/external/eigen/bench/tensors/ |
D | tensor_benchmarks_cpu.cc | 146 BM_FuncWithKernelDimsCPU(convolution, 7, 1, 4); 147 BM_FuncWithKernelDimsCPU(convolution, 7, 1, 8); 148 BM_FuncWithKernelDimsCPU(convolution, 7, 1, 12); 150 BM_FuncWithKernelDimsCPU(convolution, 1, 7, 4); 151 BM_FuncWithKernelDimsCPU(convolution, 1, 7, 8); 152 BM_FuncWithKernelDimsCPU(convolution, 1, 7, 12); 154 BM_FuncWithKernelDimsCPU(convolution, 7, 4, 4); 155 BM_FuncWithKernelDimsCPU(convolution, 7, 4, 8); 156 BM_FuncWithKernelDimsCPU(convolution, 7, 4, 12); 158 BM_FuncWithKernelDimsCPU(convolution, 4, 7, 4); [all …]
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D | tensor_benchmarks_gpu.cu | 70 BM_FuncWithKernelDimsGPU(convolution, 7, 1); 71 BM_FuncWithKernelDimsGPU(convolution, 1, 7); 72 BM_FuncWithKernelDimsGPU(convolution, 7, 4); 73 BM_FuncWithKernelDimsGPU(convolution, 4, 7); 74 BM_FuncWithKernelDimsGPU(convolution, 7, 64); 75 BM_FuncWithKernelDimsGPU(convolution, 64, 7);
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/external/tensorflow/tensorflow/compiler/xla/service/ |
D | transpose_folding.cc | 54 const HloInstruction& convolution, in CanFoldOperandsIntoConvolution() argument 57 if (HloOpcode::kConvolution != convolution.opcode()) { in CanFoldOperandsIntoConvolution() 62 for (int64 i = 0; i < convolution.operand_count(); ++i) { in CanFoldOperandsIntoConvolution() 63 auto& operand = *convolution.operand(i); in CanFoldOperandsIntoConvolution() 69 return transposable_conv_operands(convolution, operand_set); in CanFoldOperandsIntoConvolution() 101 auto& convolution = *pair.first; in FoldTransposeIntoConvolution() local 109 convolution.convolution_dimension_numbers(); in FoldTransposeIntoConvolution() 116 HloInstruction& transpose = *convolution.mutable_operand(kLhsIdx); in FoldTransposeIntoConvolution() 133 new_lhs = convolution.mutable_operand(kLhsIdx); in FoldTransposeIntoConvolution() 140 HloInstruction& transpose = *convolution.mutable_operand(kRhsIdx); in FoldTransposeIntoConvolution() [all …]
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D | hlo_cost_analysis.cc | 381 Status HloCostAnalysis::HandleConvolution(const HloInstruction* convolution) { in HandleConvolution() argument 382 auto rhs_instruction = convolution->operand(1); in HandleConvolution() 383 const auto& dnums = convolution->convolution_dimension_numbers(); in HandleConvolution() 385 convolution->shape().dimensions(dnums.output_feature_dimension()); in HandleConvolution() 393 const int64 output_elements = ShapeUtil::ElementsIn(convolution->shape()); in HandleConvolution()
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D | hlo_verifier.cc | 75 Status ShapeVerifier::HandleConvolution(HloInstruction* convolution) { in HandleConvolution() argument 79 convolution->operand(0)->shape(), convolution->operand(1)->shape(), in HandleConvolution() 80 convolution->window(), convolution->convolution_dimension_numbers())); in HandleConvolution() 81 return CheckShape(convolution, expected); in HandleConvolution()
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D | dfs_hlo_visitor_with_default.h | 85 Status HandleConvolution(HloInstructionPtr convolution) override { in HandleConvolution() argument 86 return DefaultAction(convolution); in HandleConvolution()
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D | algebraic_simplifier.cc | 141 Status HandleConvolution(HloInstruction* convolution) override; 1852 HloInstruction* convolution) { in HandleConvolution() argument 1853 auto lhs = convolution->mutable_operand(0); in HandleConvolution() 1854 auto rhs = convolution->mutable_operand(1); in HandleConvolution() 1858 convolution, in HandleConvolution() 1860 convolution->shape(), in HandleConvolution() 1862 ShapeUtil::MakeShape(convolution->shape().element_type(), {}), in HandleConvolution() 1867 const auto& window = convolution->window(); in HandleConvolution() 1885 convolution->convolution_dimension_numbers(); in HandleConvolution() 1888 const Shape& convolution_shape = convolution->shape(); in HandleConvolution() [all …]
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/external/tensorflow/tensorflow/compiler/xla/service/cpu/ |
D | ir_emission_utils.cc | 28 const HloInstruction& convolution) { in PotentiallyImplementedAsEigenConvolution() argument 36 const Shape& input_shape = convolution.operand(0)->shape(); in PotentiallyImplementedAsEigenConvolution() 37 const Shape& kernel_shape = convolution.operand(0)->shape(); in PotentiallyImplementedAsEigenConvolution() 47 if (window_util::HasWindowReversal(convolution.window())) { in PotentiallyImplementedAsEigenConvolution() 52 convolution.convolution_dimension_numbers(); in PotentiallyImplementedAsEigenConvolution() 72 const Shape& output_shape = convolution.shape(); in PotentiallyImplementedAsEigenConvolution()
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D | cpu_layout_assignment.cc | 104 const HloInstruction* convolution = instruction; in AddBackendConstraints() local 105 const HloInstruction* lhs_instruction = convolution->operand(0); in AddBackendConstraints() 106 const HloInstruction* rhs_instruction = convolution->operand(1); in AddBackendConstraints() 113 Shape output_shape(RowMajorShape(convolution->shape())); in AddBackendConstraints() 119 constraints->SetOperandLayout(input_shape, convolution, 0)); in AddBackendConstraints() 121 constraints->SetOperandLayout(filter_shape, convolution, 1)); in AddBackendConstraints() 123 constraints->SetInstructionLayout(output_shape, convolution)); in AddBackendConstraints()
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D | ir_emission_utils.h | 26 const HloInstruction& convolution);
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D | ir_emitter.cc | 846 Status IrEmitter::HandleConvolution(HloInstruction* convolution) { in HandleConvolution() argument 847 auto lhs = convolution->operand(0); in HandleConvolution() 848 auto rhs = convolution->operand(1); in HandleConvolution() 849 const auto& window = convolution->window(); in HandleConvolution() 851 /*instruction=*/*convolution, /*operands=*/{lhs, rhs}, in HandleConvolution() 855 convolution->convolution_dimension_numbers(); in HandleConvolution() 857 if (PotentiallyImplementedAsEigenConvolution(*convolution)) { in HandleConvolution() 860 const Shape& convolution_shape = convolution->shape(); in HandleConvolution() 872 TF_RETURN_IF_ERROR(EmitTargetAddressForOp(convolution)); in HandleConvolution() 875 convolution->convolution_dimension_numbers(); in HandleConvolution() [all …]
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/external/tensorflow/tensorflow/compiler/xla/tests/ |
D | isolated_convolution.hlo | 1 HloModule convolution.167: 3 ENTRY %convolution.167 (parameter.0: f32[16,28,28,128], parameter.1: f32[3,3,128,128]) -> f32[16,28… 6 …ROOT %convolution.167 = f32[16,28,28,128]{3,0,2,1} convolution(f32[16,28,28,128]{3,0,2,1} %paramet…
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/external/tensorflow/tensorflow/python/kernel_tests/ |
D | atrous_convolution_test.py | 104 y1 = nn_ops.convolution( 106 y2 = nn_ops.convolution(input=x, filter=filters_upsampled, **kwargs) 116 y = nn_ops.convolution( 123 y = nn_ops.convolution( 221 result = nn_ops.convolution( 223 result = nn_ops.convolution( 231 y1 = nn_ops.convolution( 236 y1 = nn_ops.convolution( 257 output = nn_ops.convolution(
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_Conv2DBackpropInput.pbtxt | 21 Gradients w.r.t. the output of the convolution. 28 w.r.t. the input of the convolution. 35 of the convolution. Must be in the same order as the dimension specified with 65 summary: "Computes the gradients of convolution with respect to the input."
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D | api_def_Conv2DBackpropFilter.pbtxt | 21 Gradients w.r.t. the output of the convolution. 29 the `filter` input of the convolution. 36 of the convolution. Must be in the same order as the dimension specified with 66 summary: "Computes the gradients of convolution with respect to the filter."
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D | api_def_DepthwiseConv2dNativeBackpropFilter.pbtxt | 25 Gradients w.r.t. the output of the convolution. 33 the `filter` input of the convolution. 40 of the convolution. 69 summary: "Computes the gradients of depthwise convolution with respect to the filter."
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D | api_def_DepthwiseConv2dNativeBackpropInput.pbtxt | 24 Gradients w.r.t. the output of the convolution. 33 convolution. 40 of the convolution. 69 summary: "Computes the gradients of depthwise convolution with respect to the input."
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D | api_def_SpaceToBatch.pbtxt | 95 Among others, this operation is useful for reducing atrous convolution into 96 regular convolution.
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D | api_def_FusedResizeAndPadConv2D.pbtxt | 50 summary: "Performs a resize and padding as a preprocess during a convolution." 53 the packing stage of a convolution, so this op allows for an optimized
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D | api_def_SpaceToBatchND.pbtxt | 125 Among others, this operation is useful for reducing atrous convolution into 126 regular convolution.
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D | api_def_Conv3DBackpropInput.pbtxt | 36 summary: "Computes the gradients of 3-D convolution with respect to the input."
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/external/python/cpython3/Modules/_decimal/libmpdec/literature/ |
D | bignum.txt | 6 Bignum arithmetic in libmpdec uses the scheme for fast convolution 13 The transform in a finite field can be used for convolution in the same 75 convolute.c -> do the actual fast convolution, using one of
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/external/tensorflow/tensorflow/docs_src/api_guides/python/ |
D | nn.md | 33 The convolution ops sweep a 2-D filter over a batch of images, applying the 41 Note that although these ops are called "convolution", they are strictly 54 convolution ops depend on the padding scheme chosen: `'SAME'` or `'VALID'`. 115 * @{tf.nn.convolution} 162 is the max-sum counterpart of standard sum-product convolution: 177 is the min-sum counterpart of standard sum-product convolution: 191 convolution. Please refer to the `Convolution` section for details. 311 scheme for convolution operations. 321 output size after a `'VALID'` convolution is \\(n_o\\). In other words, we need to 405 Putting all together, the total padding used by tensorflow's convolution with
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/external/tensorflow/tensorflow/compiler/xla/tools/parser/ |
D | hlo_parser_test.cc | 353 …ROOT %convolution = f32[1,2,1]{2,0,1} convolution(f32[1,2,1]{2,0,1} %copy, f32[1,1,1]{2,1,0} %filt… 366 …ROOT %convolution = f32[1,2]{0,1} convolution(f32[1,2]{1,0} %input, f32[1,1]{1,0} %filter), dim_la… 379 …ROOT %convolution-base-dilated = f32[128,14,14,512]{0,3,2,1} convolution(f32[128,7,7,512]{0,3,2,1}…
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/external/tensorflow/tensorflow/docs_src/tutorials/ |
D | pdes.md | 51 """Transform a 2D array into a convolution kernel""" 57 """A simplified 2D convolution operation"""
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