package stream_executor.dnn

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Describes a kind of non-linearity (threshold-like mathematical function).

Used in: tensorflow.ConvolutionProto

• kNone = 0

• kSigmoid = 1

• kRelu = 2

  Rectified linear activation: f(x) = x < 0 ? 0 : x

• kRelu6 = 3

  Rectified linear activation; where upper maximum is 6.0.

• kReluX = 4

  Rectified linear activation; where upper maximum specified by BatchDescriptor::value_max().

• kTanh = 5

• kBandPass = 6

  Like ReluX; but passes all values in the range [-X,X].

Generic algorithm representation.

• int64 algo_id = 1

• AlgorithmProto.MathType math_type = 2

• string exec_plan_id = 3

  cuDNN v8 uses a string to uniquely represent the backend plan.

Used in: AlgorithmProto

• DEFAULT_MATH = 0

• TENSOR_OP_MATH = 1

  The GPU may operate 4x4 matrix FMA. See cuDNN's documentation for CUDNN_TENSOR_OP_MATH.

Convolution-specific parameters.

Used in: tensorflow.ConvolutionProto

• repeated int64 paddings = 1

• repeated int64 strides = 2

• repeated int64 dilations = 3

• DataType compute_mode = 4

  The "accumulator" type. For example, use F32 as an accumulator for F16 convolutions. See cuDNN's cudnnConvolutionMode_t.

• int32 group_count = 5

  See cuDNN's group count.

• ConvolutionMode convolution_mode = 6

• string name = 7

  Tensorflow node name, same as in NodeDef, for debugging purposes.

Used in: tensorflow.ConvolutionProto

• INVALID = 0

• FORWARD = 1

• BACKWARD_FILTER = 2

• BACKWARD_DATA = 3

• FORWARD_BIAS_ACTIVATION = 4

Describe the math definition for the conv op. The popular behavior is actually called cross-correlation in math, despite the operation is often referred as convolution. See cuDNN cudnnConvolutionMode_t.

Used in: ConvolutionDescriptorProto

• CROSS_CORRELATION = 0

• CONVOLUTION = 1

Describes how a convolution input or output layer's data is formatted.

Used in: TensorDescriptorProto

• kYXDepthBatch = 0

  Naming convention: Y <-> row or height X <-> column or width Batch <-> batch, or N Depth <-> feature, or channel TODO(timshen): turn them into cuDNN names, e.g. kNCHW. Note: In cudnn, kBatchDepthYX4 and kBatchDepthYX32 are the same layout (namely, NCHW_VECT_C). It differentiates between these two by using a different data type (int8x4 vs int8x32). In StreamExecutor we use different layouts for these, because we don't usually pass an explicit data type to StreamExecutor functions.

• kYXBatchDepth = 1

• kBatchYXDepth = 2

  cuDNN's NHWC layout

• kBatchDepthYX = 3

  cuDNN's NCHW layout

• kBatchDepthYX4 = 4

  cuDNN's NCHW_VECT_C with 4-elem vectors (e.g. int8x4)

• kBatchDepthYX32 = 5

  cuDNN's NCHW_VECT_C with 32-elem vects (e.g. int8x32)

Specifies the data type used by an operation.

Used in: ConvolutionDescriptorProto, TensorDescriptorProto

• kFloat = 0

• kDouble = 1

• kHalf = 2

• kInt8 = 3

• kInt32 = 4

• kComplexFloat = 5

• kComplexDouble = 6

Describes how a convolution filter is laid out in the memory.

Used in: TensorDescriptorProto

• kOutputInputYX = 0

  Naming convention: Y <-> row or height X <-> column or width Output <-> output feature, or N Input <-> input feature, or N TODO(timshen): turn them into cuDNN names, e.g. kNCHW.

  cuDNN's NCHW layout

• kOutputYXInput = 1

  cuDNN's NHWC layout

• kOutputInputYX4 = 2

  cuDNN's NCHW_VECT_C layout with 4-elem vectors

• kOutputInputYX32 = 5

  cuDNN's NCHW_VECT_C layout with 32-elem vectors

• kInputYXOutput = 3

• kYXInputOutput = 4

Generic tensor representation.

Used in: tensorflow.ConvolutionProto

• repeated int64 dimensions = 1

• DataType data_type = 2

• oneof layout_oneof

  • DataLayout data_layout = 3

  • FilterLayout filter_layout = 4