1 /*
2  * Copyright (C) 2018 The Android Open Source Project
3  *
4  * Licensed under the Apache License, Version 2.0 (the "License");
5  * you may not use this file except in compliance with the License.
6  * You may obtain a copy of the License at
7  *
8  *      http://www.apache.org/licenses/LICENSE-2.0
9  *
10  * Unless required by applicable law or agreed to in writing, software
11  * distributed under the License is distributed on an "AS IS" BASIS,
12  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13  * See the License for the specific language governing permissions and
14  * limitations under the License.
15  */
16 
17 // Contains the implementation of the operations.
18 
19 #define LOG_TAG "Operations"
20 
21 #include <vector>
22 
23 #include "OperationResolver.h"
24 #include "Operations.h"
25 #include "Tracing.h"
26 
27 #ifdef NN_INCLUDE_CPU_IMPLEMENTATION
28 #include <tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h>
29 
30 #include "CpuOperationUtils.h"
31 #endif  // NN_INCLUDE_CPU_IMPLEMENTATION
32 
33 namespace android {
34 namespace nn {
35 namespace strided_slice {
36 
37 constexpr uint32_t kNumInputs = 7;
38 constexpr uint32_t kInputTensor = 0;
39 constexpr uint32_t kBeginTensor = 1;
40 constexpr uint32_t kEndTensor = 2;
41 constexpr uint32_t kStridesTensor = 3;
42 constexpr uint32_t kBeginMask = 4;
43 constexpr uint32_t kEndMask = 5;
44 constexpr uint32_t kShrinkAxisMask = 6;
45 
46 constexpr uint32_t kNumOutputs = 1;
47 constexpr uint32_t kOutputTensor = 0;
48 
49 #ifdef NN_INCLUDE_CPU_IMPLEMENTATION
50 namespace {
51 
52 template <typename T>
compute(const T * inputData,const Shape & inputShape,const int32_t * beginData,const int32_t * endData,const int32_t * stridesData,int32_t beginMask,int32_t endMask,int32_t shrinkAxisMask,T * outputData,const Shape & outputShape)53 bool compute(const T* inputData, const Shape& inputShape, const int32_t* beginData,
54              const int32_t* endData, const int32_t* stridesData, int32_t beginMask, int32_t endMask,
55              int32_t shrinkAxisMask, T* outputData, const Shape& outputShape) {
56     NNTRACE_TRANS("stridedSlice");
57     // This Op only supports 1-4D cases and since we use the reference 4D
58     // implementation, the 1-3D tensors are mapped to 4D.
59     const int kMaxDim = 4;
60 
61     std::vector<int> starts;
62     std::vector<int> stops;
63     std::vector<int> strides;
64 
65     int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape));
66     for (int32_t idx = numInputDims - 1; idx >= 0; --idx) {
67         starts.emplace_back(beginData[idx]);
68         stops.emplace_back(endData[idx]);
69         strides.emplace_back(stridesData[idx]);
70     }
71 
72     for (int i = numInputDims; i < kMaxDim; i++) {
73         starts.emplace_back(0);
74         stops.emplace_back(1);
75         strides.emplace_back(1);
76     }
77 
78     beginMask = ReverseMaskBits(beginMask, numInputDims);
79     endMask = ReverseMaskBits(endMask, numInputDims);
80     shrinkAxisMask = ReverseMaskBits(shrinkAxisMask, numInputDims);
81 
82     tflite::reference_ops::StridedSlice(inputData, convertShapeToDims(inputShape), beginMask,
83                                         endMask, shrinkAxisMask, starts, stops, strides, outputData,
84                                         convertShapeToDims(outputShape));
85 
86     return true;
87 }
88 
89 template <typename T>
executeTyped(IOperationExecutionContext * context)90 bool executeTyped(IOperationExecutionContext* context) {
91     return compute<T>(
92             context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor),
93             context->getInputBuffer<int32_t>(kBeginTensor),
94             context->getInputBuffer<int32_t>(kEndTensor),
95             context->getInputBuffer<int32_t>(kStridesTensor),
96             context->getInputValue<int32_t>(kBeginMask), context->getInputValue<int32_t>(kEndMask),
97             context->getInputValue<int32_t>(kShrinkAxisMask),
98             context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor));
99 }
100 
101 }  // namespace
102 #endif  // NN_INCLUDE_CPU_IMPLEMENTATION
103 
validate(const IOperationValidationContext * context)104 Result<Version> validate(const IOperationValidationContext* context) {
105     NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
106     NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
107     OperandType inputType = context->getInputType(kInputTensor);
108     NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
109                  inputType == OperandType::TENSOR_FLOAT32 ||
110                  inputType == OperandType::TENSOR_QUANT8_ASYMM ||
111                  inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
112             << "Unsupported input operand type for STRIDED_SLICE op: " << inputType;
113 
114     Version minSupportedVersion;
115     if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
116         minSupportedVersion = Version::ANDROID_R;
117     } else if (inputType == OperandType::TENSOR_FLOAT16) {
118         minSupportedVersion = Version::ANDROID_Q;
119     } else {
120         minSupportedVersion = Version::ANDROID_P;
121     }
122 
123     NN_RET_CHECK(validateInputTypes(context, {
124                                                      inputType,
125                                                      OperandType::TENSOR_INT32,
126                                                      OperandType::TENSOR_INT32,
127                                                      OperandType::TENSOR_INT32,
128                                                      OperandType::INT32,
129                                                      OperandType::INT32,
130                                                      OperandType::INT32,
131                                              }));
132     NN_RET_CHECK(validateOutputTypes(context, {inputType}));
133     const Shape& input = context->getInputShape(kInputTensor);
134     if (hasKnownRank(input)) {
135         NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
136     }
137     return minSupportedVersion;
138 }
139 
140 #ifdef NN_INCLUDE_CPU_IMPLEMENTATION
prepare(IOperationExecutionContext * context)141 bool prepare(IOperationExecutionContext* context) {
142     // StridedSlice op only supports 1D-4D input arrays.
143     const Shape& inputShape = context->getInputShape(kInputTensor);
144     uint32_t numInputDims = getNumberOfDimensions(inputShape);
145     NN_OPS_CHECK(numInputDims <= 4);
146 
147     const Shape& beginShape = context->getInputShape(kBeginTensor);
148     const Shape& endShape = context->getInputShape(kEndTensor);
149     const Shape& stridesShape = context->getInputShape(kStridesTensor);
150 
151     NN_OPS_CHECK(getNumberOfDimensions(beginShape) == 1);
152     NN_OPS_CHECK(getNumberOfDimensions(endShape) == 1);
153     NN_OPS_CHECK(getNumberOfDimensions(stridesShape) == 1);
154 
155     NN_OPS_CHECK(getSizeOfDimension(beginShape, 0) == numInputDims);
156     NN_OPS_CHECK(getSizeOfDimension(endShape, 0) == numInputDims);
157     NN_OPS_CHECK(getSizeOfDimension(stridesShape, 0) == numInputDims);
158 
159     NN_OPS_CHECK(beginShape.type == OperandType::TENSOR_INT32);
160     NN_OPS_CHECK(endShape.type == OperandType::TENSOR_INT32);
161     NN_OPS_CHECK(stridesShape.type == OperandType::TENSOR_INT32);
162 
163     const int32_t* beginData = context->getInputBuffer<int32_t>(kBeginTensor);
164     const int32_t* endData = context->getInputBuffer<int32_t>(kEndTensor);
165     const int32_t* stridesData = context->getInputBuffer<int32_t>(kStridesTensor);
166 
167     const int32_t beginMask = context->getInputValue<int32_t>(kBeginMask);
168     const int32_t endMask = context->getInputValue<int32_t>(kEndMask);
169     const int32_t shrinkAxisMask = context->getInputValue<int32_t>(kShrinkAxisMask);
170 
171     // Determine size of output tensor and map indices
172     std::vector<uint32_t> outDims;
173     for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); idx++) {
174         int32_t dim = static_cast<int32_t>(getSizeOfDimension(inputShape, idx));
175         int32_t stride = stridesData[idx];
176         // stride value has to be non-zero
177         NN_OPS_CHECK(stride != 0);
178         bool positiveStride = stride > 0;
179 
180         int32_t begin = beginMask & (1 << idx) ? positiveStride ? 0 : dim - 1
181                                                : ClampedIndex(beginData[idx], dim, positiveStride);
182         int32_t end = endMask & (1 << idx) ? positiveStride ? dim : -1
183                                            : ClampedIndex(endData[idx], dim, positiveStride);
184 
185         // This is valid for both positive and negative strides
186         int32_t outDim = ceil((end - begin) / static_cast<float>(stride));
187         outDim = outDim < 0 ? 0 : static_cast<uint32_t>(outDim);
188         if (!(shrinkAxisMask & (1 << idx))) {
189             outDims.push_back(outDim);
190         } else {
191             // Only positive stride is allowed on non-range indexing (i.e. shrinkMask is set).
192             NN_RET_CHECK_GT(stride, 0) << "index = " << idx;
193             NN_RET_CHECK_EQ(outDim, 1) << "index = " << idx;
194         }
195     }
196 
197     // Handle the case when all dimensions are removed
198     if (outDims.empty()) {
199         outDims.push_back(1);
200     }
201 
202     Shape outputShape = context->getOutputShape(kOutputTensor);
203     NN_RET_CHECK(SetShape(inputShape, &outputShape));
204     outputShape.dimensions = outDims;
205     return context->setOutputShape(kOutputTensor, outputShape);
206 }
207 
execute(IOperationExecutionContext * context)208 bool execute(IOperationExecutionContext* context) {
209     switch (context->getInputType(kInputTensor)) {
210         case OperandType::TENSOR_FLOAT16:
211             return executeTyped<_Float16>(context);
212         case OperandType::TENSOR_FLOAT32:
213             return executeTyped<float>(context);
214         case OperandType::TENSOR_QUANT8_ASYMM:
215             return executeTyped<uint8_t>(context);
216         case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
217             return executeTyped<int8_t>(context);
218         default:
219             NN_RET_CHECK_FAIL() << "Unsupported tensor type for STRIDED_SLICE op.";
220     }
221 }
222 #endif  // NN_INCLUDE_CPU_IMPLEMENTATION
223 
224 }  // namespace strided_slice
225 
226 NN_REGISTER_OPERATION(STRIDED_SLICE, "STRIDED_SLICE", strided_slice::validate,
227                       strided_slice::prepare, strided_slice::execute);
228 
229 }  // namespace nn
230 }  // namespace android
231