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/packages/modules/NeuralNetworks/common/operations/
DL2Normalization.cpp48 inline bool l2normFloat32Impl(const float* inputData, const Shape& inputShape, int32_t axis, in l2normFloat32Impl() argument
52 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in l2normFloat32Impl()
53 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in l2normFloat32Impl()
55 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normFloat32Impl()
76 inline bool l2normQuant8Impl(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8Impl() argument
79 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in l2normQuant8Impl()
80 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in l2normQuant8Impl()
82 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normQuant8Impl()
108 inline bool l2normQuant8SignedImpl(const int8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8SignedImpl() argument
111 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in l2normQuant8SignedImpl()
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DSplit.cpp29 bool splitGeneric(const Scalar* inputData, const Shape& inputShape, int32_t axis, in splitGeneric() argument
32 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in splitGeneric()
34 for (int i = 0; i < axis; ++i) { in splitGeneric()
39 for (int i = axis + 1; i < concatDimensions; ++i) { in splitGeneric()
46 const int copySize = outputShapes[i].dimensions[axis] * baseInnerSize; in splitGeneric()
55 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in splitFloat16() argument
59 return splitGeneric<_Float16>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat16()
62 bool splitFloat32(const float* inputData, const Shape& inputShape, int32_t axis, in splitFloat32() argument
66 return splitGeneric<float>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat32()
69 bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8() argument
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DLocalResponseNormalization.cpp53 int32_t axis, float* outputData, in localResponseNormFloat32Impl() argument
56 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in localResponseNormFloat32Impl()
57 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in localResponseNormFloat32Impl()
59 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in localResponseNormFloat32Impl()
83 T beta, int32_t axis, T* outputData, const Shape& outputShape);
87 float bias, float alpha, float beta, int32_t axis, float* outputData, in localResponseNorm() argument
90 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in localResponseNorm()
91 radius = std::min(radius, static_cast<int32_t>(inputShape.dimensions[axis])); in localResponseNorm()
93 if (axis == ndim - 1) { in localResponseNorm()
102 return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis, in localResponseNorm()
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DSoftmax.cpp54 int32_t axis, float* outputData, const Shape& outputShape) { in softmaxSlowFloat32() argument
56 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in softmaxSlowFloat32()
57 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in softmaxSlowFloat32()
59 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in softmaxSlowFloat32()
85 bool softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxFloat32() argument
88 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in softmaxFloat32()
90 if (axis == ndim - 1) { in softmaxFloat32()
97 return softmaxSlowFloat32(inputData, inputShape, beta, axis, outputData, outputShape); in softmaxFloat32()
102 int32_t axis, _Float16* outputData, const Shape& outputShape) { in softmaxFloat16() argument
108 softmaxFloat32(inputData_float32.data(), inputShape, beta, axis, outputData_float32.data(), in softmaxFloat16()
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DGather.cpp40 inline bool eval(const T* inputData, const Shape& inputShape, int32_t axis, in eval() argument
42 const auto outerSize = getNumberOfElements(inputShape, 0, axis); in eval()
43 const auto axisSize = getSizeOfDimension(inputShape, axis); in eval()
45 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in eval()
84 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in prepare() local
85 NN_RET_CHECK(handleNegativeAxis(input, &axis)); in prepare()
92 input.dimensions.begin() + axis); in prepare()
95 output.dimensions.insert(output.dimensions.end(), input.dimensions.begin() + axis + 1, in prepare()
102 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in execute() local
103 NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis)); in execute()
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DChannelShuffle.cpp38 inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis, in eval() argument
40 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in eval()
41 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in eval()
43 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in eval()
85 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in prepare() local
86 NN_RET_CHECK(handleNegativeAxis(input, &axis)); in prepare()
88 NN_RET_CHECK(getSizeOfDimension(input, axis) % numGroups == 0); in prepare()
94 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in execute() local
95 NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis)); in execute()
99 context->getInputShape(kInputTensor), numGroups, axis, in execute()
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DArgMinMax.cpp29 static void argMinMaxImpl(const In* inputData, const Shape& inputShape, int32_t axis, bool isArgMin, in argMinMaxImpl() argument
31 const int outerSize = getNumberOfElements(inputShape, 0, axis); in argMinMaxImpl()
32 const int axisSize = getSizeOfDimension(inputShape, axis); in argMinMaxImpl()
34 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in argMinMaxImpl()
51 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32 axis, bool isArgMin, in argMinMaxGeneric() argument
54 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in argMinMaxGeneric()
59 argMinMaxImpl(reinterpret_cast<const dataType*>(inputData), inputShape, axis, isArgMin, \ in argMinMaxGeneric()
DConcatenation.cpp51 const std::vector<Shape>& inputShapes, int32_t axis, T* outputData, in concatenation() argument
63 getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(), in concatenation()
71 const std::vector<Shape>& inputShapes, int32_t axis, in concatenation() argument
88 getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(), in concatenation()
187 int32_t axis = context->getInputValue<int32_t>(numInputs - 1); in prepare() local
188 NN_RET_CHECK_GE(axis, 0); in prepare()
189 NN_RET_CHECK_LT(axis, numDimensions); in prepare()
192 uint32_t sumAxis = getSizeOfDimension(input0, axis); in prepare()
198 if (d == axis) { in prepare()
199 sumAxis += getSizeOfDimension(input, axis); in prepare()
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DSimpleMath.cpp33 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis, in meanFloat16() argument
41 meanGeneric<float, float>(inputDataFloat32.data(), inputShape, axis, axisShape, keepDims, in meanFloat16()
48 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape, in meanGeneric() argument
69 getNumberOfDimensions(outputShape), axis, axisSize, keepDims, scratchBuffer, in meanGeneric()
78 const int32_t* axis, const Shape& axisShape, bool keepDims,
81 const int32_t* axis, const Shape& axisShape,
85 const int32_t* axis, const Shape& axisShape,
DLogSoftmax.cpp42 inline bool compute(const T* input, const Shape& shape, T beta, uint32_t axis, T* output) { in compute() argument
43 const uint32_t outerSize = getNumberOfElements(shape, 0, axis); in compute()
44 const uint32_t axisSize = getSizeOfDimension(shape, axis); in compute()
45 const uint32_t innerSize = getNumberOfElements(shape, axis + 1, getNumberOfDimensions(shape)); in compute()
98 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in execute() local
99 NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis)); in execute()
104 context->getInputValue<_Float16>(kInputBeta), axis, in execute()
109 context->getInputValue<float>(kInputBeta), axis, in execute()
DReduce.cpp141 int32_t axis = axes[i]; in prepare() local
142 NN_RET_CHECK(handleNegativeAxis(inputRank, &axis)); in prepare()
143 shouldReduce[axis] = true; in prepare()
150 for (uint32_t axis = 0; axis < inputRank; ++axis) { in prepare() local
151 if (shouldReduce[axis]) { in prepare()
156 outputShape.dimensions.push_back(getSizeOfDimension(inputShape, axis)); in prepare()
/packages/modules/NeuralNetworks/runtime/test/specs/V1_3/
Dgather_quant8_signed.mod.py18 axis = 1 variable
22 model = Model().Operation("GATHER", input0, axis, indices).To(output0)
46 def test(input0, axis, indices, output0, input_data, output_data): argument
47 model = Model().Operation("GATHER", input0, axis, indices).To(output0)
61 axis=0,
72 axis=0,
82 axis=0,
91 axis=0,
100 axis=0,
113 axis=0,
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Dsplit_quant8_signed.mod.py18 axis = Int32Scalar("axis", 0) variable
24 model = Model().Operation("SPLIT", input0, axis, num_splits).To(
41 axis = Int32Scalar("axis", 0) variable
46 model = Model().Operation("SPLIT", input0, axis, num_splits).To(
62 axis = Int32Scalar("axis", 1) variable
68 model = Model().Operation("SPLIT", input0, axis, num_splits).To(
85 axis = Int32Scalar("axis", 1) variable
92 model = Model().Operation("SPLIT", input0, axis, num_splits).To(
Dargmin_quant8_signed.mod.py19 axis = Int32Scalar("axis", -1) variable
22 model = Model().Operation("ARGMIN", input0, axis).To(output0)
37 axis = Int32Scalar("axis", 0) variable
40 model = Model().Operation("ARGMIN", input0, axis).To(output0)
55 axis = Int32Scalar("axis", 1) variable
58 model = Model().Operation("ARGMIN", input0, axis).To(output0)
Dargmax_quant8_signed.mod.py18 axis = Int32Scalar("axis", 1) variable
21 model = Model().Operation("ARGMAX", input0, axis).To(output0)
36 axis = Int32Scalar("axis", 0) variable
39 model = Model().Operation("ARGMAX", input0, axis).To(output0)
56 axis = Int32Scalar("axis", -1) variable
59 model = Model().Operation("ARGMAX", input0, axis).To(output0)
Dsoftmax_quant8_signed.mod.py60 axis = Int32Scalar("axis", -1) # last axis variable
93 Model("axis").Operation("SOFTMAX", i, 1.0, axis).To(o)
94 Example(example1).AddVariations(quant8_signed, includeDefault=False).AddAllDimsAndAxis(i, o, axis)
96 Model("axis").Operation("SOFTMAX", i, 0.000001, axis).To(o)
97 Example(example2).AddVariations(quant8_signed, includeDefault=False).AddAllDimsAndAxis(i, o, axis)
Dmean_quant8_signed.mod.py18 axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 0, -3, -3]) variable
22 model = model.Operation("MEAN", i1, axis, keepDims).To(output)
42 axis = Parameter("axis", "TENSOR_INT32", "{2}", [0, 2]) variable
46 model = model.Operation("MEAN", i1, axis, keepDims).To(output)
/packages/modules/NeuralNetworks/runtime/test/specs/V1_2/
Dgather.mod.py17 def test(input0, axis, indices, output0, input_data, output_data): argument
18 model = Model().Operation("GATHER", input0, axis, indices).To(output0)
42 axis=0,
53 axis=0,
63 axis=0,
72 axis=0,
81 axis=0,
94 axis=0,
103 axis=1,
114 axis=-1,
Dl2_normalization_axis.mod.py20 axis = Int32Scalar("axis", -1) # last axis variable
47 Model().Operation("L2_NORMALIZATION", i1, axis).To(o1)
48 Example(example0).AddAllDimsAndAxis(i1, o1, axis).AddVariations("relaxed", "float16", quant8)
54 axis = Int32Scalar("axis", -1) # last axis variable
56 Model("corner_case").Operation("L2_NORMALIZATION", i2, axis).To(o2)
60 }).AddAllDimsAndAxis(i2, o2, axis)
Dlog_softmax.mod.py19 def test(input0, output0, input_data, beta, axis, output_data): argument
20 model = Model().Operation("LOG_SOFTMAX", input0, beta, axis).To(output0)
32 axis=4,
45 axis=-1,
58 axis=-3,
69 axis=4,
Dlocal_response_normalization_v1_2.mod.py19 axis = Int32Scalar("axis", -1) # last axis variable
23 Model("axis").Operation("LOCAL_RESPONSE_NORMALIZATION", i, 20, 9.0, 4.0, 0.5, axis).To(o)
26 })).AddAllDimsAndAxis(i, o, axis).AddVariations("relaxed", "float16")
29 Model("axis").Operation("LOCAL_RESPONSE_NORMALIZATION", i, 2, 9.0, 4.0, 0.5, axis).To(o)
32 })).AddAllDimsAndAxis(i, o, axis).AddVariations("relaxed", "float16")
Dsoftmax_v1_2.mod.py19 axis = Int32Scalar("axis", -1) # last axis variable
51 Model("axis").Operation("SOFTMAX", i, 1.0, axis).To(o)
52 Example(example1).AddVariations("relaxed", "float16", quant8).AddAllDimsAndAxis(i, o, axis)
54 Model("axis").Operation("SOFTMAX", i, 0.000001, axis).To(o)
55 Example(example2).AddVariations("relaxed", "float16", quant8).AddAllDimsAndAxis(i, o, axis)
/packages/modules/NeuralNetworks/common/include/
DOperations.h84 float bias, float alpha, float beta, int32_t axis,
87 float bias, float alpha, float beta, int32_t axis, float* outputData,
113 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis,
117 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape,
125 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t axis,
128 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis,
132 bool splitFloat32(const float* inputData, const Shape& inputShape, const int32_t axis,
136 bool splitInt32(const int32_t* inputData, const Shape& inputShape, const int32_t axis,
140 bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, const int32_t axis,
144 bool splitQuant8Signed(const int8_t* inputData, const Shape& inputShape, const int32_t axis,
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/packages/modules/NeuralNetworks/runtime/test/fuzzing/operation_signatures/
DConcatSplit.cpp31 int32_t axis = getUniform<int32_t>(0, rank - 1); in concatConstructor() local
32 op->inputs[numInputs]->setScalarValue<int32_t>(axis); in concatConstructor()
37 if (axis == static_cast<int32_t>(i)) { in concatConstructor()
123 int32_t axis = getRandomAxis(rank); in splitConstructor() local
124 op->inputs[1]->setScalarValue<int32_t>(axis); in splitConstructor()
125 axis = toPositiveAxis(axis, rank); in splitConstructor()
136 if (axis == static_cast<int32_t>(i)) { in splitConstructor()
/packages/modules/NeuralNetworks/tools/test_generator/
DREADME.md77 model.Operation("MEAN", i1, [1], 0) # axis = [1], keep_dims = 0
147 …axis in input/output to target position, and optionally remove some axis. The caller need to provi…
155 …axis, such as L2_NORMALIZATION, SOFTMAX, and CHANNEL_SHUFFLE. For example, consider L2_NORMALIZATI…
158 toAxis0 = AxisConverter(-1, 0, 4).Identify([input, output, axis])
161 The target axis can also be negative to test the negative indexing
164 toAxis0 = AxisConverter(-1, -4, 4).Identify([input, output, axis])
170 toDim2 = AxisConverter(-1, -1, 4, drop=[0, 1]).Identify([input, output, axis])
173 …nsposition first and then remove the axis. For example, the following converter will result in sha…
176 toDim2Axis0 = AxisConverter(-1, 2, 4, drop=[0, 1]).Identify([input, output, axis])
270 # original axis and dim are deduced from the op_list

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