Response sender to check for improper non-decoupled model usage

src/infer_request.cc

@@ -74,7 +74,7 @@ InferRequest::InferRequest(
   pb_cancel_ =
       std::make_shared<PbCancel>(response_factory_address_, request_address_);
   response_sender_ = std::make_shared<ResponseSender>(
-      request_address_, response_factory_address_,
+      request_address_, response_factory_address_, nullptr /* is_decoupled */,
       Stub::GetOrCreateInstance()->SharedMemory(), pb_cancel_);
 #endif
 }
@@ -272,7 +272,8 @@ InferRequest::SaveToSharedMemory(std::unique_ptr<SharedMemoryManager>& shm_pool)
 std::unique_ptr<InferRequest>
 InferRequest::LoadFromSharedMemory(
     std::unique_ptr<SharedMemoryManager>& shm_pool,
-    bi::managed_external_buffer::handle_t request_handle, bool open_cuda_handle)
+    bi::managed_external_buffer::handle_t request_handle, bool open_cuda_handle,
+    bool const* is_model_decoupled)
 {
   AllocatedSharedMemory<char> infer_request_shm =
       shm_pool->Load<char>(request_handle);
@@ -328,7 +329,7 @@ InferRequest::LoadFromSharedMemory(
   return std::unique_ptr<InferRequest>(new InferRequest(
       infer_request_shm, request_id_shm, correlation_id_shm,
       requested_output_names_shm, model_name_shm, input_tensors, parameters_shm,
-      infer_trace_shm));
+      infer_trace_shm, is_model_decoupled));
 }
 
 InferRequest::InferRequest(
@@ -339,7 +340,8 @@ InferRequest::InferRequest(
     std::unique_ptr<PbString>& model_name_shm,
     std::vector<std::shared_ptr<PbTensor>>& input_tensors,
     std::unique_ptr<PbString>& parameters_shm,
-    std::unique_ptr<InferenceTrace>& infer_trace_shm)
+    std::unique_ptr<InferenceTrace>& infer_trace_shm,
+    bool const* is_model_decoupled)
     : infer_request_shm_(std::move(infer_request_shm)),
       request_id_shm_(std::move(request_id_shm)),
       requested_output_names_shm_(std::move(requested_output_names_shm)),
@@ -387,7 +389,7 @@ InferRequest::InferRequest(
   pb_cancel_ =
       std::make_shared<PbCancel>(response_factory_address_, request_address_);
   response_sender_ = std::make_shared<ResponseSender>(
-      request_address_, response_factory_address_,
+      request_address_, response_factory_address_, is_model_decoupled,
       Stub::GetOrCreateInstance()->SharedMemory(), pb_cancel_);
 #endif
 }

src/infer_request.h

@@ -118,7 +118,7 @@ class InferRequest {
   static std::unique_ptr<InferRequest> LoadFromSharedMemory(
       std::unique_ptr<SharedMemoryManager>& shm_pool,
       bi::managed_external_buffer::handle_t request_handle,
-      bool open_cuda_handle);
+      bool open_cuda_handle, bool const* is_model_decoupled);
 
   /// Disallow copying the inference request object.
   DISALLOW_COPY_AND_ASSIGN(InferRequest);
@@ -135,7 +135,8 @@ class InferRequest {
       std::unique_ptr<PbString>& model_name_shm,
       std::vector<std::shared_ptr<PbTensor>>& input_tensors,
       std::unique_ptr<PbString>& parameters_shm,
-      std::unique_ptr<InferenceTrace>& infer_trace_shm);
+      std::unique_ptr<InferenceTrace>& infer_trace_shm,
+      bool const* is_model_decoupled);
 
   std::string request_id_;
   CorrelationId correlation_id_;

src/pb_stub.cc

@@ -658,7 +658,8 @@ Stub::LoadRequestsFromSharedMemory(RequestBatch* request_batch_shm_ptr)
   for (size_t i = 0; i < batch_size; i++) {
     std::shared_ptr<InferRequest> infer_request =
         InferRequest::LoadFromSharedMemory(
-            shm_pool_, request_shm_handle[i], true /* open_cuda_handle */);
+            shm_pool_, request_shm_handle[i], true /* open_cuda_handle */,
+            &ipc_control_->decoupled /* is_model_decoupled */);
     py_request_list.append(infer_request);
   }
 

src/python_be.cc

@@ -571,7 +571,8 @@ ModelInstanceState::ExecuteBLSRequest(
           reinterpret_cast<bi::managed_external_buffer::handle_t*>(
               request_batch.data_.get() + sizeof(RequestBatch));
       infer_request = InferRequest::LoadFromSharedMemory(
-          Stub()->ShmPool(), *request_handle, false /* open_cuda_handle */);
+          Stub()->ShmPool(), *request_handle, false /* open_cuda_handle */,
+          nullptr /* is_model_decoupled */);
 
       // If the BLS inputs are in GPU an additional round trip between the
       // stub process and the main process is required. The reason is that we

src/response_sender.cc

@@ -35,13 +35,31 @@
 
 namespace triton { namespace backend { namespace python {
 
+void
+AssertResponseSenderArgumentsWellFormed(
+    const std::shared_ptr<InferResponse>& response, const uint32_t flags)
+{
+  // Check the correctness of the provided flags.
+  if (flags != TRITONSERVER_RESPONSE_COMPLETE_FINAL && flags != 0) {
+    throw PythonBackendException(
+        "Unable to send response. Unsupported flag provided.");
+  }
+
+  if (flags == 0 && response == nullptr) {
+    throw PythonBackendException(
+        "Inference Response object must be provided when the response flags is "
+        "set to zero.");
+  }
+}
+
 ResponseSender::ResponseSender(
     intptr_t request_address, intptr_t response_factory_address,
-    std::unique_ptr<SharedMemoryManager>& shm_pool,
+    bool const* is_decoupled, std::unique_ptr<SharedMemoryManager>& shm_pool,
     const std::shared_ptr<PbCancel>& pb_cancel)
     : request_address_(request_address),
-      response_factory_address_(response_factory_address), shm_pool_(shm_pool),
-      closed_(false), pb_cancel_(pb_cancel)
+      response_factory_address_(response_factory_address),
+      is_decoupled_(is_decoupled), shm_pool_(shm_pool), pb_cancel_(pb_cancel),
+      closed_(false), number_of_response_sent_(0)
 {
 }
 
@@ -54,15 +72,32 @@ ResponseSender::~ResponseSender()
 }
 
 void
-ResponseSender::Send(
-    std::shared_ptr<InferResponse> infer_response, const uint32_t flags)
+ResponseSender::UpdateStateAndCounters(
+    const std::shared_ptr<InferResponse>& response, const uint32_t flags)
 {
-  // Release the GIL. This avoids a potential deadlock situation in the parent
-  // process, where every thread in the thread pool is indirectly waiting for a
-  // function in the stub process that acquires the GIL. Meanwhile, the current
-  // thread, which holds the GIL, is also waiting for the parent side to have
-  // the next available thread to pick up the job during resource contention.
-  py::gil_scoped_release release;
+  if (is_decoupled_ == nullptr) {
+    // TODO: Can a model access the response sender on a BLS infer request?
+    throw PythonBackendException(
+        "Unable to send response. Response sender has no reference to the "
+        "decoupled state of the model.");
+  }
+  bool is_decoupled = *is_decoupled_;
+
+  std::lock_guard<std::mutex> lk(mu_);
+
+  if (!is_decoupled) {
+    if (response != nullptr && number_of_response_sent_ > 0) {
+      throw PythonBackendException(
+          "Unable to send response. Non-decoupled model cannot send more than "
+          "one response.");
+    }
+    if (response == nullptr && flags == TRITONSERVER_RESPONSE_COMPLETE_FINAL &&
+        number_of_response_sent_ == 0) {
+      throw PythonBackendException(
+          "Unable to send response. Non-decoupled model cannot send complete "
+          "final before sending a response.");
+    }
+  }
 
   if (closed_) {
     throw PythonBackendException(
@@ -72,18 +107,22 @@ ResponseSender::Send(
   if (flags == TRITONSERVER_RESPONSE_COMPLETE_FINAL) {
     closed_ = true;
   }
+  number_of_response_sent_++;
+}
 
-  // Check the correctness of the provided flags.
-  if (flags != TRITONSERVER_RESPONSE_COMPLETE_FINAL && flags != 0) {
-    throw PythonBackendException(
-        "Unable to send response. Unsupported flag provided.");
-  }
+void
+ResponseSender::Send(
+    std::shared_ptr<InferResponse> infer_response, const uint32_t flags)
+{
+  // Release the GIL. This avoids a potential deadlock situation in the parent
+  // process, where every thread in the thread pool is indirectly waiting for a
+  // function in the stub process that acquires the GIL. Meanwhile, the current
+  // thread, which holds the GIL, is also waiting for the parent side to have
+  // the next available thread to pick up the job during resource contention.
+  py::gil_scoped_release release;
 
-  if (flags == 0 && infer_response == nullptr) {
-    throw PythonBackendException(
-        "Inference Response object must be provided when the response flags is "
-        "set to zero.");
-  }
+  AssertResponseSenderArgumentsWellFormed(infer_response, flags);
+  UpdateStateAndCounters(infer_response, flags);
 
   std::unique_ptr<Stub>& stub = Stub::GetOrCreateInstance();
 

src/response_sender.h

@@ -26,6 +26,8 @@
 
 #pragma once
 
+#include <mutex>
+
 #include "infer_response.h"
 #include "pb_cancel.h"
 #include "shm_manager.h"
@@ -36,17 +38,24 @@ class ResponseSender {
  public:
   ResponseSender(
       intptr_t request_address, intptr_t response_factory_address,
-      std::unique_ptr<SharedMemoryManager>& shm_pool,
+      bool const* is_decoupled, std::unique_ptr<SharedMemoryManager>& shm_pool,
       const std::shared_ptr<PbCancel>& pb_cancel);
   ~ResponseSender();
   void Send(std::shared_ptr<InferResponse> response, const uint32_t flags);
   bool IsCancelled();
 
  private:
+  void UpdateStateAndCounters(
+      const std::shared_ptr<InferResponse>& response, const uint32_t flags);
+
   intptr_t request_address_;
   intptr_t response_factory_address_;
+  bool const* is_decoupled_;
   std::unique_ptr<SharedMemoryManager>& shm_pool_;
-  bool closed_;
   std::shared_ptr<PbCancel> pb_cancel_;
+
+  std::mutex mu_;
+  bool closed_;
+  size_t number_of_response_sent_;
 };
 }}}  // namespace triton::backend::python