feat: Ensemble async callback execution (rework)

src/constants.h

@@ -62,6 +62,7 @@ constexpr char kPythonBackend[] = "python";
 
 #ifdef TRITON_ENABLE_ENSEMBLE
 constexpr char kEnsemblePlatform[] = "ensemble";
+constexpr uint64_t ENSEMBLE_CB_POOL_SIZE = 8u;
 #endif  // TRITON_ENABLE_ENSEMBLE
 
 constexpr char kTensorRTExecutionAccelerator[] = "tensorrt";

src/ensemble_scheduler/ensemble_scheduler.cc

@@ -1,4 +1,4 @@
-// Copyright 2019-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// Copyright 2019-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions
@@ -45,17 +45,45 @@ class EnsembleContext;
 
 using IterationCount = size_t;
 
+// Check if the model is configured to preserve the order of responses.
+// This is critical for async execution of ResponseComplete callbacks.
+inline bool
+preserve_responses_order(const inference::ModelConfig& config)
+{
+  uint64_t total_instance_groups = 0;
+  for (const auto& group : config.instance_group()) {
+    total_instance_groups += group.count();
+  }
+
+  // Case 1: Sequence batching is enabled
+  // Case 2: Dynamic batching is disabled and there is only one instance group
+  // Case 3: Dynamic batching is enabled and preserve_ordering is true
+  // Case 4: Model transaction policy is decoupled (breaks RequestTracker
+  // lifecycle)
+  // Note: Although decoupled models do not preserve the order of
+  // responses, if the final response callback is not executed in the last step,
+  // the RequestTracker object will be freed prematurely and led to segmentation
+  // fault.
+  return config.has_sequence_batching() ||
+         (!config.has_dynamic_batching() && total_instance_groups <= 1) ||
+         (config.has_dynamic_batching() &&
+          config.dynamic_batching().preserve_ordering()) ||
+         config.model_transaction_policy().decoupled();
+}
+
 // Request tracker is passed as 'userp' in RequestRelease function and used
 // to manage the lifecycle of the ensemble request
 class RequestTracker {
  public:
   explicit RequestTracker(
       std::unique_ptr<InferenceRequest>&& request, uint64_t compute_start_ns,
       MetricModelReporter* metric_reporter,
-      InferenceStatsAggregator* stats_aggregator)
+      InferenceStatsAggregator* stats_aggregator,
+      triton::common::ThreadPool* callback_pool)
       : inflight_request_counter_(1), request_(std::move(request)),
         compute_start_ns_(compute_start_ns), metric_reporter_(metric_reporter),
-        stats_aggregator_(stats_aggregator), status_(Status::Success)
+        stats_aggregator_(stats_aggregator), status_(Status::Success),
+        callback_pool_(callback_pool)
   {
   }
 
@@ -70,6 +98,8 @@ class RequestTracker {
     return context_stats_aggregator_;
   }
 
+  triton::common::ThreadPool* CallbackPool() const { return callback_pool_; }
+
   void IncrementCounter()
   {
     std::lock_guard<std::mutex> lk(mtx_);
@@ -120,6 +150,7 @@ class RequestTracker {
   InferenceStatsAggregator* stats_aggregator_;
   InferenceStatsAggregator context_stats_aggregator_;
   Status status_;
+  triton::common::ThreadPool* const callback_pool_;
 };
 
 // Step is used as 'userp' and keeps ensemble context alive
@@ -129,9 +160,9 @@ class RequestTracker {
 struct Step {
   Step(
       size_t step_idx, const InferenceRequest::SequenceId& correlation_id,
-      uint32_t flags)
+      uint32_t flags, bool preserve_responses_order)
       : correlation_id_(correlation_id), flags_(flags), response_flags_(0),
-        step_idx_(step_idx)
+        preserve_responses_order_(preserve_responses_order), step_idx_(step_idx)
   {
   }
 
@@ -154,7 +185,7 @@ struct Step {
   // returning from the callback.
   uint32_t response_flags_;
   TRITONSERVER_InferenceResponse* response_;
-
+  const bool preserve_responses_order_;
 
   size_t step_idx_;
 };
@@ -237,7 +268,7 @@ class EnsembleContext {
       MetricModelReporter* metric_reporter,
       InferenceStatsAggregator* stats_aggregator, InferenceServer* is,
       EnsembleInfo* info, std::unique_ptr<InferenceRequest>& request,
-      cudaStream_t stream);
+      cudaStream_t stream, triton::common::ThreadPool* callback_pool);
 
   // Perform transition on 'context' state given the information of
   // 'completed_step'
@@ -326,6 +357,8 @@ class EnsembleContext {
   void CacheEnsembleTopLevelRequest(
       std::unique_ptr<InferenceResponse>& response);
 
+  triton::common::ThreadPool* CallbackPool() const { return callback_pool_; }
+
   InferenceServer* is_;
 
   EnsembleInfo* info_;
@@ -375,20 +408,26 @@ class EnsembleContext {
       TRITONSERVER_ResponseAllocator,
       decltype(&TRITONSERVER_ResponseAllocatorDelete)>
       allocator_;
+
+  // The thread pool used to execute ensemble callbacks and reduce e2e latency.
+  // The thread pool is managed by InferenceServer.
+  triton::common::ThreadPool* const callback_pool_;
 };
 
 EnsembleContext::EnsembleContext(
     MetricModelReporter* metric_reporter,
     InferenceStatsAggregator* stats_aggregator, InferenceServer* is,
     EnsembleInfo* info, std::unique_ptr<InferenceRequest>& request,
-    cudaStream_t stream)
+    cudaStream_t stream, triton::common::ThreadPool* callback_pool)
     : is_(is), info_(info), stream_(stream), inflight_step_counter_(0),
-      allocator_(nullptr, TRITONSERVER_ResponseAllocatorDelete)
+      allocator_(nullptr, TRITONSERVER_ResponseAllocatorDelete),
+      callback_pool_(callback_pool)
 {
   uint64_t compute_start_ns = 0;
   INFER_STATS_SET_TIMESTAMP(compute_start_ns);
   request_tracker_ = new RequestTracker(
-      std::move(request), compute_start_ns, metric_reporter, stats_aggregator);
+      std::move(request), compute_start_ns, metric_reporter, stats_aggregator,
+      callback_pool);
 
   auto& lrequest = request_tracker_->Request();
 
@@ -603,29 +642,57 @@ void
 EnsembleContext::RequestComplete(
     TRITONSERVER_InferenceRequest* request, const uint32_t flags, void* userp)
 {
-  if ((flags & TRITONSERVER_REQUEST_RELEASE_ALL) != 0) {
-    LOG_TRITONSERVER_ERROR(
-        TRITONSERVER_InferenceRequestDelete(request),
-        "deleting ensemble inference request");
-    auto request_tracker = reinterpret_cast<RequestTracker*>(userp);
-    if (request_tracker->DecrementCounter()) {
-      delete request_tracker;
+  auto request_tracker = reinterpret_cast<RequestTracker*>(userp);
+  auto pool = request_tracker->CallbackPool();
+  auto fn = [request, flags, request_tracker]() {
+    if ((flags & TRITONSERVER_REQUEST_RELEASE_ALL) != 0) {
+      LOG_TRITONSERVER_ERROR(
+          TRITONSERVER_InferenceRequestDelete(request),
+          "deleting ensemble inference request");
+      if (request_tracker->DecrementCounter()) {
+        delete request_tracker;
+      }
     }
+  };
+
+  // Attempt to enqueue the callback. If all workers are busy and queue is at
+  // capacity, execute the callback immediately in current thread.
+  if (pool->TaskQueueSize() < pool->Size()) {
+    pool->Enqueue(fn);
+  } else {
+    fn();
   }
 }
 
 void
 EnsembleContext::ResponseComplete(
     TRITONSERVER_InferenceResponse* response, const uint32_t flags, void* userp)
 {
-  auto step_ptr = std::unique_ptr<Step>(reinterpret_cast<Step*>(userp));
-  step_ptr->response_flags_ = flags;
-  step_ptr->response_ = response;
-
-  EnsembleContext::Proceed(step_ptr->ctx_, step_ptr);
-  // Expecting more responses
-  if ((flags & TRITONSERVER_RESPONSE_COMPLETE_FINAL) == 0) {
-    step_ptr.release();
+  auto step_raw_ptr = reinterpret_cast<Step*>(userp);
+  auto pool = step_raw_ptr->ctx_->CallbackPool();
+  auto fn = [response, flags, step_raw_ptr]() {
+    auto step_ptr = std::unique_ptr<Step>(step_raw_ptr);
+    step_ptr->response_flags_ = flags;
+    step_ptr->response_ = response;
+
+    EnsembleContext::Proceed(step_ptr->ctx_, step_ptr);
+    // Expecting more responses
+    if ((flags & TRITONSERVER_RESPONSE_COMPLETE_FINAL) == 0) {
+      step_ptr.release();
+    }
+  };
+
+  // Attempt to enqueue the callback. If all workers are busy and queue is at
+  // capacity, execute the callback immediately in current thread.
+  // Note: The async callback optimization does not guarantee the order of
+  // responses and expolit cases where responses can be out-of-order. For models
+  // required to preserve the order of responses, the response callbacks must be
+  // executed in the same thread synchronously.
+  if (!step_raw_ptr->preserve_responses_order_ &&
+      pool->TaskQueueSize() < pool->Size()) {
+    pool->Enqueue(fn);
+  } else {
+    fn();
   }
 }
 
@@ -971,8 +1038,8 @@ EnsembleContext::InitStep(
   for (const auto& pair : istep.output_to_tensor_) {
     irequest->AddOriginalRequestedOutput(pair.first);
   }
-
-  step->reset(new Step(step_idx, correlation_id, flags));
+  const bool preserve_order = preserve_responses_order(model->Config());
+  step->reset(new Step(step_idx, correlation_id, flags, preserve_order));
 
   irequest->SetId(request_id_);
   irequest->SetCorrelationId(correlation_id);
@@ -1448,7 +1515,7 @@ EnsembleScheduler::Enqueue(std::unique_ptr<InferenceRequest>& request)
   RETURN_IF_ERROR(request->SetState(InferenceRequest::State::EXECUTING));
   std::shared_ptr<EnsembleContext> context(new EnsembleContext(
       metric_reporter_.get(), stats_aggregator_, is_, info_.get(), request,
-      stream_));
+      stream_, callback_pool_));
   EnsembleContext::Proceed(context);
   return Status::Success;
 }
@@ -1537,6 +1604,7 @@ EnsembleScheduler::EnsembleScheduler(
       info_->tensor_to_prev_step_.emplace(pair.second, step_idx);
     }
   }
+  callback_pool_ = is_->EnsembleCallbackPool();
 }
 
 EnsembleScheduler::~EnsembleScheduler()

src/ensemble_scheduler/ensemble_scheduler.h

@@ -1,4 +1,4 @@
-// Copyright 2019-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// Copyright 2019-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions
@@ -36,6 +36,7 @@
 #include "scheduler.h"
 #include "scheduler_utils.h"
 #include "status.h"
+#include "triton/common/thread_pool.h"
 
 #ifdef TRITON_ENABLE_GPU
 #include <cuda_runtime_api.h>
@@ -107,6 +108,8 @@ class EnsembleScheduler : public Scheduler {
   // \see Scheduler::Stop()
   void Stop() override {}
 
+  triton::common::ThreadPool* CallbackPool() const { return callback_pool_; }
+
  private:
   EnsembleScheduler(
       InferenceStatsAggregator* const stats_aggregator,
@@ -128,6 +131,10 @@ class EnsembleScheduler : public Scheduler {
   cudaStream_t stream_;
 
   std::atomic<size_t> inflight_count_;
+
+  // Fixed-size thread pool to run callbacks at end of each ensemble step.
+  // Managed by the server.
+  triton::common::ThreadPool* callback_pool_;
 };
 
 }}  // namespace triton::core

src/server.cc

@@ -1,4 +1,4 @@
-// Copyright 2018-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// Copyright 2018-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions
@@ -117,6 +117,13 @@ InferenceServer::InferenceServer()
 #endif  // TRITON_ENABLE_GPU
 
   inflight_request_counter_ = 0;
+
+#ifdef TRITON_ENABLE_ENSEMBLE
+  // TODO: Need to scale the thread pool size smarter, e.g. based on the
+  // instance_group count of composing models.
+  ensemble_cb_pool_.reset(
+      new triton::common::ThreadPool(ENSEMBLE_CB_POOL_SIZE));
+#endif
 }
 
 Status

src/server.h

@@ -1,4 +1,4 @@
-// Copyright 2018-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// Copyright 2018-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions
@@ -332,6 +332,13 @@ class InferenceServer {
     return cache_manager_;
   }
 
+#ifdef TRITON_ENABLE_ENSEMBLE
+  triton::common::ThreadPool* EnsembleCallbackPool() const
+  {
+    return ensemble_cb_pool_.get();
+  }
+#endif  // TRITON_ENABLE_ENSEMBLE
+
  private:
   const std::string version_;
   std::string id_;
@@ -375,6 +382,12 @@ class InferenceServer {
   std::unique_ptr<ModelRepositoryManager> model_repository_manager_;
   std::shared_ptr<TritonBackendManager> backend_manager_;
   std::shared_ptr<TritonCacheManager> cache_manager_;
+
+#ifdef TRITON_ENABLE_ENSEMBLE
+  // The thread pool for all ensemble models to execute callbacks
+  // asynchronously.
+  std::unique_ptr<triton::common::ThreadPool> ensemble_cb_pool_;
+#endif  // TRITON_ENABLE_ENSEMBLE
 };
 
 }}  // namespace triton::core