diff --git a/docs/usage/environment_variables.md b/docs/usage/environment_variables.md index bc2b4ae58a..1dccb38eac 100644 --- a/docs/usage/environment_variables.md +++ b/docs/usage/environment_variables.md @@ -147,6 +147,9 @@ 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")), diff --git a/docs/zh/usage/environment_variables.md b/docs/zh/usage/environment_variables.md index de591d5a07..45b014218c 100644 --- a/docs/zh/usage/environment_variables.md +++ b/docs/zh/usage/environment_variables.md @@ -147,6 +147,9 @@ 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")), diff --git a/fastdeploy/engine/common_engine.py b/fastdeploy/engine/common_engine.py index ed28bd10c6..8f72cf90b0 100644 --- a/fastdeploy/engine/common_engine.py +++ b/fastdeploy/engine/common_engine.py @@ -313,15 +313,6 @@ class EngineService: 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 @@ -984,23 +975,26 @@ class EngineService: 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: - 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 - # 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 not (self.engine_worker_queue.num_tasks() == 0 and self.engine_forward_signal.value[0] == 0): + 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) + + else: + 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 # 2. Schedule requests tasks, error_tasks = self.resource_manager.schedule() @@ -1050,13 +1044,6 @@ class EngineService: else: task.metrics.inference_start_time = time.time() self.engine_worker_queue.put_tasks((tasks, 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( - ([], self.resource_manager.real_bsz) - ) # Empty (as idle tasks for ep) # 4. Response error tasks if error_tasks: diff --git a/fastdeploy/envs.py b/fastdeploy/envs.py index 47c3578781..54aa45b371 100644 --- a/fastdeploy/envs.py +++ b/fastdeploy/envs.py @@ -121,6 +121,8 @@ environment_variables: dict[str, Callable[[], Any]] = { "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. diff --git a/fastdeploy/scheduler/dp_scheduler.py b/fastdeploy/scheduler/dp_scheduler.py index bd6606bfd6..05ee40487a 100644 --- a/fastdeploy/scheduler/dp_scheduler.py +++ b/fastdeploy/scheduler/dp_scheduler.py @@ -23,7 +23,7 @@ from typing import Dict, List, Optional from fastdeploy.engine.request import Request, RequestOutput 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): @@ -131,19 +131,52 @@ class DPLocalScheduler(LocalScheduler): 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: self.scheduler_logger.info( diff --git a/fastdeploy/splitwise/internal_adapter_utils.py b/fastdeploy/splitwise/internal_adapter_utils.py index e91cf17828..576a3717fe 100644 --- a/fastdeploy/splitwise/internal_adapter_utils.py +++ b/fastdeploy/splitwise/internal_adapter_utils.py @@ -53,9 +53,6 @@ class InternalAdapter: 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 @@ class InternalAdapter: "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 diff --git a/fastdeploy/worker/worker_process.py b/fastdeploy/worker/worker_process.py index ce02882390..fb4cec59a5 100644 --- a/fastdeploy/worker/worker_process.py +++ b/fastdeploy/worker/worker_process.py @@ -283,16 +283,6 @@ class PaddleDisWorkerProc: create=False, ) - # init engine forward signal - 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 @@ -450,6 +440,9 @@ class PaddleDisWorkerProc: # 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: self.model_weights_signal[0] = int(self.model_weights_status.value[0]) if self.ranks > 1: @@ -523,7 +516,7 @@ class PaddleDisWorkerProc: if self.exist_task_signal.value[0] == ExistTaskStatus.EXIST or self.task_queue.read_finish_flag.get() == 1: logger.info(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: @@ -532,48 +525,35 @@ class PaddleDisWorkerProc: self.task_queue.read_finish_flag.set(0) else: self.exist_task_signal.value[0] = ExistTaskStatus.EMPTY - # 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) req_dicts, control_reqs = [], [] - # In EP + DP prefill, empty task ([]) is delived in worker to barrier. For empty task, just skip and continue. - if tasks[0][0]: - for req_dict, bsz in tasks: - if len(req_dict) > 0 and isinstance(req_dict[0], ControlRequest): - control_reqs.append(req_dict[0]) - else: - max_occupied_batch_index = int(bsz) - req_dicts.extend(req_dict) + for req_dict, bsz in tasks: + if len(req_dict) > 0 and isinstance(req_dict[0], ControlRequest): + control_reqs.append(req_dict[0]) + else: + max_occupied_batch_index = int(bsz) + req_dicts.extend(req_dict) - # todo: run control request async - if len(control_reqs) > 0: - logger.info(f"Rank: {self.local_rank} received {len(control_reqs)} control request.") - for control_req in control_reqs: - self.run_control_method(control_req) - self._tp_barrier_wait() if tp_size > 1 else None + # todo: run control request async + if len(control_reqs) > 0: + logger.info(f"Rank: {self.local_rank} received {len(control_reqs)} control request.") + for control_req in control_reqs: + self.run_control_method(control_req) + self._tp_barrier_wait() if tp_size > 1 else None - # Count prefill requests in current batch - num_prefill_requests = sum(1 for req in req_dicts if req.task_type == RequestType.PREFILL) - num_scheduled_requests = len(req_dicts) - scheduled_request_ids = [req.request_id for req in req_dicts] - logger.info( - f"Rank: {self.local_rank}, num_prefill_requests: {num_prefill_requests}, " - f"max_occupied_batch_index: {max_occupied_batch_index}, " - f"num_scheduled_requests: {num_scheduled_requests}, " - f"scheduled_request_ids: {scheduled_request_ids}" - ) + # Count prefill requests in current batch + num_prefill_requests = sum(1 for req in req_dicts if req.task_type == RequestType.PREFILL) + num_scheduled_requests = len(req_dicts) + scheduled_request_ids = [req.request_id for req in req_dicts] + logger.info( + f"Rank: {self.local_rank}, num_prefill_requests: {num_prefill_requests}, " + f"max_occupied_batch_index: {max_occupied_batch_index}, " + f"num_scheduled_requests: {num_scheduled_requests}, " + f"scheduled_request_ids: {scheduled_request_ids}" + ) - # Process prefill inputs - self.worker.preprocess_new_task(req_dicts, 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 + # Process prefill inputs + self.worker.preprocess_new_task(req_dicts, max_occupied_batch_index) if ( (not self.parallel_config.use_ep) @@ -581,7 +561,7 @@ class PaddleDisWorkerProc: and (not self.enable_overlap_schedule) ): self._tp_barrier_wait() if tp_size > 1 else None - self.engine_forward_signal.value[0] = 0 + time.sleep(0.001) continue @@ -593,9 +573,6 @@ class PaddleDisWorkerProc: 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 def initialize_kv_cache(self) -> None: """Profiles the peak memory usage of the model to determine how many diff --git a/tests/ci_use/metrics/test_metrics.py b/tests/ci_use/metrics/test_metrics.py index 0d5353780f..a54504c29b 100644 --- a/tests/ci_use/metrics/test_metrics.py +++ b/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}" + 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"] + 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, - # ) + 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" diff --git a/tests/scheduler/test_dp_scheduler.py b/tests/scheduler/test_dp_scheduler.py index 72ee7ccd86..cb753d44db 100644 --- a/tests/scheduler/test_dp_scheduler.py +++ b/tests/scheduler/test_dp_scheduler.py @@ -411,6 +411,32 @@ class TestDPLocalScheduler(unittest.TestCase): 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): diff --git a/tests/splitwise/test_internal_adapter_utils.py b/tests/splitwise/test_internal_adapter_utils.py index f8f22215c0..4d77278984 100644 --- a/tests/splitwise/test_internal_adapter_utils.py +++ b/tests/splitwise/test_internal_adapter_utils.py @@ -25,9 +25,6 @@ class DummyEngine: """Dummy Engine class to simulate the actual Engine for testing.""" class ResourceManager: - def __init__(self): - self.waiting = [] - def available_batch(self): return 4