// LINT: LEGACY_NAMES
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syntax = "proto3";
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package stream_executor.dnn;
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// Specifies the data type used by an operation.
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enum DataType {
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kFloat = 0;
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kDouble = 1;
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kHalf = 2;
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kInt8 = 3;
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kInt32 = 4;
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}
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// Describes how a convolution input or output layer's data is formatted.
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enum DataLayout {
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// Naming convention:
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// Y <-> row or height
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// X <-> column or width
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// Batch <-> batch, or N
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// Depth <-> feature, or channel
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// TODO(timshen): turn them into cuDNN names, e.g. kNCHW.
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kYXDepthBatch = 0;
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kYXBatchDepth = 1;
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kBatchYXDepth = 2; // cuDNN's NHWC layout
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kBatchDepthYX = 3; // cuDNN's NCHW layout
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kBatchDepthYX4 = 4; // cuDNN's NCHW_VECT_C layout
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}
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// Describes how a convolution filter is laid out in the memory.
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enum FilterLayout {
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// Naming convention:
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// Y <-> row or height
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// X <-> column or width
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// Output <-> output feature, or N
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// Input <-> input feature, or N
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// TODO(timshen): turn them into cuDNN names, e.g. kNCHW.
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kOutputInputYX = 0; // cuDNN's NCHW layout
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kOutputYXInput = 1; // cuDNN's NHWC layout
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kOutputInputYX4 = 2; // cuDNN's NCHW_VECT_C layout
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kInputYXOutput = 3;
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kYXInputOutput = 4;
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}
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// Describes a kind of non-linearity (threshold-like mathematical function).
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enum ActivationMode {
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kNone = 0;
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kSigmoid = 1;
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// Rectified linear activation: f(x) = x < 0 ? 0 : x
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kRelu = 2;
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// Rectified linear activation; where upper maximum is 6.0.
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kRelu6 = 3;
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// Rectified linear activation; where upper maximum specified by
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// BatchDescriptor::value_max().
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kReluX = 4;
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kTanh = 5;
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// Like ReluX; but passes all values in the range [-X,X].
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kBandPass = 6;
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}
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// Describe the math definition for the conv op. The popular behavior is
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// actually called cross-correlation in math, despite the operation is often
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// referred as convolution. See cuDNN cudnnConvolutionMode_t.
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enum ConvolutionMode {
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CROSS_CORRELATION = 0;
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CONVOLUTION = 1;
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}
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enum ConvolutionKind {
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INVALID = 0;
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FORWARD = 1;
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BACKWARD_FILTER = 2;
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BACKWARD_DATA = 3;
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}
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// Generic tensor representation.
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message TensorDescriptorProto {
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repeated int64 dimensions = 1;
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DataType data_type = 2;
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oneof layout_oneof {
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DataLayout data_layout = 3;
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FilterLayout filter_layout = 4;
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}
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}
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// Generic algorithm representation.
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message AlgorithmProto {
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enum MathType {
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DEFAULT_MATH = 0;
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// The GPU may operate 4x4 matrix FMA.
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// See cuDNN's documentation for CUDNN_TENSOR_OP_MATH.
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TENSOR_OP_MATH = 1;
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}
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int64 algo_id = 1;
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MathType math_type = 2;
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}
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// Convolution-specific parameters.
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message ConvolutionDescriptorProto {
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repeated int64 paddings = 1;
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repeated int64 strides = 2;
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repeated int64 dilations = 3;
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// The "accumulator" type. For example, use F32 as an accumulator for F16
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// convolutions.
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// See cuDNN's cudnnConvolutionMode_t.
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DataType compute_mode = 4;
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// See cuDNN's group count.
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int32 group_count = 5;
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ConvolutionMode convolution_mode = 6;
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}
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