1 | /* Copyright 2017 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 | #ifndef TENSORFLOW_LITE_MEMORY_PLANNER_H_ |
16 | #define TENSORFLOW_LITE_MEMORY_PLANNER_H_ |
17 | |
18 | #include <vector> |
19 | |
20 | #include "tensorflow/lite/c/common.h" |
21 | |
22 | namespace tflite { |
23 | |
24 | // A MemoryPlanner is responsible for planning and executing a number of |
25 | // memory-related operations that are necessary in TF Lite. |
26 | class MemoryPlanner { |
27 | public: |
28 | virtual ~MemoryPlanner() {} |
29 | |
30 | // Plans the necessary memory allocations. This is the MemoryPlanner's |
31 | // pre-processing step and is called when the graph structure is known but |
32 | // actual size of the tensors is not. |
33 | virtual TfLiteStatus PlanAllocations() = 0; |
34 | |
35 | // Allocates the necessary memory to execute all nodes in the interval |
36 | // [first_node, last_node]. |
37 | virtual TfLiteStatus ExecuteAllocations(int first_node, int last_node) = 0; |
38 | |
39 | // Invalidates allocations made earlier. This is called when tensors sizes |
40 | // have changed. All planned allocations remain, but can't be used until |
41 | // ExecuteAllocations() is called. |
42 | virtual TfLiteStatus ResetAllocations() = 0; |
43 | |
44 | // Invalidates allocations after the given node execution. |
45 | virtual TfLiteStatus ResetAllocationsAfter(int node) = 0; |
46 | |
47 | // NOTE: The following two methods modify the data pointers for all tensors on |
48 | // the non-persistent arena (inputs, outputs, intermediates). If the user has |
49 | // manually set the pointers for any of these, they would need to be set |
50 | // again. |
51 | |
52 | // This releases memory allocated for non-persistent tensors. |
53 | // It does NOT clear the allocation plan, but the memory can't be used |
54 | // until AcquireNonPersistentMemory() is called. |
55 | // It is safe to call Reset/PlanAllocations after this method, without calling |
56 | // ReleaseTemporaryAllocations in case tensor sizes change. |
57 | virtual TfLiteStatus ReleaseNonPersistentMemory() = 0; |
58 | |
59 | // Allocates the necessary memory to contain non-persistent tensors. |
60 | virtual TfLiteStatus AcquireNonPersistentMemory() = 0; |
61 | |
62 | // Returns true if the non-persistent memory is available. |
63 | virtual bool HasNonPersistentMemory() = 0; |
64 | |
65 | // Dumps the memory planning information against the specified op node |
66 | // execution plan (i.e. `execution_plan`) for the purpose of debugging. |
67 | virtual void DumpDebugInfo(const std::vector<int>& execution_plan) const = 0; |
68 | |
69 | // Returns a map of allocation information. It's only used for debugging. |
70 | virtual void GetAllocInfo(size_t *arena_size, |
71 | size_t *arena_persist_size) const = 0; |
72 | }; |
73 | |
74 | } // namespace tflite |
75 | |
76 | #endif // TENSORFLOW_LITE_MEMORY_PLANNER_H_ |
77 | |