> AsyncGenerator[str, None]:
-    """Generate assistant response with streaming."""
+) -> AsyncGenerator[tuple[str, int], None]:
+    """Generate assistant response with streaming. Returns (text, token_count) tuples."""
     temperature = temperature if temperature is not None else args.temperature
     max_new_tokens = max_new_tokens if max_new_tokens is not None else args.max_tokens
     top_k = top_k if top_k is not None else args.top_k
@@ -278,6 +355,7 @@ async def generate_stream(
     accumulated_tokens = []
     # Track the last complete UTF-8 string (without replacement characters)
     last_clean_text = ""
+    completion_tokens = 0
 
     with worker.autocast_ctx:
         for token_column, token_masks in worker.engine.generate(
@@ -296,6 +374,7 @@ async def generate_stream(
 
             # Append the token to sequence
             accumulated_tokens.append(token)
+            completion_tokens += 1
             # Decode all accumulated tokens to get proper UTF-8 handling
             # Note that decode is a quite efficient operation, basically table lookup and string concat
             current_text = worker.tokenizer.decode(accumulated_tokens)
@@ -305,11 +384,9 @@ async def generate_stream(
                 # Extract only the new text since last clean decode
                 new_text = current_text[len(last_clean_text):]
                 if new_text:  # Only yield if there's new content
-                    yield f"data: {json.dumps({'token': new_text, 'gpu': worker.gpu_id}, ensure_ascii=False)}\n\n"
+                    yield (new_text, completion_tokens)
                     last_clean_text = current_text
 
-    yield f"data: {json.dumps({'done': True})}\n\n"
-
 @app.post("/chat/completions")
 async def chat_completions(request: ChatRequest):
     """Chat completion endpoint (streaming only) - uses worker pool for multi-GPU."""
@@ -352,7 +429,7 @@ async def chat_completions(request: ChatRequest):
         response_tokens = []
         async def stream_and_release():
             try:
-                async for chunk in generate_stream(
+                async for text_chunk, token_count in generate_stream(
                     worker,
                     conversation_tokens,
                     temperature=request.temperature,
@@ -360,10 +437,11 @@ async def chat_completions(request: ChatRequest):
                     top_k=request.top_k
                 ):
                     # Accumulate response for logging
-                    chunk_data = json.loads(chunk.replace("data: ", "").strip())
-                    if "token" in chunk_data:
-                        response_tokens.append(chunk_data["token"])
-                    yield chunk
+                    response_tokens.append(text_chunk)
+                    # Format as custom JSON for web UI
+                    yield f"data: {json.dumps({'token': text_chunk, 'gpu': worker.gpu_id}, ensure_ascii=False)}\n\n"
+                # Send done message
+                yield f"data: {json.dumps({'done': True})}\n\n"
             finally:
                 # Log the assistant response to console
                 full_response = "".join(response_tokens)
@@ -381,6 +459,160 @@ async def chat_completions(request: ChatRequest):
         await worker_pool.release_worker(worker)
         raise e
 
+@app.post("/v1/chat/completions")
+async def openai_chat_completions(request: ChatRequest):
+    """OpenAI-compatible chat completion endpoint (supports streaming and non-streaming).
+    
+    Note: System role messages are accepted but ignored as nanochat doesn't support system prompts.
+    """
+
+    validate_chat_request(request)
+
+    # Log incoming request
+    logger.info(f"OpenAI API Request: model={request.model}, stream={request.stream}, messages={len(request.messages)}")
+
+    worker_pool = app.state.worker_pool
+    worker = await worker_pool.acquire_worker()
+
+    request_id = f"chatcmpl-{random.randint(1000000, 9999999)}"
+    created = int(time.time())
+
+    try:
+        # Build conversation tokens
+        bos = worker.tokenizer.get_bos_token_id()
+        user_start = worker.tokenizer.encode_special("<|user_start|>")
+        user_end = worker.tokenizer.encode_special("<|user_end|>")
+        assistant_start = worker.tokenizer.encode_special("<|assistant_start|>")
+        assistant_end = worker.tokenizer.encode_special("<|assistant_end|>")
+
+        conversation_tokens = [bos]
+        for message in request.messages:
+            if message.role == "user":
+                conversation_tokens.append(user_start)
+                conversation_tokens.extend(worker.tokenizer.encode(message.content))
+                conversation_tokens.append(user_end)
+            elif message.role == "assistant":
+                conversation_tokens.append(assistant_start)
+                conversation_tokens.extend(worker.tokenizer.encode(message.content))
+                conversation_tokens.append(assistant_end)
+            elif message.role == "system":
+                # Note: system messages are ignored as nanochat doesn't have system role support
+                logger.warning(f"System role message ignored (not supported by this model): {message.content[:100]}...")
+
+        conversation_tokens.append(assistant_start)
+        prompt_tokens = len(conversation_tokens)
+
+        if request.stream:
+            # Streaming response
+            async def stream_response():
+                try:
+                    completion_text = ""
+                    completion_tokens = 0
+                    
+                    async for text_chunk, token_count in generate_stream(
+                        worker,
+                        conversation_tokens,
+                        temperature=request.temperature,
+                        max_new_tokens=request.max_tokens,
+                        top_k=request.top_k
+                    ):
+                        completion_text += text_chunk
+                        completion_tokens = token_count
+                        
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            created=created,
+                            model=request.model,
+                            choices=[
+                                ChatCompletionResponseStreamChoice(
+                                    index=0,
+                                    delta={"content": text_chunk},
+                                    finish_reason=None
+                                )
+                            ]
+                        )
+                        yield f"data: {json.dumps(chunk.model_dump(), ensure_ascii=False)}\n\n"
+                    
+                    # Final chunk with finish_reason
+                    final_chunk = ChatCompletionStreamResponse(
+                        id=request_id,
+                        created=created,
+                        model=request.model,
+                        choices=[
+                            ChatCompletionResponseStreamChoice(
+                                index=0,
+                                delta={},
+                                finish_reason="stop"
+                            )
+                        ]
+                    )
+                    yield f"data: {json.dumps(final_chunk.model_dump(), ensure_ascii=False)}\n\n"
+                    yield "data: [DONE]\n\n"
+                    
+                    logger.info(f"OpenAI API Response (GPU {worker.gpu_id}): {completion_tokens} tokens")
+                finally:
+                    await worker_pool.release_worker(worker)
+
+            return StreamingResponse(
+                stream_response(),
+                media_type="text/event-stream"
+            )
+        else:
+            # Non-streaming response
+            try:
+                completion_text = ""
+                completion_tokens = 0
+                
+                async for text_chunk, token_count in generate_stream(
+                    worker,
+                    conversation_tokens,
+                    temperature=request.temperature,
+                    max_new_tokens=request.max_tokens,
+                    top_k=request.top_k
+                ):
+                    completion_text += text_chunk
+                    completion_tokens = token_count
+                
+                response = ChatCompletionResponse(
+                    id=request_id,
+                    created=created,
+                    model=request.model,
+                    choices=[
+                        ChatCompletionResponseChoice(
+                            index=0,
+                            message=ChatMessage(role="assistant", content=completion_text),
+                            finish_reason="stop"
+                        )
+                    ],
+                    usage=Usage(
+                        prompt_tokens=prompt_tokens,
+                        completion_tokens=completion_tokens,
+                        total_tokens=prompt_tokens + completion_tokens
+                    )
+                )
+                
+                logger.info(f"OpenAI API Response (GPU {worker.gpu_id}): {completion_tokens} tokens")
+                return response
+            finally:
+                await worker_pool.release_worker(worker)
+
+    except Exception as e:
+        logger.error(f"Error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/v1/models")
+async def list_models() -> ModelList:
+    """List available models (OpenAI-compatible endpoint)."""
+    return ModelList(
+        data=[
+            Model(
+                id="nanochat",
+                created=int(time.time()),
+                owned_by="nanochat"
+            )
+        ]
+    )
+
 @app.get("/health")
 async def health():
     """Health check endpoint."""
@@ -410,6 +642,6 @@ async def stats():
 
 if __name__ == "__main__":
     import uvicorn
-    print(f"Starting NanoChat Web Server")
+    print("Starting NanoChat Web Server")
     print(f"Temperature: {args.temperature}, Top-k: {args.top_k}, Max tokens: {args.max_tokens}")
     uvicorn.run(app, host=args.host, port=args.port)