Merge branch 'main' into bugfix/windows_multiple_workers

src/litserve/__about__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = "0.2.6.dev0"
+__version__ = "0.2.6.dev1"
 __author__ = "Lightning-AI et al."
 __author_email__ = "community@lightning.ai"
 __license__ = "Apache-2.0"

src/litserve/api.py

@@ -114,6 +114,7 @@ def device(self, value):
         self._device = value
 
     def _sanitize(self, max_batch_size: int, spec: Optional[LitSpec]):
+        self.max_batch_size = max_batch_size
         if self.stream:
             self._default_unbatch = self._unbatch_stream
         else:

src/litserve/loops.py

@@ -18,8 +18,9 @@
 import sys
 import time
 from abc import ABC
+from dataclasses import dataclass
 from queue import Empty, Queue
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 from fastapi import HTTPException
 from starlette.formparsers import MultiPartParser
@@ -37,10 +38,11 @@
     asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
 
 except ImportError:
-    print(
-        "uvloop is not installed. Falling back to the default asyncio event loop. "
-        "Please install uvloop for better performance using `pip install uvloop`."
-    )
+    if sys.platform != "win32":
+        print(
+            "uvloop is not installed. Falling back to the default asyncio event loop. "
+            "Please install uvloop for better performance using `pip install uvloop`."
+        )
 
 logger = logging.getLogger(__name__)
 
@@ -573,6 +575,238 @@ def __call__(
         )
 
 
+def notify_timed_out_requests(
+    response_queues: List[Queue],
+    timed_out_uids: List[Tuple[int, str]],
+):
+    for response_queue_id, uid in timed_out_uids:
+        logger.error(f"Request {uid} was waiting in the queue for too long and has been timed out.")
+        response_queues[response_queue_id].put((uid, (HTTPException(504, "Request timed out"), LitAPIStatus.ERROR)))
+
+
+@dataclass
+class Output:
+    """Outputs from a single step of the loop."""
+
+    uid: str
+    output: Any
+    status: LitAPIStatus
+
+
+class LitLoop(_BaseLoop):
+    def __init__(self):
+        self._context = {}
+
+    def get_batch_requests(self, lit_api: LitAPI, request_queue: Queue, max_batch_size: int, batch_timeout: float):
+        if max_batch_size <= 1:
+            raise ValueError("max_batch_size must be greater than 1")
+
+        batches, timed_out_uids = collate_requests(
+            lit_api,
+            request_queue,
+            max_batch_size,
+            batch_timeout,
+        )
+        return batches, timed_out_uids
+
+    def get_request(self, request_queue: Queue, timeout: float = 1.0):
+        response_queue_id, uid, timestamp, x_enc = request_queue.get(timeout=timeout)
+        return response_queue_id, uid, timestamp, x_enc
+
+    def populate_context(self, lit_spec: LitSpec, request: Any):
+        if lit_spec and hasattr(lit_spec, "populate_context"):
+            lit_spec.populate_context(self._context, request)
+
+    def put_response(
+        self, response_queues: List[Queue], response_queue_id: int, uid: str, response_data: Any, status: LitAPIStatus
+    ) -> None:
+        response_queues[response_queue_id].put((uid, (response_data, status)))
+
+    def put_error_response(
+        self, response_queues: List[Queue], response_queue_id: int, uid: str, error: Exception
+    ) -> None:
+        response_queues[response_queue_id].put((uid, (error, LitAPIStatus.ERROR)))
+
+
+class ContinuousBatchingLoop(LitLoop):
+    def __init__(self, max_sequence_length: int = 2048):
+        super().__init__()
+        self.active_sequences: Dict[str, Dict] = {}  # uid -> {input, current_length, generated_tokens}
+        self.max_sequence_length = max_sequence_length
+        self.response_queue_ids: Dict[str, int] = {}  # uid -> response_queue_id
+
+    def add_request(self, uid: str, request: Any, lit_api: LitAPI, lit_spec: Optional[LitSpec]) -> None:
+        """Add a new sequence to active sequences."""
+        decoded_request = lit_api.decode_request(request)
+        self.active_sequences[uid] = {"input": decoded_request, "current_length": 0, "generated_tokens": []}
+
+    def mark_completed(self, uid: str) -> None:
+        """Mark a sequence as completed."""
+        logger.info(f"Marking sequence {uid} as completed")
+        del self.active_sequences[uid]
+        del self.response_queue_ids[uid]
+
+    def has_capacity(self, lit_api: LitAPI) -> bool:
+        """Check if we can add more sequences based on current batch."""
+        capacity = len(self.active_sequences) < lit_api.max_batch_size
+        if not capacity:
+            logger.info(
+                f"No capacity: {len(self.active_sequences)} active sequences, max batch size: {lit_api.max_batch_size}"
+            )
+        return capacity
+
+    def step(
+        self, prev_outputs: Optional[List[Output]], lit_api: LitAPI, lit_spec: Optional[LitSpec]
+    ) -> List[Tuple[str, Tuple[Any, LitAPIStatus]]]:
+        """Process one token generation step for all active sequences."""
+        if not self.active_sequences:
+            return []
+
+        # Batch forward pass for all active sequences
+        inputs = [seq["input"] for seq in self.active_sequences.values()]
+        generated = [seq["generated_tokens"] for seq in self.active_sequences.values()]
+
+        try:
+            # Assume lit_api.predict handles batched token generation
+            new_tokens = lit_api.predict(inputs, generated)
+
+            responses = []
+
+            # Process each sequence's new token
+            for uid, token in zip(self.active_sequences.keys(), new_tokens):
+                seq = self.active_sequences[uid]
+                seq["generated_tokens"].append(token)
+                seq["current_length"] += 1
+
+                # Check completion conditions
+                is_finished = lit_api.is_finished(uid, token, self.max_sequence_length)
+
+                if is_finished:
+                    # Encode final response for completed sequence
+                    response = lit_api.encode_response(seq["generated_tokens"])
+                    step_output = Output(uid, response, LitAPIStatus.FINISH_STREAMING)
+                    responses.append(step_output)
+
+            return responses
+
+        except Exception as e:
+            logger.exception("Error during batch token generation")
+            # On error, terminate all active sequences
+            responses = [(uid, (e, LitAPIStatus.ERROR)) for uid in self.active_sequences]
+            self.active_sequences.clear()
+            return responses
+
+    def prefill(
+        self,
+        pending_requests: List[Tuple[str, Any]],
+        lit_api: LitAPI,
+        lit_spec: Optional[LitSpec],
+        request_queue: Queue,
+        max_batch_size: int,
+        response_queues: List[Queue],
+    ) -> List[Tuple[str, Any]]:
+        """Fill available capacity with pending and new requests."""
+        # First process existing pending requests
+        while pending_requests and self.has_capacity(lit_api):
+            response_queue_id, uid, input = pending_requests.pop(0)
+            self.add_request(uid, input, lit_api, lit_spec)
+            self.response_queue_ids[uid] = response_queue_id
+
+        # Then check for new requests if we still have capacity
+        if self.has_capacity(lit_api):
+            new_batches, timed_out_uids = self.get_batch_requests(
+                lit_api, request_queue, max_batch_size, batch_timeout=0.0001
+            )
+            notify_timed_out_requests(response_queues, timed_out_uids)
+
+            if new_batches:
+                # Add new requests to pending_requests and try to process them
+                for response_queue_id, uid, input in new_batches:
+                    logger.info(f"New request: {uid}, {input}")
+                    if self.has_capacity(lit_api):
+                        self.add_request(uid, input, lit_api, lit_spec)
+                        self.response_queue_ids[uid] = response_queue_id
+                    else:
+                        pending_requests.append((response_queue_id, uid, input))
+
+        return pending_requests
+
+    def run(
+        self,
+        lit_api: LitAPI,
+        lit_spec: Optional[LitSpec],
+        device: str,
+        worker_id: int,
+        request_queue: Queue,
+        response_queues: List[Queue],
+        max_batch_size: int,
+        batch_timeout: float,
+        stream: bool,
+        workers_setup_status: Dict[int, str],
+        callback_runner: CallbackRunner,
+    ):
+        if not lit_api.stream:
+            raise ValueError(
+                "Continuous batching loop requires streaming to be enabled. Please set LitServe(..., stream=True)"
+            )
+
+        """Main loop that processes batches of requests."""
+        pending_requests = self.prefill(
+            [],
+            lit_api,
+            lit_spec,
+            request_queue,
+            max_batch_size,
+            response_queues,
+        )
+        try:
+            prev_outputs = None
+            while pending_requests or self.active_sequences:
+                # Process one step for all active sequences
+                responses = self.step(prev_outputs, lit_api, lit_spec)
+                logger.debug(f"Responses from step(): {responses}")
+                if len(responses) == 0:
+                    raise HTTPException(500, "No responses from step()")
+                if responses and not isinstance(responses[0], Output):
+                    raise HTTPException(500, "Expected StepOutput from step()")
+
+                prev_outputs = responses
+
+                # Send responses for all sequences (both streaming and completed)
+                for step_output in responses:
+                    logger.debug(f"Processing response: {step_output}")
+                    status = step_output.status
+                    response_data = step_output.output
+                    uid = step_output.uid
+                    response_queue_id = self.response_queue_ids[uid]
+
+                    if status == LitAPIStatus.ERROR:
+                        self.put_error_response(response_queues, response_queue_id, uid, response_data)
+                        self.mark_completed(uid)
+                    elif status == LitAPIStatus.FINISH_STREAMING:
+                        self.put_response(response_queues, response_queue_id, uid, response_data, status)
+                        self.mark_completed(uid)
+                    else:
+                        self.put_response(response_queues, response_queue_id, uid, response_data, status)
+
+                # Fill available capacity with both pending and new requests
+                pending_requests = self.prefill(
+                    pending_requests,
+                    lit_api,
+                    lit_spec,
+                    request_queue,
+                    max_batch_size,
+                    response_queues,
+                )
+
+        except Exception as e:
+            logger.exception("Error in continuous batching loop")
+            # Handle any errors by sending error responses for all tracked requests
+            for uid, response_queue_id in self.response_queue_ids.items():
+                self.put_error_response(response_queues, response_queue_id, uid, e)
+            self.response_queue_ids.clear()
+
+
 def inference_worker(
     lit_api: LitAPI,
     lit_spec: Optional[LitSpec],