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onnxruntime/onnxruntime/test/framework/execution_frame_test.cc
Scott McKay ac6a4afb0f
Add validation of shape when re-using a buffer in ExecutionFrame (#1356) 
* Check for empty string as dim_param in allocation planner.
* Validate shape is compatible at runtime when re-using Tensor.
2019-07-09 14:59:07 +10:00

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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "core/framework/execution_frame.h"
#include "core/framework/op_kernel.h"
#include "core/framework/session_state.h"
#include "core/graph/model.h"
#include "core/providers/cpu/cpu_execution_provider.h"
#include "core/session/inference_session.h"
#include "test_utils.h"
#include "test/test_environment.h"
#include "gtest/gtest.h"
#include "gmock/gmock.h"
using namespace ONNX_NAMESPACE;
using namespace std;
namespace onnxruntime {
namespace test {
typedef std::vector<onnxruntime::NodeArg*> ArgMap;
std::shared_ptr<onnxruntime::Model> DummyGraphWithClip() {
auto model = std::make_shared<onnxruntime::Model>("test");
onnxruntime::Graph& graph = model->MainGraph();
TypeProto tensor_float;
tensor_float.mutable_tensor_type()->set_elem_type(TensorProto_DataType_FLOAT);
onnxruntime::NodeArg input_def("X", &tensor_float), output_def("Y", &tensor_float);
graph.AddNode("node1", "Clip", "clip operator", ArgMap{&input_def}, ArgMap{&output_def});
return model;
}
std::unique_ptr<IExecutionProvider> CreateCPUExecutionProvider() {
CPUExecutionProviderInfo info;
return std::make_unique<CPUExecutionProvider>(info);
}
TEST(ExecutionFrameTest, TensorAllocationTest) {
onnxruntime::Model model("test");
onnxruntime::Graph& graph = model.MainGraph();
TypeProto tensor_float;
tensor_float.mutable_tensor_type()->set_elem_type(TensorProto_DataType_FLOAT);
onnxruntime::NodeArg input_def("X", &tensor_float), output_def("Y", &tensor_float);
graph.AddNode("node1", "Clip", "Clip operator", ArgMap{&input_def}, ArgMap{&output_def});
onnxruntime::Node* node = graph.GetNode(graph.NumberOfNodes() - 1);
Status status = graph.Resolve();
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
auto cpu_xp = CreateCPUExecutionProvider();
auto xp_typ = cpu_xp->Type();
ExecutionProviders execution_providers;
execution_providers.Add(xp_typ, std::move(cpu_xp));
KernelRegistryManager kernel_registry_manager;
status = kernel_registry_manager.RegisterKernels(execution_providers);
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
SessionState state{execution_providers, true};
state.SetGraphViewer(std::make_unique<GraphViewer>(graph));
OrtValueNameIdxMap& mlvalue_name_idx_map{state.GetOrtValueNameIdxMap()};
mlvalue_name_idx_map.Add("X");
mlvalue_name_idx_map.Add("Y");
node->SetExecutionProviderType(xp_typ);
std::unique_ptr<SequentialExecutionPlan> p_seq_exec_plan;
// TODO below line is for testing only. In production use SequentialPlanner::CreatePlan()
SequentialPlannerContext context(false);
status = SequentialPlanner::CreatePlan(nullptr, GraphViewer(graph), {}, execution_providers, kernel_registry_manager,
mlvalue_name_idx_map, context, p_seq_exec_plan);
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
state.SetExecutionPlan(std::move(p_seq_exec_plan));
state.CalculateNodeIndexInfo();
vector<OrtValue> outputs;
ExecutionFrame frame({}, {}, {}, outputs, {}, state);
int start_index = frame.GetNodeOffset(node->Index());
EXPECT_EQ(start_index, 0);
TensorShape shape(std::vector<int64_t>{2, 3});
OrtValue& mlvalue0 = *frame.GetMutableNodeInputOrOutputMLValue(start_index);
status = frame.AllocateMLValueTensorSelfOwnBuffer(mlvalue0, start_index, DataTypeImpl::GetType<float>(),
execution_providers.Get(xp_typ)->GetAllocator(0, OrtMemTypeDefault)->Info(), shape);
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
OrtValue* p_ml_value = frame.GetMutableNodeInputOrOutputMLValue(0);
Tensor* p_tensor = p_ml_value ? p_ml_value->GetMutable<Tensor>() : nullptr;
EXPECT_TRUE(p_tensor);
EXPECT_EQ(p_tensor->Shape(), shape);
EXPECT_EQ(p_tensor->DataType(), DataTypeImpl::GetType<float>());
//test share memory from tensor
TensorShape shape2(std::vector<int64_t>{3, 2});
OrtValue& mlvalue1 = *frame.GetMutableNodeInputOrOutputMLValue(start_index + 1);
status = frame.AllocateMLValueTensorPreAllocateBuffer(mlvalue1,
start_index,
DataTypeImpl::GetType<float>(),
p_tensor->Location(),
shape2);
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
const OrtValue* p_ml_value_const = frame.GetNodeInputOrOutputMLValue(1);
auto tensor2 = p_ml_value_const ? &(p_ml_value_const->Get<Tensor>()) : nullptr;
EXPECT_TRUE(tensor2);
EXPECT_EQ(tensor2->Shape(), shape2);
EXPECT_EQ(tensor2->template Data<float>(), p_tensor->template Data<float>());
}
TEST(ExecutionFrameTest, FeedInDataTest) {
onnxruntime::Model model("test");
onnxruntime::Graph& graph = model.MainGraph();
TypeProto tensor_float;
tensor_float.mutable_tensor_type()->set_elem_type(TensorProto_DataType_FLOAT);
onnxruntime::NodeArg input_def("X", &tensor_float), output_def("Y", &tensor_float);
graph.AddNode("node1", "Clip", "Clip operator", ArgMap{&input_def}, ArgMap{&output_def});
graph.Resolve();
auto cpu_allocator = TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault);
auto element_type = DataTypeImpl::GetType<float>();
TensorShape shape({3, 2});
//create fake ml value with owned buffer.
std::unique_ptr<Tensor> p_tensor = std::make_unique<Tensor>(element_type,
shape,
cpu_allocator);
OrtValue value;
value.Init(p_tensor.release(),
DataTypeImpl::GetType<Tensor>(),
DataTypeImpl::GetType<Tensor>()->GetDeleteFunc());
auto cpu_xp = CreateCPUExecutionProvider();
auto xp_typ = cpu_xp->Type();
KernelRegistryManager kernel_registry_manager;
ExecutionProviders execution_providers;
execution_providers.Add(xp_typ, std::move(cpu_xp));
EXPECT_TRUE(kernel_registry_manager.RegisterKernels(execution_providers).IsOK());
SessionState state{execution_providers, true};
state.SetGraphViewer(std::make_unique<GraphViewer>(graph));
OrtValueNameIdxMap& mlvalue_name_idx_map{state.GetOrtValueNameIdxMap()};
auto x_idx = mlvalue_name_idx_map.Add("X");
auto y_idx = mlvalue_name_idx_map.Add("Y");
state.CalculateNodeIndexInfo();
vector<OrtValue> outputs;
ExecutionFrame frame({x_idx}, {value}, {y_idx}, outputs, {}, state);
OrtValue* p_ml_value = frame.GetMutableNodeInputOrOutputMLValue(0);
Tensor* p_tensor_arg_0 = p_ml_value ? p_ml_value->GetMutable<Tensor>() : nullptr;
EXPECT_TRUE(p_tensor_arg_0);
EXPECT_EQ(p_tensor_arg_0->Shape(), shape);
EXPECT_EQ(p_tensor_arg_0->DataType(), DataTypeImpl::GetType<float>());
EXPECT_EQ(p_tensor_arg_0->MutableData<float>(), value.GetMutable<Tensor>()->MutableData<float>());
}
TEST(ExecutionFrameTest, MemPatternTest) {
auto cpu_xp = CreateCPUExecutionProvider();
auto xp_type = cpu_xp->Type();
std::unordered_map<std::string, int> domain_to_version;
domain_to_version[onnxruntime::kOnnxDomain] = 7;
onnxruntime::Model model("test", true, ModelMetaData(), IOnnxRuntimeOpSchemaRegistryList(), domain_to_version);
onnxruntime::Graph& graph = model.MainGraph();
TypeProto tensor_float;
tensor_float.mutable_tensor_type()->set_elem_type(TensorProto_DataType_FLOAT);
onnxruntime::NodeArg input_def1("X1", &tensor_float),
input_def2("X2", &tensor_float),
input_def3("X3", &tensor_float),
gemm1_out_def("T1", &tensor_float),
gemm2_out_def("T2", &tensor_float),
clip_out_def("T3", &tensor_float);
graph.AddNode("node1", "MatMul", "gemm1", ArgMap{&input_def1, &input_def2}, ArgMap{&gemm1_out_def})
.SetExecutionProviderType(xp_type);
graph.AddNode("node2", "MatMul", "gemm2", ArgMap{&gemm1_out_def, &input_def3}, ArgMap{&gemm2_out_def})
.SetExecutionProviderType(xp_type);
graph.AddNode("node3", "Clip", "clip1", ArgMap{&gemm2_out_def}, ArgMap{&clip_out_def})
.SetExecutionProviderType(xp_type);
auto status = graph.Resolve();
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
KernelRegistryManager kernel_registry_manager;
ExecutionProviders execution_providers;
execution_providers.Add(xp_type, std::move(cpu_xp));
kernel_registry_manager.RegisterKernels(execution_providers);
//1. prepare input
SessionState state{execution_providers, true};
state.SetGraphViewer(std::make_unique<GraphViewer>(graph));
OrtValueNameIdxMap& mlvalue_name_idx_map{state.GetOrtValueNameIdxMap()};
auto x1_idx = mlvalue_name_idx_map.Add("X1");
auto x2_idx = mlvalue_name_idx_map.Add("X2");
auto x3_idx = mlvalue_name_idx_map.Add("X3");
mlvalue_name_idx_map.Add("T1");
mlvalue_name_idx_map.Add("T2");
auto t3_idx = mlvalue_name_idx_map.Add("T3");
auto cpu_allocator = execution_providers.Get(xp_type)->GetAllocator(0, OrtMemTypeDefault);
OrtValue v1, v2, v3;
CreateMLValue<float>(cpu_allocator,
std::vector<int64_t>{1, 2},
std::vector<float>{1.0f, 1.0f}, &v1);
CreateMLValue<float>(cpu_allocator,
std::vector<int64_t>{2, 2},
std::vector<float>(4, 1.0f), &v2);
CreateMLValue<float>(cpu_allocator,
std::vector<int64_t>{2, 3},
std::vector<float>(6, 1.0f), &v3);
std::unique_ptr<SequentialExecutionPlan> p_seq_exec_plan = std::make_unique<SequentialExecutionPlan>();
SequentialPlannerContext context(false);
status = SequentialPlanner::CreatePlan(nullptr, GraphViewer(graph), {}, execution_providers, kernel_registry_manager,
mlvalue_name_idx_map, context, p_seq_exec_plan);
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
state.SetExecutionPlan(std::move(p_seq_exec_plan));
state.CalculateNodeIndexInfo();
vector<OrtValue> outputs;
ExecutionFrame frame({x1_idx, x2_idx, x3_idx}, {v1, v2, v3}, {t3_idx}, outputs, {}, state);
OrtValue& mlvalue3 = *frame.GetMutableNodeInputOrOutputMLValue(3);
OrtValue& mlvalue4 = *frame.GetMutableNodeInputOrOutputMLValue(4);
OrtValue& mlvalue5 = *frame.GetMutableNodeInputOrOutputMLValue(5);
status = frame.AllocateMLValueTensorSelfOwnBuffer(mlvalue3, 3,
DataTypeImpl::GetType<float>(),
cpu_allocator->Info(),
TensorShape(std::vector<int64_t>{2, 2}));
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
status = frame.AllocateMLValueTensorSelfOwnBuffer(mlvalue4, 4,
DataTypeImpl::GetType<float>(),
cpu_allocator->Info(),
TensorShape(std::vector<int64_t>{2, 3}));
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
status = frame.AllocateMLValueTensorSelfOwnBuffer(mlvalue5, 5,
DataTypeImpl::GetType<float>(),
cpu_allocator->Info(),
TensorShape(std::vector<int64_t>{2, 3}));
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
MemoryPatternGroup pattern;
status = frame.GeneratePatterns(&pattern);
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
EXPECT_EQ(pattern.patterns.size(), pattern.locations.size());
EXPECT_EQ(pattern.patterns.size(), 1);
auto p = pattern.GetPatterns(cpu_allocator->Info());
EXPECT_EQ(p->PeakSize(), 2 * 64); // each allocation is 64-byte aligned
EXPECT_EQ(p->GetBlock(3)->offset_, 0);
EXPECT_EQ(p->GetBlock(4)->offset_, 64);
}
TEST(ExecutionFrameTest, BadModelInvalidDimParamUsage) {
// load model with 2 Scan ops that both incorrectly use shapes of { 'None', 'None' } for their outputs.
// as 'None' is not a special value it's treated as a variable name, leading to a runtime error when we
// attempt to re-use the output from the first Scan node for the second. validate we detect this and error out.
SessionOptions so;
so.session_logid = "BadModelInvalidDimParamUsage";
InferenceSession session_object{so, &DefaultLoggingManager()};
Status st;
ASSERT_TRUE((st = session_object.Load("testdata/invalid_dim_param_value_repetition.onnx")).IsOK()) << st;
ASSERT_TRUE((st = session_object.Initialize()).IsOK()) << st;
std::vector<int64_t> dims_X = {10, 6};
std::vector<float> values_X;
values_X.reserve(60);
for (int i = 0; i < 60; ++i) {
values_X.push_back(float(i));
}
OrtValue ml_value;
CreateMLValue<float>(TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault), dims_X, values_X, &ml_value);
NameMLValMap feeds;
feeds.insert(std::make_pair("X", ml_value));
// prepare outputs
std::vector<std::string> output_names;
output_names.push_back("Y");
std::vector<OrtValue> fetches;
// Now run
RunOptions run_options;
st = session_object.Run(run_options, feeds, output_names, &fetches);
EXPECT_FALSE(st.IsOK()) << st;
EXPECT_THAT(st.ErrorMessage(), testing::HasSubstr("Shape mismatch attempting to re-use buffer."));
}
} // namespace test
} // namespace onnxruntime
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