1 /* Copyright 2020 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 #include "tensorflow/c/eager/unified_api_testutil.h"
16
17 #include "absl/container/flat_hash_set.h"
18 #include "tensorflow/c/eager/c_api_experimental.h"
19 #include "tensorflow/c/eager/c_api_test_util.h"
20 #include "tensorflow/c/eager/c_api_unified_experimental.h"
21 #include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
22 #include "tensorflow/c/tf_status.h"
23 #include "tensorflow/c/tf_status_helper.h"
24 #include "tensorflow/core/framework/tensor_shape.h"
25 #include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
26 #include "tensorflow/core/platform/errors.h"
27
28 namespace tensorflow {
29
BuildFunction(const char * fn_name)30 AbstractContext* BuildFunction(const char* fn_name) {
31 std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
32 TF_NewStatus(), TF_DeleteStatus);
33 TF_ExecutionContext* graph_ctx = TF_CreateFunction(fn_name, status.get());
34 return unwrap(graph_ctx);
35 }
36
CreateParamsForInputs(AbstractContext * ctx,absl::Span<AbstractTensorHandle * const> inputs,std::vector<AbstractTensorHandle * > * params)37 Status CreateParamsForInputs(AbstractContext* ctx,
38 absl::Span<AbstractTensorHandle* const> inputs,
39 std::vector<AbstractTensorHandle*>* params) {
40 tracing::TracingTensorHandle* handle = nullptr;
41 for (auto input : inputs) {
42 PartialTensorShape shape;
43 TF_RETURN_IF_ERROR(input->Shape(&shape));
44 TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
45 input->DataType(), shape, &handle));
46 params->emplace_back(handle);
47 }
48 return Status::OK();
49 }
50
51 // Runs `model` maybe wrapped in a function.
RunModel(Model model,AbstractContext * ctx,absl::Span<AbstractTensorHandle * const> inputs,absl::Span<AbstractTensorHandle * > outputs,bool use_function)52 Status RunModel(Model model, AbstractContext* ctx,
53 absl::Span<AbstractTensorHandle* const> inputs,
54 absl::Span<AbstractTensorHandle*> outputs, bool use_function) {
55 if (use_function) {
56 const char* fn_name = "test_fn";
57 std::unique_ptr<AbstractFunction> scoped_func;
58 // Returning null tensors from a tf.function is not supported, so we keep
59 // track of indices in the model's outputs are nullptr in this set.
60 // The FunctionDef only outputs the non-null tensors. We later pad the
61 // function op outputs to have nullptrs at the `null_indices`.
62 absl::flat_hash_set<int> null_indices;
63 {
64 AbstractContextPtr func_ctx(BuildFunction(fn_name));
65 std::vector<AbstractTensorHandle*> func_inputs;
66 func_inputs.reserve(inputs.size());
67 TF_RETURN_IF_ERROR(
68 CreateParamsForInputs(func_ctx.get(), inputs, &func_inputs));
69 std::vector<AbstractTensorHandle*> model_outputs;
70 model_outputs.resize(outputs.size());
71 TF_RETURN_IF_ERROR(model(func_ctx.get(), absl::MakeSpan(func_inputs),
72 absl::MakeSpan(model_outputs)));
73 for (auto func_input : func_inputs) {
74 func_input->Unref();
75 }
76 AbstractFunction* func = nullptr;
77 OutputList output_list;
78 output_list.expected_num_outputs = 0;
79 output_list.outputs.reserve(outputs.size());
80 for (int i = 0; i < model_outputs.size(); i++) {
81 if (model_outputs[i]) {
82 output_list.outputs.emplace_back(model_outputs[i]);
83 output_list.expected_num_outputs += 1;
84 } else {
85 null_indices.insert(i);
86 }
87 }
88 TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(func_ctx.get())
89 ->Finalize(&output_list, &func));
90 scoped_func.reset(func);
91 for (auto output : output_list.outputs) {
92 output->Unref();
93 }
94 TF_RETURN_IF_ERROR(ctx->RegisterFunction(func));
95 }
96
97 AbstractOperationPtr fn_op(ctx->CreateOperation());
98 TF_RETURN_IF_ERROR(fn_op->Reset(fn_name, /*raw_device_name=*/nullptr));
99 for (auto input : inputs) {
100 TF_RETURN_IF_ERROR(fn_op->AddInput(input));
101 }
102 int retvals = outputs.size() - null_indices.size();
103 std::vector<AbstractTensorHandle*> fn_outputs(retvals);
104 TF_RETURN_IF_ERROR(fn_op->Execute(
105 absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()),
106 &retvals));
107 int skipped_indices = 0;
108 for (int i = 0; i < outputs.size(); i++) {
109 if (!null_indices.contains(i)) {
110 outputs[i] = fn_outputs[i - skipped_indices];
111 } else {
112 skipped_indices += 1;
113 }
114 }
115 TF_RETURN_IF_ERROR(ctx->RemoveFunction(fn_name));
116 return Status::OK();
117 } else {
118 return model(ctx, inputs, outputs);
119 }
120 }
121
BuildImmediateExecutionContext(bool use_tfrt,AbstractContext ** ctx)122 Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx) {
123 std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
124 TF_NewStatus(), TF_DeleteStatus);
125 TFE_ContextOptions* opts = TFE_NewContextOptions();
126 TFE_ContextOptionsSetTfrt(opts, use_tfrt);
127 *ctx = unwrap(TF_NewEagerExecutionContext(opts, status.get()));
128 TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
129 TFE_DeleteContextOptions(opts);
130 return Status::OK();
131 }
132
TestScalarTensorHandle(AbstractContext * ctx,float value,AbstractTensorHandle ** tensor)133 Status TestScalarTensorHandle(AbstractContext* ctx, float value,
134 AbstractTensorHandle** tensor) {
135 std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
136 TF_NewStatus(), TF_DeleteStatus);
137 TFE_Context* eager_ctx =
138 TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());
139 TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
140 TFE_TensorHandle* input_eager = TestScalarTensorHandle(eager_ctx, value);
141 *tensor =
142 unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
143 return Status::OK();
144 }
145
TestTensorHandleWithDimsFloat(AbstractContext * ctx,float * data,int64_t * dims,int num_dims,AbstractTensorHandle ** tensor)146 Status TestTensorHandleWithDimsFloat(AbstractContext* ctx, float* data,
147 int64_t* dims, int num_dims,
148 AbstractTensorHandle** tensor) {
149 std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
150 TF_NewStatus(), TF_DeleteStatus);
151 TFE_Context* eager_ctx =
152 TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());
153 TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
154 TFE_TensorHandle* input_eager =
155 TestTensorHandleWithDimsFloat(eager_ctx, data, dims, num_dims);
156 *tensor =
157 unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
158 return Status::OK();
159 }
160
TestTensorHandleWithDimsInt(AbstractContext * ctx,int * data,int64_t * dims,int num_dims,AbstractTensorHandle ** tensor)161 Status TestTensorHandleWithDimsInt(AbstractContext* ctx, int* data,
162 int64_t* dims, int num_dims,
163 AbstractTensorHandle** tensor) {
164 std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
165 TF_NewStatus(), TF_DeleteStatus);
166 TFE_Context* eager_ctx =
167 TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());
168 TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
169 TFE_TensorHandle* input_eager =
170 TestTensorHandleWithDimsInt(eager_ctx, data, dims, num_dims);
171 *tensor =
172 unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
173 return Status::OK();
174 }
175
GetValue(AbstractTensorHandle * t,TF_Tensor ** result_tensor)176 Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor) {
177 std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
178 TF_NewStatus(), TF_DeleteStatus);
179 TFE_TensorHandle* result_t =
180 TF_AbstractTensorGetEagerTensor(wrap(t), status.get());
181 TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
182 *result_tensor = TFE_TensorHandleResolve(result_t, status.get());
183 return StatusFromTF_Status(status.get());
184 }
185
186 } // namespace tensorflow
187