1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2
3 Licensed under the Apache License, Version 2.0 (the "License");
4 you may not use this file except in compliance with the License.
5 You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9 Unless required by applicable law or agreed to in writing, software
10 distributed under the License is distributed on an "AS IS" BASIS,
11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 See the License for the specific language governing permissions and
13 limitations under the License.
14 ==============================================================================*/
15
16 #ifndef TENSORFLOW_KERNELS_OPS_TESTUTIL_H_
17 #define TENSORFLOW_KERNELS_OPS_TESTUTIL_H_
18
19 #include <memory>
20 #include <vector>
21
22 #include "tensorflow/core/common_runtime/device.h"
23 #include "tensorflow/core/common_runtime/device_factory.h"
24 #include "tensorflow/core/framework/allocator.h"
25 #include "tensorflow/core/framework/device_base.h"
26 #include "tensorflow/core/framework/graph.pb.h"
27 #include "tensorflow/core/framework/node_def.pb.h"
28 #include "tensorflow/core/framework/op_kernel.h"
29 #include "tensorflow/core/framework/resource_mgr.h"
30 #include "tensorflow/core/framework/tensor.h"
31 #include "tensorflow/core/framework/tensor_testutil.h"
32 #include "tensorflow/core/framework/types.h"
33 #include "tensorflow/core/framework/types.pb.h"
34 #include "tensorflow/core/lib/core/status.h"
35 #include "tensorflow/core/lib/core/status_test_util.h"
36 #include "tensorflow/core/lib/gtl/array_slice.h"
37 #include "tensorflow/core/lib/gtl/inlined_vector.h"
38 #include "tensorflow/core/lib/gtl/stl_util.h"
39 #include "tensorflow/core/platform/env.h"
40 #include "tensorflow/core/platform/logging.h"
41 #include "tensorflow/core/platform/macros.h"
42 #include "tensorflow/core/platform/mutex.h"
43 #include "tensorflow/core/platform/test.h"
44 #include "tensorflow/core/platform/types.h"
45 #include "tensorflow/core/public/session_options.h"
46 #include "tensorflow/core/public/version.h"
47 #include "tensorflow/core/util/tensor_slice_reader_cache.h"
48
49 namespace tensorflow {
50 namespace test {
51
SetOutputAttrs(OpKernelContext::Params * params,std::vector<AllocatorAttributes> * attrs)52 inline void SetOutputAttrs(OpKernelContext::Params* params,
53 std::vector<AllocatorAttributes>* attrs) {
54 attrs->clear();
55 for (int index = 0; index < params->op_kernel->num_outputs(); index++) {
56 AllocatorAttributes attr;
57 const bool on_host =
58 (params->op_kernel->output_memory_types()[index] == HOST_MEMORY);
59 attr.set_on_host(on_host);
60 attrs->push_back(attr);
61 }
62 params->output_attr_array = gtl::vector_as_array(attrs);
63 }
64
65 } // namespace test
66
67 // Helpful functions to test operators.
68 //
69 // This class will eventually be replaced / heavily modified
70 // to use the BrainClient interface.
71 class OpsTestBase : public ::testing::Test {
72 public:
OpsTestBase()73 OpsTestBase()
74 : device_(DeviceFactory::NewDevice("CPU", {}, "/job:a/replica:0/task:0")),
75 device_type_(DEVICE_CPU) {
76 CHECK(device_.get()) << "Could not create CPU device";
77 allocator_ = device_->GetAllocator(AllocatorAttributes());
78 }
79
~OpsTestBase()80 ~OpsTestBase() override {
81 gtl::STLDeleteElements(&tensors_);
82 gtl::STLDeleteElements(&managed_outputs_);
83 context_.reset(nullptr);
84 params_.reset(nullptr);
85 }
86
87 // Allow kernel unit tests to run on GPU
88 void SetDevice(const DeviceType& device_type, std::unique_ptr<Device> device);
89
set_node_def(const NodeDef & node_def)90 void set_node_def(const NodeDef& node_def) { node_def_.CopyFrom(node_def); }
91
92 // Clients can manipulate the underlying NodeDef via this accessor.
node_def()93 NodeDef* node_def() { return &node_def_; }
94
95 // Initializes an operator that takes in 'input_types' as input
96 // and output types as output.
97 //
98 // Returns the status of initialization.
InitOp()99 Status InitOp() { return InitOpWithGraphVersion(TF_GRAPH_DEF_VERSION); }
100
101 // Only use this directly if you have a deprecated op that you need to test.
InitOpWithGraphVersion(int graph_def_version)102 Status InitOpWithGraphVersion(int graph_def_version) {
103 Status status;
104 kernel_ = CreateOpKernel(device_type_, device_.get(), allocator(),
105 node_def_, graph_def_version, &status);
106 if (kernel_ != nullptr) input_types_ = kernel_->input_types();
107 return status;
108 }
109
110 // Adds an input for every element described by the shape.
111 // 'input_mapping' maps an index (0...NumElements(shape)) to a
112 // value.
113 //
114 // TODO(vrv): Replace with something like a BrainClient Feed.
115 template <typename T>
AddInput(const TensorShape & shape,std::function<T (int)> input_mapping)116 void AddInput(const TensorShape& shape, std::function<T(int)> input_mapping) {
117 test::FillFn(AddInput(DataTypeToEnum<T>::v(), shape), input_mapping);
118 }
119
120 // Like AddInput but takes in an explicit arrayslice of data.
121 template <typename T>
AddInputFromArray(const TensorShape & shape,const gtl::ArraySlice<T> & data)122 void AddInputFromArray(const TensorShape& shape,
123 const gtl::ArraySlice<T>& data) {
124 test::FillValues<T>(AddInput(DataTypeToEnum<T>::v(), shape), data);
125 }
126
127 // Convenience function to add an input and populate it with the elements from
128 // an initializer list converting the types as needed.
129 template <typename T, typename SrcType>
AddInputFromList(const TensorShape & shape,std::initializer_list<SrcType> data)130 void AddInputFromList(const TensorShape& shape,
131 std::initializer_list<SrcType> data) {
132 test::FillValues<T>(AddInput(DataTypeToEnum<T>::v(), shape), data);
133 }
134
135 // Adds a Resource type as input. If <container> is empty, uses the default
136 // container name.
137 template <typename T>
AddResourceInput(const string & container,const string & name,T * resource)138 void AddResourceInput(const string& container, const string& name,
139 T* resource) {
140 CHECK_GT(input_types_.size(), inputs_.size())
141 << "Adding more inputs than types; perhaps you need to call MakeOp";
142 ResourceMgr* rm = device_->resource_manager();
143 EXPECT_TRUE(
144 rm->Create(container == "" ? rm->default_container() : container, name,
145 resource)
146 .ok());
147 TypeIndex type_index = MakeTypeIndex<T>();
148 ResourceHandle handle;
149 handle.set_device(device_->name());
150 handle.set_container(container);
151 handle.set_name(name);
152 handle.set_hash_code(type_index.hash_code());
153 handle.set_maybe_type_name(type_index.name());
154 Tensor* input = new Tensor(allocator(), DT_RESOURCE, TensorShape({}));
155 input->scalar<ResourceHandle>()() = handle;
156 tensors_.push_back(input);
157 inputs_.push_back({nullptr, input});
158 }
159
160 // Runs an operation producing 'num_outputs' outputs.
161 //
162 // Returns the context's status after running the operation.
RunOpKernel()163 Status RunOpKernel() {
164 // Make sure the old OpKernelContext is deleted before the Params
165 // it was using.
166 context_.reset(nullptr);
167
168 params_.reset(new OpKernelContext::Params);
169 params_.get()->device = device_.get();
170 params_.get()->frame_iter = FrameAndIter(0, 0);
171 params_.get()->inputs = &inputs_;
172 params_.get()->op_kernel = kernel_.get();
173 step_container_.reset(new ScopedStepContainer(0, [](const string&) {}));
174 params_->step_container = step_container_.get();
175 std::vector<AllocatorAttributes> attrs;
176 test::SetOutputAttrs(params_.get(), &attrs);
177 checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_wrapper;
178 params_.get()->slice_reader_cache = &slice_reader_cache_wrapper;
179 params_.get()->resource_manager = device_.get()->resource_manager();
180
181 context_.reset(new OpKernelContext(params_.get()));
182 device_->Compute(kernel_.get(), context_.get());
183 return context_->status();
184 }
185
186 // Returns the tensor input for 'input_index'.
187 //
188 // REQUIRES: 0 <= input_index < context_->num_inputs()
GetInput(int input_index)189 const Tensor& GetInput(int input_index) const {
190 CHECK_LT(input_index, context_->num_inputs());
191 CHECK(!IsRefType(context_->input_dtype(input_index)));
192 return context_->input(input_index);
193 }
194
mutable_input(int input_index)195 TensorValue mutable_input(int input_index) {
196 CHECK_LT(input_index, inputs_.size());
197 return inputs_[input_index];
198 }
199 // Returns the tensor output for 'output_index'.
200 //
201 // REQUIRES: 0 <= output_index < context_->num_outputs()
202 Tensor* GetOutput(int output_index);
203
allocator()204 Allocator* allocator() { return allocator_; }
205
output_types()206 const DataTypeVector& output_types() const { return kernel_->output_types(); }
207
208 private:
AddInput(DataType dtype,const TensorShape & shape)209 Tensor* AddInput(DataType dtype, const TensorShape& shape) {
210 CHECK_GT(input_types_.size(), inputs_.size())
211 << "Adding more inputs than types; perhaps you need to call MakeOp";
212 bool is_ref = IsRefType(input_types_[inputs_.size()]);
213 Tensor* input = new Tensor(allocator(), dtype, shape);
214 tensors_.push_back(input);
215 if (is_ref) {
216 CHECK_EQ(RemoveRefType(input_types_[inputs_.size()]), dtype);
217 inputs_.push_back({&lock_for_refs_, input});
218 } else {
219 CHECK_EQ(input_types_[inputs_.size()], dtype);
220 inputs_.push_back({nullptr, input});
221 }
222 return input;
223 }
224
225 protected:
226 std::unique_ptr<Device> device_;
227 // The device allocator, or the managed_allocator_ below if running on GPU.
228 Allocator* allocator_;
229
230 std::unique_ptr<OpKernel> kernel_;
231 std::unique_ptr<ScopedStepContainer> step_container_;
232 NodeDef node_def_;
233 DataTypeVector input_types_;
234 DeviceType device_type_;
235
236 mutex lock_for_refs_; // Used as the Mutex for inputs added as refs
237
238 gtl::InlinedVector<TensorValue, 4> inputs_;
239 // Owns Tensors.
240 std::vector<Tensor*> tensors_;
241 // Copies of the outputs in unified memory (host and device accessible).
242 std::vector<Tensor*> managed_outputs_;
243
244 std::unique_ptr<OpKernelContext::Params> params_;
245 std::unique_ptr<OpKernelContext> context_;
246 // Unified memory allocator, only used when running on GPU.
247 std::unique_ptr<Allocator> managed_allocator_;
248
249 private:
250 TF_DISALLOW_COPY_AND_ASSIGN(OpsTestBase);
251 };
252
253 } // namespace tensorflow
254
255 #endif // TENSORFLOW_KERNELS_OPS_TESTUTIL_H_
256