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/external/tensorflow/tensorflow/lite/kernels/internal/reference/
Dlegacy_reference_ops.h7 http://www.apache.org/licenses/LICENSE-2.0
37 static constexpr int kDepthwiseReverseShift = -1;
40 shape->BuildFrom( in ShapeFromDims()
44 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv() argument
66 DepthwiseConv(op_params, DimsToShape(input_dims), input_data, in DepthwiseConv()
71 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv() argument
79 DepthwiseConv(input_data, input_dims, filter_data, filter_dims, bias_data, in DepthwiseConv()
85 // Legacy, for compatibility with old checked-in code.
87 void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv() argument
95 DepthwiseConv(input_data, input_dims, filter_data, filter_dims, bias_data, in DepthwiseConv()
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Dreduce.h7 http://www.apache.org/licenses/LICENSE-2.0
55 inline bool Reduce(const In* input_data, const int* input_dims, in Reduce() argument
67 ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); in Reduce()
68 size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, in Reduce()
72 } while (NextIndex(input_num_dims, input_dims, input_iter)); in Reduce()
80 inline bool Reduce(const In* input_data, const int* input_dims, in Reduce() argument
94 ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); in Reduce()
95 size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, in Reduce()
103 } while (NextIndex(input_num_dims, input_dims, input_iter)); in Reduce()
113 // Short-circuit axis resolution for scalars; the axis will go unused. in ResolveAxis()
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/external/tensorflow/tensorflow/core/kernels/
Deigen_benchmark.h7 http://www.apache.org/licenses/LICENSE-2.0
46 void SpatialConvolution(Dimensions input_dims, Dimensions filter_dims) { in SpatialConvolution() argument
47 Dimensions output_dims(input_dims[0], // batch in SpatialConvolution()
48 input_dims[1], // input_height in SpatialConvolution()
49 input_dims[2], // input_width in SpatialConvolution()
53 static_cast<Scalar*>(device_.allocate(BufferSize(input_dims))); in SpatialConvolution()
59 device_.memset(input_data, 123, BufferSize(input_dims)); in SpatialConvolution()
62 Input input(input_data, input_dims); in SpatialConvolution()
76 void SpatialConvolutionBackwardInput(Dimensions input_dims, in SpatialConvolutionBackwardInput() argument
81 Dimensions output_dims(input_dims[0], // batch in SpatialConvolutionBackwardInput()
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Dconv_2d_gpu.h7 http://www.apache.org/licenses/LICENSE-2.0
61 c_conj.y = -c.y;
75 c_conj.y = -c.y;
127 // A dimension type with compile-time known size.
141 // An index type with compile-time known size.
168 for (int i = IndexCount - 1; i >= 0; i--) {
170 tensor_index[i] = index - dims[i] * new_index;
187 Dimension<3> input_dims,
190 output_dims[sp0] = input_dims[0];
191 output_dims[sp1] = input_dims[1];
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Dreduce_join_op.cc7 http://www.apache.org/licenses/LICENSE-2.0
37 for (int32_t i = shape.dims() - 1; i >= 0; --i) { in GetStrides()
47 // nonspecified dimensions set to 0. Dimensions must be ordered from outer-most
48 // to inner-most with respect to the subset linear index.
55 for (int32_t i = dim_list.size() - 1; i >= 0; --i) { in LinearSubIndexToFullIndex()
78 int32_t input_dims) { in GetReducedIndices() argument
84 reduced_indices[i] = reduction_indices_flat(reduction_dims - i - 1); in GetReducedIndices()
85 reduced_indices[i] += reduced_indices[i] < 0 ? input_dims : 0; in GetReducedIndices()
93 int32_t input_dims, in MakeUnreducedIndices() argument
95 for (int32_t index = 0; index < input_dims; ++index) { in MakeUnreducedIndices()
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Dsparse_slice_op.cc7 http://www.apache.org/licenses/LICENSE-2.0
40 const int input_dims = input_shape.NumElements(); in operator ()() local
52 input_dims); in operator ()()
54 input_dims); in operator ()()
61 context->set_output(0, output.indices()); in operator ()()
62 context->set_output(1, output.values()); in operator ()()
74 context->allocate_output(2, allocated_shape, &shape)); in operator ()()
76 shape->vec<int64_t>()(dim) = output_shape.dim_size(dim); in operator ()()
94 const Tensor& input_indices = context->input(0); in SparseSliceOpImpl()
95 const Tensor& input_values = context->input(1); in SparseSliceOpImpl()
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/external/pytorch/torch/distributed/tensor/_ops/
D_einsum_strategy.py24 def parse_equation(cls, equation: str) -> Tuple[List[str], str]:
29 inputs, outputs = equation.split("->")
30 input_dims, output_dims = inputs.split(","), outputs.split(",")
34 assert len(input_dims) <= 2, "Only support at most two inputs"
37 return input_dims, output_dim
40 def parse_dims(cls, input_dims: List[str], output_dim: str) -> "EinsumDims":
46 for input_dim in input_dims:
61 for input_dim in input_dims:
68 len(input_dims) == 2
70 lhs, rhs = input_dims
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/external/XNNPACK/test/
Dconstant-pad-operator-tester.h3 // This source code is licensed under the BSD-style license found in the
96 this->iterations_ = iterations; in iterations()
101 return this->iterations_; in iterations()
114 std::array<size_t, XNN_MAX_TENSOR_DIMS> input_dims; in TestX8() local
118 std::fill(input_dims.begin(), input_dims.end(), 1); in TestX8()
123 input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i); in TestX8()
124 input_pre_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = pre_padding(i); in TestX8()
125 input_post_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = post_padding(i); in TestX8()
126 output_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = output_dim(i); in TestX8()
133 for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) { in TestX8()
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Ddepth-to-space.cc3 // This source code is licensed under the BSD-style license found in the
17 #include <xnnpack/node-type.h>
37 input_dims = RandomShape(4); in DepthToSpaceTest()
40 …output_dims = {input_dims[0], input_dims[1] * block_size, input_dims[2] * block_size, output_chann… in DepthToSpaceTest()
41 input_dims[3] = block_size * block_size * output_channels; in DepthToSpaceTest()
44 input = std::vector<T>(NumElements(input_dims) + XNN_EXTRA_BYTES / sizeof(T)); in DepthToSpaceTest()
63 assert(input_dims[0] == output_dims[0]); in batch_size()
64 return input_dims[0]; in batch_size()
67 size_t input_height() { return input_dims[1]; } in input_height()
68 size_t input_width() { return input_dims[2]; } in input_width()
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Dprelu.cc3 // This source code is licensed under the BSD-style license found in the
16 #include <xnnpack/node-type.h>
29 input_dims = RandomShape(4); in SetUp()
30 output_dims = input_dims; in SetUp()
31 batch_size = input_dims[0] * input_dims[1] * input_dims[2]; in SetUp()
32 channels = input_dims[3]; in SetUp()
34 input = std::vector<float>(XNN_EXTRA_BYTES / sizeof(float) + NumElements(input_dims)); in SetUp()
57 std::vector<size_t> input_dims; member in PreluTestF32
78 … subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, 0, in TEST_F()
92 … subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, 2, in TEST_F()
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Dglobal-average-pooling-1d.cc3 // This source code is licensed under the BSD-style license found in the
16 #include <xnnpack/node-type.h>
38 input_dims = RandomShape(); in GlobalAveragePooling1DTest()
39 output_dims = input_dims; in GlobalAveragePooling1DTest()
40 output_dims[output_dims.size() - 2] = 1; in GlobalAveragePooling1DTest()
43 for (size_t i = 0; i < input_dims.size() - 2; i++) { in GlobalAveragePooling1DTest()
44 batch_size *= input_dims[i]; in GlobalAveragePooling1DTest()
46 input_width = input_dims[input_dims.size() - 2]; in GlobalAveragePooling1DTest()
47 channels = input_dims[input_dims.size() - 1]; in GlobalAveragePooling1DTest()
49 input = std::vector<T>(XNN_EXTRA_BYTES / sizeof(T) + NumElements(input_dims)); in GlobalAveragePooling1DTest()
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Dglobal-average-pooling-2d.cc3 // This source code is licensed under the BSD-style license found in the
16 #include <xnnpack/node-type.h>
38 input_dims = RandomShape(4); in GlobalAveragePooling2DTest()
39 output_dims = {input_dims[0], 1, 1, input_dims[3]}; in GlobalAveragePooling2DTest()
40 input = std::vector<T>(XNN_EXTRA_BYTES / sizeof(T) + NumElements(input_dims)); in GlobalAveragePooling2DTest()
43 batch_size = input_dims[0]; in GlobalAveragePooling2DTest()
44 input_width = input_dims[1] * input_dims[2]; in GlobalAveragePooling2DTest()
45 channels = input_dims[3]; in GlobalAveragePooling2DTest()
69 float output_min = -std::numeric_limits<float>::infinity();
75 std::vector<size_t> input_dims; member in GlobalAveragePooling2DTest
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Deven-split2.cc3 // This source code is licensed under the BSD-style license found in the
16 #include <xnnpack/node-type.h>
40 input_dims = output1_dims; in EvenSplit2Test()
41 input_dims[axis] = output1_dims[axis] + output2_dims[axis]; in EvenSplit2Test()
43 input = std::vector<T>(NumElements(input_dims)); in EvenSplit2Test()
56 batch_size *= input_dims[i]; in EvenSplit2Test()
59 for (size_t i = axis; i < input_dims.size(); i++) { in EvenSplit2Test()
60 input_stride *= input_dims[i]; in EvenSplit2Test()
74 return std::uniform_int_distribution<size_t>(0, dims.size() - 1)(rng); in RandomAxis()
97 std::vector<size_t> input_dims; member in EvenSplit2Test
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/external/tensorflow/tensorflow/lite/kernels/internal/optimized/
Dreduce.h7 http://www.apache.org/licenses/LICENSE-2.0
70 for (; out_d <= end_depth - 16; out_d += 16) { in MeanImpl()
223 temp = temp > 0 ? temp + 0.5f : temp - 0.5f; in Mean()
224 int32_t bias = output_zero_point - static_cast<int32_t>(temp); in Mean()
234 std::min(thread_count, cpu_backend_context->max_num_threads()); in Mean()
250 (output_depth - depth_start) / (capped_thread_count - i); in Mean()
312 void ReduceIsCopy(const T* input_data, const int* input_dims, in ReduceIsCopy() argument
314 int num_elems = NumElements(input_dims, input_num_dims); in ReduceIsCopy()
328 const int* input_dims, U* output_data, in ReduceImpl() argument
338 for (int i = 0; i < input_dims[0]; ++i) { in ReduceImpl()
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Dlegacy_optimized_ops.h7 http://www.apache.org/licenses/LICENSE-2.0
77 static constexpr int kDepthwiseReverseShift = -1;
99 const int cols = dims.sizes[N - 1]; in MapAsMatrixWithLastDimAsCols()
101 for (int d = 0; d < N - 1; d++) { in MapAsMatrixWithLastDimAsCols()
119 // as we have the --variable_batch hack.
139 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv() argument
164 DepthwiseConvImpl(op_params, DimsToShape(input_dims), input_data, in DepthwiseConv()
171 inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, in DepthwiseConv() argument
179 DepthwiseConv(input_data, input_dims, filter_data, filter_dims, bias_data, in DepthwiseConv()
185 // legacy, for compatibility with old checked-in code
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/external/libtextclassifier/native/utils/tflite/
Dblacklist_base.cc8 * http://www.apache.org/licenses/LICENSE-2.0
37 auto* op = reinterpret_cast<BlacklistOpBase*>(node->user_data); in Resize()
39 TfLiteIntArray* input_dims = op->GetInputShape(context, node); in Resize() local
40 TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_dims->size + 1); in Resize()
41 for (int i = 0; i < input_dims->size; i++) { in Resize()
42 output_dims->data[i] = input_dims->data[i]; in Resize()
44 output_dims->data[input_dims->size] = op->categories(); in Resize()
45 return context->ResizeTensor( in Resize()
46 context, &context->tensors[node->outputs->data[kOutputCategories]], in Resize()
51 auto* op = reinterpret_cast<BlacklistOpBase*>(node->user_data); in Eval()
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/external/tensorflow/tensorflow/security/advisory/
Dtfsa-2021-160.md1 ## TFSA-2021-160: Heap OOB in TFLite
4 CVE-2021-37685
8 …rflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50)
14 axis = input_dims.size + 1 + axis;
16 TF_LITE_ENSURE(context, axis <= input_dims.size);
18 TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_dims.size + 1);
19 for (int i = 0; i < output_dims->size; ++i) {
21 output_dims->data[i] = input_dims.data[i];
23 output_dims->data[i] = 1;
25 output_dims->data[i] = input_dims.data[i - 1];
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/external/executorch/backends/qualcomm/builders/
Dop_expand.py4 # This source code is licensed under the BSD-style license found in the
22 def __init__(self, *args) -> None:
29 ) -> PyQnnWrapper.PyQnnOpWrapper:
52 input_dims = len(input_tensor.size())
55 if input_dims < output_dims:
57 …f"[QNN Delegate Op Builder]: The rank of input tensor: {input_dims} is less than the rank of outpu…
62 multiples = [1] * input_dims
63 multiples_shape = [input_dims]
64 for i in range(input_dims):
65 if sizes[i] != -1 and shape[i] == 1:
/external/tensorflow/tensorflow/lite/kernels/
Dmirror_pad.cc7 http://www.apache.org/licenses/LICENSE-2.0
42 const int kUnsetOffset = -1;
48 const TfLiteIntArray* input_dims = nullptr; member
70 switch (padding_matrix->type) { in GetPadding()
72 GetPadding(padding_matrix->data.i32, dimension, left_pad, right_pad); in GetPadding()
75 GetPadding(padding_matrix->data.i64, dimension, left_pad, right_pad); in GetPadding()
85 const int input_dims = NumDimensions(input); in GetPaddedOutputShape() local
87 TfLiteIntArrayCreate(input_dims), TfLiteIntArrayFree); in GetPaddedOutputShape()
90 for (int i = 0; i < input_dims; ++i) { in GetPaddedOutputShape()
92 shape->data[i] = SizeOfDimension(input, i) + left_pad + right_pad; in GetPaddedOutputShape()
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Ddynamic_update_slice.cc7 http://www.apache.org/licenses/LICENSE-2.0
63 // avoid generating out-of-bounds update indices. in Prepare()
72 TF_LITE_ENSURE_TYPES_EQ(context, operand->type, update->type); in Prepare()
73 TF_LITE_ENSURE_TYPES_EQ(context, start_indices->type, kTfLiteInt32); in Prepare()
75 output->type = operand->type; in Prepare()
76 TfLiteIntArray* output_size = TfLiteIntArrayCopy(operand->dims); in Prepare()
77 return context->ResizeTensor(context, output, output_size); in Prepare()
95 std::vector<int> ClampStartIndices(int input_dims, const int32_t* indices_data, in ClampStartIndices() argument
98 std::vector<int> clamped_start_indices(input_dims, 0); in ClampStartIndices()
99 for (int i = 0; i < input_dims; i++) { in ClampStartIndices()
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/external/tensorflow/tensorflow/lite/delegates/gpu/common/
Dquantization_util_test.cc7 http://www.apache.org/licenses/LICENSE-2.0
44 std::copy(data.begin(), data.end(), result->data); in BuildTfLiteIntArray()
71 return (max - min) / ((std::numeric_limits<T>::max() * 1.0) - in ScaleFromMinMax()
80 static_cast<int>(-min / ScaleFromMinMax<T>(min, max) + 0.5f); in ZeroPointFromMinMax()
149 auto input_dims = BuildTfLiteIntArray({1, 3, 2, 1}); in TEST() local
150 std::vector<int8_t> data = {-3, -2, -1, 1, 2, 3}; in TEST()
154 data.data(), input_dims.get(), "input", in TEST()
155 /*min=*/-12.8f, /*max=*/12.7f, /*is_variable=*/false); in TEST()
157 dequantized_data.data(), input_dims.get(), "input_dequant", in TEST()
169 Pointwise(FloatNear(1e-6), {-0.3, -0.2, -0.1, 0.1, 0.2, 0.3})); in TEST()
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/
Droll_op.cc7 http://www.apache.org/licenses/LICENSE-2.0
29 const TensorShape input_shape = ctx->InputShape(0); in Compile()
30 xla::XlaOp shift = ctx->Input(1); in Compile()
31 const TensorShape shift_shape = ctx->InputShape(1); in Compile()
32 const TensorShape axis_shape = ctx->InputShape(2); in Compile()
34 int64_t input_dims = input_shape.dims(); in Compile() local
35 OP_REQUIRES(ctx, input_dims >= 1, in Compile()
36 errors::InvalidArgument("input must be 1-D or higher")); in Compile()
39 "shift must be a scalar or a 1-D vector. Found: ", in Compile()
43 "axis must be a scalar or a 1-D vector. Found: ", in Compile()
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Dslice_op.cc7 http://www.apache.org/licenses/LICENSE-2.0
16 // XLA-specific Slice Op.
44 const TensorShape input_shape = ctx->InputShape(0); in Compile()
45 const TensorShape begin_tensor_shape = ctx->InputShape(1); in Compile()
46 const TensorShape size_tensor_shape = ctx->InputShape(2); in Compile()
48 const int input_dims = input_shape.dims(); in Compile() local
53 begin_tensor_shape.num_elements() == input_dims && in Compile()
54 size_tensor_shape.num_elements() == input_dims, in Compile()
56 "Expected begin and size arguments to be 1-D tensors of size ", in Compile()
57 input_dims, ", but got shapes ", begin_tensor_shape.DebugString(), in Compile()
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/external/pytorch/test/distributed/_tensor/
Dtest_op_strategy.py26 equation = "abc,abc->abc"
27 input_dims, output_dim = EinsumDims.parse_equation(equation)
28 edims = EinsumDims.parse_dims(input_dims, output_dim)
36 equation = "mk,kn->mn"
37 input_dims, output_dim = EinsumDims.parse_equation(equation)
38 edims = EinsumDims.parse_dims(input_dims, output_dim)
46 equation = "bmk,bkn->bmn"
47 input_dims, output_dim = EinsumDims.parse_equation(equation)
48 edims = EinsumDims.parse_dims(input_dims, output_dim)
55 equation = "bcmk,bckn->bcmn"
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/external/tensorflow/tensorflow/compiler/xla/tests/
Dconvolution_test.cc7 http://www.apache.org/licenses/LICENSE-2.0
45 ErrorSpec error_spec_ = ErrorSpec(1e-2, 1e-3);
47 ErrorSpec error_spec_ = ErrorSpec(1e-4, 1e-3);
75 alhs->FillWithMultiples(static_cast<T>(static_cast<T>(1.0f))); in RunTest()
76 ASSERT_EQ(3, alhs->width()); in RunTest()
77 ASSERT_EQ(3, alhs->height()); in RunTest()
86 arhs->FillWithYX(rhs_raster); in RunTest()
87 ASSERT_EQ(2, arhs->width()); in RunTest()
88 ASSERT_EQ(2, arhs->height()); in RunTest()
108 this->RunTest(); in XLA_TYPED_TEST()
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