[Engine] Revert TTFT optimize (#6680) and add EP batched token scheduler

docs/usage/environment_variables.md

@@ -156,6 +156,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Whether to enable the decode caches requests for preallocating resource
     "FD_ENABLE_CACHE_TASK": lambda: os.getenv("FD_ENABLE_CACHE_TASK", "0"),
 
+    # Batched token timeout in EP
+    "FD_EP_BATCHED_TOKEN_TIMEOUT": lambda: float(os.getenv("FD_EP_BATCHED_TOKEN_TIMEOUT", "0.1")),
+
     # Max pre-fetch requests number in PD
     "FD_EP_MAX_PREFETCH_TASK_NUM": lambda: int(os.getenv("FD_EP_MAX_PREFETCH_TASK_NUM", "8")),
 

docs/zh/usage/environment_variables.md

@@ -156,6 +156,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # 是否启用 decode 缓存请求以预分配资源
     "FD_ENABLE_CACHE_TASK": lambda: os.getenv("FD_ENABLE_CACHE_TASK", "0"),
 
+    # EP 中批处理 token 的超时时间
+    "FD_EP_BATCHED_TOKEN_TIMEOUT": lambda: float(os.getenv("FD_EP_BATCHED_TOKEN_TIMEOUT", "0.1")),
+
     # PD 中最大预取请求数量
     "FD_EP_MAX_PREFETCH_TASK_NUM": lambda: int(os.getenv("FD_EP_MAX_PREFETCH_TASK_NUM", "8")),
 

fastdeploy/engine/common_engine.py

@@ -392,15 +392,6 @@ def _init_worker_monitor_signals(self):  # exist_task_signal 用于各worker进
             create=True,
         )
 
-        engine_forward_signal_data = np.zeros([1], dtype=np.int32)
-        self.engine_forward_signal = IPCSignal(
-            name="engine_forward_signal",
-            array=engine_forward_signal_data,
-            dtype=np.int32,
-            suffix=current_suffix,
-            create=True,
-        )
-
         # worker_live_signal 用于engine感知各worker进程是否存活，记录每个step 时间
         worker_healthy_live_recorded_time_array = np.zeros(
             shape=[min(self.cfg.worker_num_per_node, self.cfg.parallel_config.tensor_parallel_size)], dtype=np.int32
@@ -1091,29 +1082,26 @@ def _fetch_request():
             with self._pause_cond:
                 self._pause_cond.wait_for(lambda: not self.is_paused)
             try:
-                if not is_fetching:
-                    # Check if the thread pool is still available to avoid submitting tasks to a shutdown thread pool.
-                    try:
+                if self.engine_worker_queue.exist_tasks():
+                    time.sleep(0.001)
+                    continue
+                if self.cfg.scheduler_config.splitwise_role != "mixed":
+                    if not is_fetching:
                         is_fetching = True
                         get_request_pool.submit(_fetch_request)
-                    except RuntimeError as e:
-                        if "shutdown" in str(e):
-                            self.llm_logger.info("Thread pool shutdown detected, exiting scheduler loop")
-                            break
-                        else:
-                            raise
-                if self.cfg.scheduler_config.splitwise_role != "mixed":
-                    # Continue preprocessing incoming requests and accumulating them in the queue when forward pass not finished.
-                    # Once the forward pass finishes, these accumulated requests can be scheduled in larger,
-                    # more efficient batches.
-                    if self.engine_worker_queue.exist_tasks() or self.engine_forward_signal.value[0] != 0:
-                        time.sleep(0.001)
-                        continue
+
                 else:
-                    # In mixed, todo: optimze cache swap, to decouple swap from scheduler
-                    if self.engine_worker_queue.exist_tasks():
-                        time.sleep(0.001)
-                        continue
+                    if len(self.resource_manager.waiting) == 0 and (not is_fetching):
+                        # Check if the thread pool is still available to avoid submitting tasks to a shutdown thread pool.
+                        try:
+                            is_fetching = True
+                            get_request_pool.submit(_fetch_request)
+                        except RuntimeError as e:
+                            if "shutdown" in str(e):
+                                self.llm_logger.info("Thread pool shutdown detected, exiting scheduler loop")
+                                break
+                            else:
+                                raise
 
                 if hasattr(self.resource_manager, "scheduler_unhandled_request_num"):
                     self.resource_manager.scheduler_unhandled_request_num = self._get_scheduler_unhandled_request_num()
@@ -1178,13 +1166,6 @@ def _fetch_request():
                             elif not task.has_been_preempted_before:
                                 task.metrics.inference_start_time = time.time()
                     self.engine_worker_queue.put_tasks((batch_request, self.resource_manager.real_bsz))
-                else:
-                    # When there are no actual tasks to schedule, send an empty task batch to EP workers.
-                    # This helps EP workers barrier for syncing tasks not hang.
-                    if self.cfg.parallel_config.enable_expert_parallel:
-                        self.engine_worker_queue.put_tasks(
-                            (batch_request, self.resource_manager.real_bsz)
-                        )  # Empty (as idle tasks for ep)
 
                 # 4. Response error tasks
                 if error_tasks:

fastdeploy/envs.py

@@ -143,6 +143,8 @@ def _validate_split_kv_size(value: int) -> int:
     "FD_ZMQ_CONTROL_CMD_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_CONTROL_CMD_SERVER_PORTS", "8202"),
     # Whether to enable the decode caches requests for preallocating resource
     "FD_ENABLE_CACHE_TASK": lambda: os.getenv("FD_ENABLE_CACHE_TASK", "0"),
+    # Batched token timeout in EP
+    "FD_EP_BATCHED_TOKEN_TIMEOUT": lambda: float(os.getenv("FD_EP_BATCHED_TOKEN_TIMEOUT", "0.1")),
     # Max pre-fetch requests number in PD
     "FD_EP_MAX_PREFETCH_TASK_NUM": lambda: int(os.getenv("FD_EP_MAX_PREFETCH_TASK_NUM", "8")),
     # Enable or disable model caching.

fastdeploy/scheduler/dp_scheduler.py

@@ -24,7 +24,7 @@
 from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.scheduler.data import ScheduledResponse
 from fastdeploy.scheduler.local_scheduler import LocalScheduler
-from fastdeploy.utils import get_logger
+from fastdeploy.utils import envs, get_logger
 
 
 class DPLocalScheduler(LocalScheduler):
@@ -136,19 +136,52 @@ def get_requests(
         Returns:
             List of Request objects ready for processing
         """
-        # DP scheduler is used in V1, there is no need to manage request fetching in the scheduler, resource_manager_v1 will do that.
+        if available_blocks <= reserved_output_blocks or batch < 1:
+            self.scheduler_logger.debug(
+                f"Scheduler's resource are insufficient: available_blocks={available_blocks} "
+                f"reserved_output_blocks={reserved_output_blocks} batch={batch} "
+                f"max_num_batched_tokens={max_num_batched_tokens}"
+            )
+            return []
+        required_total_blocks = 0
+        current_prefill_tokens = 0
+        start_batch_time = time.time()
         requests: List[Request] = []
 
         with self.requests_not_empty:
-            batch_ids = self.requests_not_empty.wait_for(
-                lambda: self.ids[self.ids_read_cursor : self.ids_read_cursor + 1],
-                0.005,
-            )
-            if batch_ids:
-                for request_id in batch_ids:
-                    request = self.requests[request_id]
-                    requests.append(request.raw)
-                    self.ids_read_cursor += 1
+            while True:
+                batch_ids = self.requests_not_empty.wait_for(
+                    lambda: self.ids[self.ids_read_cursor : self.ids_read_cursor + batch],
+                    0.005,
+                )
+                if batch_ids:
+                    for request_id in batch_ids:
+                        request = self.requests[request_id]
+                        required_input_blocks = self.calc_required_blocks(request.prompt_tokens_ids_len, block_size)
+                        current_prefill_tokens += request.prompt_tokens_ids_len
+                        required_total_blocks += required_input_blocks + reserved_output_blocks
+                        if required_total_blocks > available_blocks:
+                            break
+
+                        requests.append(request.raw)
+                        self.ids_read_cursor += 1
+                        start_batch_time = time.time()
+                        if current_prefill_tokens > max_num_batched_tokens:
+                            break
+                        if len(requests) >= batch:
+                            break
+                if (
+                    (current_prefill_tokens > max_num_batched_tokens)
+                    or (len(requests) >= batch)
+                    or (time.time() - start_batch_time > envs.FD_EP_BATCHED_TOKEN_TIMEOUT)
+                ):
+                    break
+
+        if batch_ids:
+            if len(batch_ids) > 0 and len(requests) == 0:
+                self.scheduler_logger.debug(
+                    f"Scheduler has put all just-pulled request into the queue: {len(batch_ids)}"
+                )
 
         if len(requests) > 0:
             log_request(

fastdeploy/splitwise/internal_adapter_utils.py

@@ -53,9 +53,6 @@ def _get_current_server_info(self):
         available_batch_size = min(self.cfg.max_prefill_batch, self.engine.resource_manager.available_batch())
 
         available_block_num = self.engine.resource_manager.available_block_num()
-        unhandled_request_num = self.engine.scheduler.get_unhandled_request_num()
-        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
-            unhandled_request_num = max(unhandled_request_num, len(self.engine.resource_manager.waiting))
         server_info = {
             "splitwise_role": self.cfg.scheduler_config.splitwise_role,
             "block_size": int(self.cfg.cache_config.block_size),
@@ -65,7 +62,7 @@ def _get_current_server_info(self):
             "available_resource": float(1.0 * available_block_num / self.cfg.cache_config.total_block_num),
             "max_batch_size": int(available_batch_size),
             "max_input_token_num": self.cfg.model_config.max_model_len,
-            "unhandled_request_num": unhandled_request_num,
+            "unhandled_request_num": self.engine.scheduler.get_unhandled_request_num(),
             "available_batch": int(self.engine.resource_manager.available_batch()),
         }
         return server_info

fastdeploy/worker/worker_process.py

@@ -291,20 +291,6 @@ def init_health_status(self) -> None:
             create=False,
         )
 
-        # init engine forward signal
-        # If engine is being forward, engine_forward_signal_data should be 1.
-        # If engine is out of forward, engine_forward_signal_data should be 0.
-        # In pd disaggregation + EP parallel, only when engine is out of forward, scheduler send next batch to worker.
-        # When engine is out of forward, engine_forward_signal_data must be 0, otherwise scheduler will not schedule next batch.
-        engine_forward_signal_data = np.zeros([1], dtype=np.int32)
-        self.engine_forward_signal = IPCSignal(
-            name="engine_forward_signal",
-            array=engine_forward_signal_data,
-            dtype=np.int32,
-            suffix=self.parallel_config.local_engine_worker_queue_port,
-            create=False,
-        )
-
     def update_weights_from_tensor(self, mmap_infos):
         """
         update_weights_from_tensor
@@ -473,6 +459,9 @@ def event_loop_normal(self) -> None:
         # TODO: Unify status variables model_weights_status (shared memory) and model_weights_signal (numpy array) to one
         self.model_weights_signal = np.zeros([1], dtype=np.int32)
         while True:
+            # run eplb
+            self._run_eplb(tp_rank)
+
             if self.fd_config.load_config.dynamic_load_weight and not envs.FD_ENABLE_V1_UPDATE_WEIGHTS:
                 self.model_weights_signal[0] = int(self.model_weights_status.value[0])
                 if self.ranks > 1:
@@ -551,23 +540,12 @@ def event_loop_normal(self) -> None:
 
             if self._get_exist_task_flag():
                 logger.debug(f"Rank: {self.local_rank} Detected new requests.")
-                self.engine_forward_signal.value[0] = 1
                 tasks, read_finish = self.task_queue.get_tasks()
                 # Only one of all tp_size client will get read_finish == True.
                 if read_finish:
                     self._update_exist_task_flag(False)
                 self._tp_barrier_wait() if tp_size > 1 else None
 
-                # In EP parallel(corresponing to dp attention), we need to barrier for prefill to prevent data imbalance due to inconsistent data arrival.
-                # Only EP + DP prefill should barrier for data arrival.
-                # In mixed mode and decoder in D, we should not barrier to influence decoding.
-                if self.parallel_config.use_ep and self.scheduler_config.splitwise_role == "prefill":
-                    paddle.distributed.barrier(self.parallel_config.ep_group)
-
-                assert (
-                    len(tasks) > 0
-                ), f"task_queue.get_tasks() should contain at least one tuple, [([req1, ...] ,real_bsz)], but got len(tasks)={len(tasks)}"
-
                 batch_request, control_reqs, max_occupied_batch_index = BatchRequest.from_tasks(tasks)
 
                 if len(control_reqs) > 0:
@@ -594,12 +572,6 @@ def event_loop_normal(self) -> None:
 
                     # Process prefill inputs
                     self.worker.preprocess_new_task(batch_request, max_occupied_batch_index)
-            else:
-                if self.scheduler_config.splitwise_role == "prefill":
-                    if tp_size > 1:
-                        # Synchronize the signal for other workers
-                        self._tp_barrier_wait()
-                    continue
 
             # Let the ep group run control method synchronically
             if envs.FD_ENABLE_V1_UPDATE_WEIGHTS and self.parallel_config.use_ep:
@@ -614,7 +586,6 @@ def event_loop_normal(self) -> None:
                 and not self.worker.model_runner.not_need_stop()
             ):
                 self._tp_barrier_wait() if tp_size > 1 else None
-                self.engine_forward_signal.value[0] = 0
                 time.sleep(0.001)
                 continue
 
@@ -637,9 +608,6 @@ def event_loop_normal(self) -> None:
             if not envs.ENABLE_V1_KVCACHE_SCHEDULER:
                 self.exist_prefill_task_signal.value[0] = self.worker.exist_prefill()
             logger.debug(f"execute model cost: {time.time()-start_execute_time:.5f} s")
-            # run eplb
-            self._run_eplb(tp_rank)
-            self.engine_forward_signal.value[0] = 0
 
             if (
                 not self.parallel_config.use_ep

tests/ci_use/metrics/test_metrics.py

@@ -214,29 +214,28 @@ def test_metrics_with_clear_and_reset():
     """
     Test the metrics monitoring endpoint.
     """
-    pass  # not stable, uncomment after bug fix
-    # metrics_url = f"http://0.0.0.0:{FD_METRICS_PORT}/metrics"
+    metrics_url = f"http://0.0.0.0:{FD_METRICS_PORT}/metrics"
 
-    # async_concurrency(n=10)
+    async_concurrency(n=10)
 
-    # time.sleep(0.3)
+    time.sleep(0.3)
 
     # ===== clear_load_weight =====
-    # clear_url = f"http://0.0.0.0:{FD_API_PORT}/clear_load_weight"
-    # print("Calling clear_load_weight...")
-    # r = requests.get(clear_url, timeout=30)
-    # assert r.status_code == 200, f"clear_load_weight failed: {r.status_code}"
-
-    # metrics = get_metrics_dict(metrics_url)
-    # running = metrics["fastdeploy:num_requests_running"]
-    # waiting = metrics["fastdeploy:num_requests_waiting"]
-
-    # print(
-    #     "ASSERT after the clear_load_weight operation, the value is 0 (Request interruption stopped inference, and related requests were cleared):",
-    #     running,
-    #     "waiting:",
-    #     waiting,
-    # )
+    clear_url = f"http://0.0.0.0:{FD_API_PORT}/clear_load_weight"
+    print("Calling clear_load_weight...")
+    r = requests.get(clear_url, timeout=30)
+    assert r.status_code == 200, f"clear_load_weight failed: {r.status_code}"
+
+    metrics = get_metrics_dict(metrics_url)
+    running = metrics["fastdeploy:num_requests_running"]
+    waiting = metrics["fastdeploy:num_requests_waiting"]
+
+    print(
+        "ASSERT after the clear_load_weight operation, the value is 0 (Request interruption stopped inference, and related requests were cleared):",
+        running,
+        "waiting:",
+        waiting,
+    )
     # assert running == 0 and waiting == 0, "Expected both running and waiting to be 0 after clear_load_weight"
 
 

tests/engine/test_common_engine.py

@@ -1477,9 +1477,7 @@ def test_schedule_request_to_worker_v1_decode_preempted_and_errors(self):
         task.metrics.scheduler_recv_req_time = time.time()
 
         eng.scheduler = Mock(get_requests=Mock(return_value=[]), put_results=Mock())
-        eng.engine_worker_queue = Mock(
-            exist_tasks=Mock(return_value=False), put_tasks=Mock(), num_tasks=Mock(return_value=0)
-        )
+        eng.engine_worker_queue = Mock(exist_tasks=Mock(return_value=False), put_tasks=Mock())
         eng._send_error_response = Mock()
 
         eng.resource_manager = self._make_v1_decode_rm(eng, ([task], [("rid_x", None), ("rid_y", "bad")]))
@@ -1513,9 +1511,7 @@ def test_schedule_request_to_worker_v1_decode_prefill_task_path(self):
         task.metrics.scheduler_recv_req_time = time.time()
 
         eng.scheduler = Mock(get_requests=Mock(return_value=[]), put_results=Mock())
-        eng.engine_worker_queue = Mock(
-            exist_tasks=Mock(return_value=False), put_tasks=Mock(), num_tasks=Mock(return_value=0)
-        )
+        eng.engine_worker_queue = Mock(exist_tasks=Mock(return_value=False), put_tasks=Mock())
 
         eng.resource_manager = self._make_v1_decode_rm(eng, ([task], []))
 
@@ -1546,9 +1542,7 @@ def test_schedule_request_to_worker_v1_error_task_none_skips_send(self):
         task.metrics.scheduler_recv_req_time = time.time()
 
         eng.scheduler = Mock(get_requests=Mock(return_value=[]), put_results=Mock())
-        eng.engine_worker_queue = Mock(
-            exist_tasks=Mock(return_value=False), put_tasks=Mock(), num_tasks=Mock(return_value=0)
-        )
+        eng.engine_worker_queue = Mock(exist_tasks=Mock(return_value=False), put_tasks=Mock())
         eng._send_error_response = Mock()
 
         eng.resource_manager = self._make_v1_decode_rm(eng, ([task], [("rid_none", None)]))

tests/scheduler/test_dp_scheduler.py

@@ -415,6 +415,32 @@ def test_recycle_expired_requests(self, mock_time):
         self.assertEqual(scheduler.ids, ["fresh_req"])
         self.assertEqual(scheduler.ids_read_cursor, 1)
 
+    def test_get_requests_insufficient_resources(self):
+        """Test getting requests when resources are insufficient."""
+        mock_logger.reset_mock()
+
+        # Test with insufficient blocks - mock the condition variable to avoid threading issues
+        with patch.object(self.scheduler, "requests_not_empty"):
+            requests = self.scheduler.get_requests(
+                available_blocks=5, block_size=16, reserved_output_blocks=10, max_num_batched_tokens=1024, batch=1
+            )
+
+        self.assertEqual(requests, [])
+        # The logger should have been called for insufficient resources
+        self.assertTrue(mock_logger.debug.called)
+        # Check the message contains expected content
+        call_args = mock_logger.debug.call_args[0][0]
+        self.assertIn("insufficient", call_args.lower())
+
+    def test_get_requests_insufficient_batch(self):
+        """Test getting requests when batch size is insufficient."""
+        with patch.object(self.scheduler, "requests_not_empty"):
+            requests = self.scheduler.get_requests(
+                available_blocks=20, block_size=16, reserved_output_blocks=10, max_num_batched_tokens=1024, batch=0
+            )
+
+        self.assertEqual(requests, [])
+
     @patch("time.time")
     @patch.object(dp_scheduler_module, "envs")
     def test_get_requests_no_requests_available(self, mock_envs, mock_time):