diff --git a/onnxruntime/core/framework/allocation_planner.cc b/onnxruntime/core/framework/allocation_planner.cc index ea7a6432a7507..158ab8ed610f4 100644 --- a/onnxruntime/core/framework/allocation_planner.cc +++ b/onnxruntime/core/framework/allocation_planner.cc @@ -182,7 +182,6 @@ class PlannerImpl { // upstream_node_0 and upstream_node_1 are the immmediate upstream nodes of downstream_node // upstream_node_2 is the immediate nodes ahead of downstream_node in the same logic stream InlinedHashMap> dependence_graph_; - InlinedHashMap> value_consumer_map_; InlinedHashMap value_node_map_; // OrtValueInfo: Auxiliary information about an OrtValue used only during plan-generation: @@ -295,7 +294,7 @@ class PlannerImpl { } #endif - // Find if there exists some input tensor that we can use in-place for output_arg_num-th input in the node. + // Find if there exists some input tensor that we can use in-place for output_arg_num-th output in the node. bool FindReusableInput(const onnxruntime::Node& node, int output_arg_num, OrtValueIndex* reusable_input, bool* is_strided_tensor) { *is_strided_tensor = false; @@ -530,6 +529,7 @@ class PlannerImpl { // Initialize allocation plan: plan_.allocation_plan.resize(num_ml_values); + for (int i = 0; static_cast(i) < num_ml_values; i++) AllocPlan(i).reused_buffer = i; } bool HasExternalOutputs(const Node& node) const { @@ -1065,7 +1065,8 @@ class PlannerImpl { // build the consumer list for each value int num_ml_values = ort_value_name_idx_map_.MaxIdx() + 1; - value_consumer_map_.reserve(num_ml_values); + InlinedHashMap> value_consumer_map; + value_consumer_map.reserve(num_ml_values); // iterate each stream from back, so the first element is the last consumer in single stream case for (auto& stream : stream_nodes_) { @@ -1078,10 +1079,10 @@ class PlannerImpl { const auto& name = input.Name(); int value_idx; ORT_RETURN_IF_ERROR(ort_value_name_idx_map_.GetIdx(name, value_idx)); - auto origin = Buffer(value_idx); - if (origin != -1 && plan_.allocation_plan[origin].alloc_kind == AllocKind::kAllocate) { + auto origin = AllocPlan(value_idx).reused_buffer; + if (AllocPlan(origin).alloc_kind == AllocKind::kAllocate) { // add current node as consumer for origin buffer - value_consumer_map_[origin].insert(node_index); + value_consumer_map[origin].insert(node_index); } } return Status::OK(); @@ -1138,8 +1139,8 @@ class PlannerImpl { std::cout << p_input_arg->Name() << " reused by " << p_output_arg->Name() << " as input" << std::endl; allocation_plan[output_idx_global].alloc_kind = AllocKind::kReuse; allocation_plan[output_idx_global].reused_buffer = reusable_input; - value_consumer_map_[reusable_input].insert(value_consumer_map_[output_idx_global].begin(), - value_consumer_map_[output_idx_global].end()); + value_consumer_map[reusable_input].insert(value_consumer_map[output_idx_global].begin(), + value_consumer_map[output_idx_global].end()); reused.insert(reusable_input); found_reusable = true; break; @@ -1168,8 +1169,8 @@ class PlannerImpl { allocation_plan[reusable_input].alloc_kind == AllocKind::kAllocate) { allocation_plan[output_idx_global].alloc_kind = AllocKind::kReuse; allocation_plan[output_idx_global].reused_buffer = reusable_input; - value_consumer_map_[reusable_input].insert(value_consumer_map_[output_idx_global].begin(), - value_consumer_map_[output_idx_global].end()); + value_consumer_map[reusable_input].insert(value_consumer_map[output_idx_global].begin(), + value_consumer_map[output_idx_global].end()); reused.insert(reusable_input); continue; } // if @@ -1187,11 +1188,11 @@ class PlannerImpl { OrtValueIndex input_arg_index{}; if (value_map.GetIdx(p_input_arg->Name(), input_arg_index).IsOK() && allocation_plan[input_arg_index].alloc_kind == AllocKind::kAllocate) { - if (value_consumer_map_[input_arg_index].size() == 1 && SameSize(*p_input_arg, *p_output_arg)) { + if (value_consumer_map[input_arg_index].size() == 1 && SameSize(*p_input_arg, *p_output_arg)) { allocation_plan[output_idx_global].alloc_kind = AllocKind::kReuse; allocation_plan[output_idx_global].reused_buffer = input_arg_index; - value_consumer_map_[input_arg_index].insert(value_consumer_map_[output_idx_global].begin(), - value_consumer_map_[output_idx_global].end()); + value_consumer_map[input_arg_index].insert(value_consumer_map[output_idx_global].begin(), + value_consumer_map[output_idx_global].end()); reused.insert(input_arg_index); } } @@ -1266,7 +1267,7 @@ class PlannerImpl { } bool all_covered = true; - for (auto consumer : value_consumer_map_[output_idx_global]) { + for (auto consumer : value_consumer_map[output_idx_global]) { if (deps->find(consumer) == deps->end()) { all_covered = false; break; @@ -1277,9 +1278,9 @@ class PlannerImpl { allocation_plan[downstream_value].reused_buffer = output_idx_global; get_reused = true; // add new consumer for the value to be reused - value_consumer_map_[output_idx_global].insert(value_node_map_[downstream_value]); - value_consumer_map_[output_idx_global].insert(value_consumer_map_[downstream_value].begin(), - value_consumer_map_[downstream_value].end()); + value_consumer_map[output_idx_global].insert(value_node_map_[downstream_value]); + value_consumer_map[output_idx_global].insert(value_consumer_map[downstream_value].begin(), + value_consumer_map[downstream_value].end()); node_iter = size_iter->second.erase(node_iter); if (size_iter->second.empty()) { local_iter->second.erase(size_iter); @@ -1342,8 +1343,9 @@ class PlannerImpl { ort_value_usecount.reserve(ort_value_info_.size()); #endif for (size_t i = 0; i < stream_nodes_.size(); ++i) { - // compute use count first + // compute use count first. TODO(leca): call ComputeReuseCount() only once is enough! ORT_RETURN_IF_ERROR(ComputeReuseCount()); + for (int j = 0; static_cast(j) < ort_value_info_.size(); j++) Buffer(j) = j; #if !defined(ORT_MINIMAL_BUILD) && defined(ORT_MEMORY_PROFILE) if (i == 0) { for (auto ort_value_info : ort_value_info_) { @@ -1693,8 +1695,8 @@ class PlannerImpl { const auto& name = input.Name(); int value_idx; ORT_RETURN_IF_ERROR(ort_value_name_idx_map_.GetIdx(name, value_idx)); - auto origin = Buffer(value_idx); - if (origin != -1 && plan_.allocation_plan[origin].alloc_kind == AllocKind::kAllocate) { + auto origin = AllocPlan(value_idx).reused_buffer; + if (AllocPlan(origin).alloc_kind == AllocKind::kAllocate) { // add current node as consumer for origin buffer value_consumers[origin].push_back(node_index); } @@ -1889,7 +1891,7 @@ class PlannerImpl { // 2. the consumer is in the same stream(non-cpu device), but it consumes a CPU tensor from an non-shape op. // for example, a resize cuda kernel consumer a tensor from MemCpyToHost cuda kernel on the same stream. // in this case, the FIFO can't guarantee the cpu tensor is ready when resize kernel is launching - OrtDevice::DeviceType output_arg_device = plan_.allocation_plan[output_arg_idx].location.Type(); + OrtDevice::DeviceType output_arg_device = AllocPlan(output_arg_idx).location.Type(); WaitNotificationFn wait_handle = stream_handle_registry.GetWaitHandle(stream_device, output_arg_device); if ((node_stream_map_[it->Index()] != i || output_arg_device == OrtDevice::CPU) && wait_handle != nullptr) { if (node_to_notification.find(node_index) == node_to_notification.end()) { diff --git a/onnxruntime/test/framework/allocation_planner_test.cc b/onnxruntime/test/framework/allocation_planner_test.cc index d7b1de5c930c5..3e0d94e94e48c 100644 --- a/onnxruntime/test/framework/allocation_planner_test.cc +++ b/onnxruntime/test/framework/allocation_planner_test.cc @@ -1974,6 +1974,74 @@ TEST_F(PlannerTest, TestCpuIf) { ASSERT_TRUE(exe_plan[1]->steps_[6]->ToString().substr(0, WaitOnEPStep.size()) == WaitOnEPStep); } } + +// model looks like: +// |-----------> Gather +// |-----------> Gather +// |-----------> Gather +// |-----------> Gather +// Shape ----------------> Reshape --> Shape ------------------> Reshape +// ^ ^ +// InstanceNormalization ----| InstanceNormalization ------| +// +// Python script to create this model: +// def CreateModelFor19480(): +// #shape->reshape->shape->reshape, 4 gather +// graphNodes = [] +// graphNodes.append(h.make_node('Shape', inputs=['shape_input'], outputs=['9'])) +// graphNodes.append(h.make_node('InstanceNormalization', inputs=['in0_input', 'scale0', 'B0'], outputs=['8'])) +// graphNodes.append(h.make_node('Reshape', inputs=['8', '9'], outputs=['Reshape15_output'])) +// graphNodes.append(h.make_node('Shape', inputs=['Reshape15_output'], outputs=['281'])) +// graphNodes.append(h.make_node('InstanceNormalization', inputs=['in1_input', 'scale1', 'B1'], outputs=['293'])) +// graphNodes.append(h.make_node('Reshape', inputs=['293', '281'], outputs=['output0'])) +// graphNodes.append(h.make_node('Gather', inputs=['281', 'indices1'], outputs=['output1'])) +// graphNodes.append(h.make_node('Gather', inputs=['281', 'indices2'], outputs=['output2'])) +// graphNodes.append(h.make_node('Gather', inputs=['281', 'indices3'], outputs=['output3'])) +// graphNodes.append(h.make_node('Gather', inputs=['281', 'indices4'], outputs=['output4'])) +// g = h.make_graph(graphNodes, 'issue_19480', +// [h.make_tensor_value_info('shape_input', tp.FLOAT, ['batch', 128, None, None]), +// h.make_tensor_value_info('in0_input', tp.FLOAT, ['batch', 32, None]), +// h.make_tensor_value_info('scale0', tp.FLOAT, [32]), +// h.make_tensor_value_info('B0', tp.FLOAT, [32]), +// h.make_tensor_value_info('in1_input', tp.FLOAT, ['batch', 32, None]), +// h.make_tensor_value_info('scale1', tp.FLOAT, [32]), +// h.make_tensor_value_info('B1', tp.FLOAT, [32]), +// h.make_tensor_value_info('indices1', tp.INT32, []), +// h.make_tensor_value_info('indices2', tp.INT32, []), +// h.make_tensor_value_info('indices3', tp.INT32, []), +// h.make_tensor_value_info('indices4', tp.INT32, [])], +// [h.make_tensor_value_info('output0', tp.FLOAT, None), +// h.make_tensor_value_info('output1', tp.INT64, None), +// h.make_tensor_value_info('output2', tp.INT64, None), +// h.make_tensor_value_info('output3', tp.INT64, None), +// h.make_tensor_value_info('output4', tp.INT64, None)]) +// model = h.make_model(g, opset_imports=[h.make_operatorsetid("", 17)], producer_name='producer_name') +// onnx.save(model, 'issue_19480.onnx') +// +TEST(AllocationPlannerTest, ReusedInputCrossDifferentStreams) { + SessionOptions sess_opt; + sess_opt.graph_optimization_level = TransformerLevel::Default; + + InferenceSession sess(sess_opt, GetEnvironment(), ORT_TSTR("./testdata/multi_stream_models/issue_19480.onnx")); + auto status = sess.RegisterExecutionProvider(DefaultCudaExecutionProvider()); + status = sess.Load(); + status = sess.Initialize(); + ASSERT_TRUE(status.IsOK()) << "No crash"; + const SequentialExecutionPlan* plan = sess.GetSessionState().GetExecutionPlan(); + ASSERT_EQ(plan->allocation_plan[14].alloc_kind, AllocKind::kReuse) << "The input of reshape and gather will reuse the output of shape"; + + int gather_count = 0; + for (size_t i = 0; i < plan->execution_plan[1]->steps_.size(); i++) { + if (strstr(typeid(*(plan->execution_plan[1]->steps_[i])).name(), "LaunchKernelStep")) { + const Node* node = sess.GetSessionState().GetGraphViewer().GetNode(plan->execution_plan[1]->steps_[i]->GetNodeIndex()); + if (node->OpType() == "Gather") + gather_count++; + else + FAIL() << "CPU stream should contain only gather ops"; + } + } + ASSERT_EQ(gather_count, 4) << "4 gather ops are all placed in CPU stream"; +} #endif } // namespace test } // namespace onnxruntime diff --git a/onnxruntime/test/testdata/multi_stream_models/issue_19480.onnx b/onnxruntime/test/testdata/multi_stream_models/issue_19480.onnx new file mode 100644 index 0000000000000..dc7d39206dd49 Binary files /dev/null and b/onnxruntime/test/testdata/multi_stream_models/issue_19480.onnx differ