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//! Provides the INn Plugin trait for Coaster implementation.
use crate::co::tensor::SharedTensor;
use std::fmt::Formatter;
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Different algorithms to compute the convolution forward algorithm.
pub enum ConvForwardAlgo {
/// Attempt to automatically find the best algorithm of all the other available ones.
Auto,
/// Compute the convolution as explicit matrix product.
///
/// Needs a significant memory workspace.
GEMM,
/// Compute the convolution as matrix product without forming the matrix that holds the input data.
///
/// Does not need any memory workspace.
ImplicitGEMM,
/// Similar to `ImplicitGEMM` but needs some workspace to precompile the implicit indices.
ImplicitPrecompiledGEMM,
/// Compute the convolution as Fast-Fourier Transform.
///
/// Needs a significant memory workspace.
FFT,
/// Compute the convolution as Fast-Fourier Transform with 32x32 tiles.
///
/// Needs a significant memory workspace.
FFTTiling,
/// Compute the convolution without implicit or explicit matrix-multiplication. **Do not try to use this**.
///
/// Listed in cuDNN docs but cuDNN does not provide a implementation.
Direct,
/// Winograd Transform
Winograd,
/// Winograd Transform Non-Fused
WinogradNonFused,
}
impl ConvForwardAlgo {
/// Check if algorithim should be chosen automatically.
pub fn is_auto(&self) -> bool {
match *self {
ConvForwardAlgo::Auto => true,
_ => false,
}
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Different algorithms to compute the gradient with respect to the filter.
pub enum ConvBackwardFilterAlgo {
/// Attempt to automatically find the best algorithm of all the other available ones.
Auto,
/// Compute the convolution as matrix product without forming the matrix that holds the input data.
///
/// Does not need any memory workspace.
///
/// The results are deterministic.
ImplicitGEMM,
/// Compute the convolution as sum of matrix product without forming the matrix that holds the input data.
///
/// Does not need any memory workspace.
///
/// The results are non-deterministic.
ImplicitGEMMSum,
/// Similar to `ImplicitGEMMSum` but needs some workspace to precompile the implicit indices.
///
/// The results are non-deterministic.
ImplicitPrecompiledGEMMSum,
/// Compute the convolution as Fast-Fourier Transform.
///
/// Needs a significant memory workspace.
///
/// The results are deterministic.
FFT,
/// Winograd Transform Non-Fused
WinogradNonFused,
}
impl ConvBackwardFilterAlgo {
/// Check if algorithim should be chosen automatically.
pub fn is_auto(&self) -> bool {
match *self {
ConvBackwardFilterAlgo::Auto => true,
_ => false,
}
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Different algorithms to compute the gradient with respect to the filter.
pub enum ConvBackwardDataAlgo {
/// Attempt to automatically find the best algorithm of all the other available ones.
Auto,
/// Compute the convolution as matrix product without forming the matrix that holds the input data.
///
/// Does not need any memory workspace.
///
/// The results are deterministic.
ImplicitGEMM,
/// Compute the convolution as sum of matrix product without forming the matrix that holds the input data.
///
/// Does not need any memory workspace.
///
/// The results are non-deterministic.
ImplicitGEMMSum,
/// Compute the convolution as Fast-Fourier Transform.
///
/// Needs a significant memory workspace.
///
/// The results are deterministic.
FFT,
/// Compute the convolution as Fast-Fourier Transform with 32x32 tiles.
///
/// Needs a significant memory workspace.
///
/// The results are deterministic.
FFTTiling,
/// Winograd Transform
Winograd,
/// Winograd Transform Non-Fused
WinogradNonFused,
}
impl ConvBackwardDataAlgo {
/// Check if algorithim should be chosen automatically.
pub fn is_auto(&self) -> bool {
match *self {
ConvBackwardDataAlgo::Auto => true,
_ => false,
}
}
}
/// Provides generic NN Operation Config functionality.
///
/// Needs to be implemented for Operation specific configurations.
pub trait NNOperationConfig<F> {}
/// Provides Convolution Config functionality.
///
/// Needs to be implemented for Operation specific configurations.
pub trait ConvolutionConfig<F> {
/// Returns the largest workspace size in bytes needed
/// for any of the convolution operations.
fn workspace_size(&self) -> usize {
0
}
}
/// Provides Rnn Config functionality.
///
/// Needs to be implemented for Operation specific configurations.
pub trait RnnConfig<F> {
/// Workspace Size - Overwritten by each plugin method except native, which doesn't require
/// a workspace size.
fn workspace_size(&self) -> usize {
0
}
}
/// Provides the functionality for a backend to support Neural Network related operations.
pub trait NN<F> {
/// The Convolution Operation Config representation for this Plugin.
type CC: NNOperationConfig<F> + ConvolutionConfig<F>;
/// The LRN Operation Config representation for this Plugin.
type CLRN: NNOperationConfig<F>;
/// The Pooling Operation Config representation for this Plugin.
type CPOOL: NNOperationConfig<F>;
// /// The Activation Operation Config representation for this Plugin.
// type CACTI: NNOperationConfig<F>;
/// The Dropout Operation Config representation for this Plugin.
type CDROP: NNOperationConfig<F>;
/// The RNN Operation Config representation for this Plugin
type CRNN: NNOperationConfig<F> + RnnConfig<F>;
/// Initializes the Plugin.
fn init_nn();
}
/// Provides the functionality for a Backend to support Sigmoid operations.
pub trait Sigmoid<F>: NN<F> {
/// Computes the [Sigmoid function][sigmoid] over the input Tensor `x`.
/// [sigmoid]: https://en.wikipedia.org/wiki/Sigmoid_function
///
/// Saves the result to `result`.
fn sigmoid(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a [Sigmoid function][sigmoid] over the input Tensor `x`.
/// [sigmoid]: https://en.wikipedia.org/wiki/Sigmoid_function
///
/// Saves the result to `result_diff`.
fn sigmoid_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for pointwise Sigmoid operations (overwrites the input with the result of the operation).
pub trait SigmoidPointwise<F>: NN<F> {
/// Computes the [Sigmoid function][sigmoid] over the input Tensor `x`.
/// [sigmoid]: https://en.wikipedia.org/wiki/Sigmoid_function
///
/// Saves the result back to `x`.
///
/// For a no-memory managed version see `sigmoid_pointwise_plain`.
fn sigmoid_pointwise(&self, x: &mut SharedTensor<F>) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a [Sigmoid function][sigmoid] over the input Tensor `x`.
/// [sigmoid]: https://en.wikipedia.org/wiki/Sigmoid_function
///
/// Saves the result back to `x_diff`.
fn sigmoid_pointwise_grad(
&self,
x: &SharedTensor<F>,
x_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for a Backend to support ReLU operations.
pub trait Relu<F>: NN<F> {
/// Computes the [Rectified linear units][relu] over the input Tensor `x`.
/// [relu]: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
///
/// Saves the result to `result`.
fn relu(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of [ReLU][relu] over the input Tensor `x`.
/// [relu]: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
///
/// Saves the result to `result_diff`.
fn relu_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for pointwise ReLU operations (overwrites the input with the result of the operation).
pub trait ReluPointwise<F>: NN<F> {
/// Computes the [Rectified linear units][relu] over the input Tensor `x`.
/// [relu]: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
///
/// Saves the result back to `x`.
fn relu_pointwise(&self, x: &mut SharedTensor<F>) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of [ReLU][relu] over the input Tensor `x`.
/// [relu]: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
///
/// Saves the result back to `x_diff`.
fn relu_pointwise_grad(
&self,
x: &SharedTensor<F>,
x_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for a Backend to support TanH operations.
pub trait Tanh<F>: NN<F> {
/// Computes the [hyperbolic Tangent][tanh] over the input Tensor `x`.
/// [tanh]: https://en.wikipedia.org/wiki/Hyperbolic_function
///
/// Saves the result to `result`.
fn tanh(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of [hyperbolic Tangent][tanh] over the input Tensor `x`.
/// [tanh]: https://en.wikipedia.org/wiki/Hyperbolic_function
///
/// Saves the result to `result_diff`.
fn tanh_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for pointwise ReLU operations (overwrites the input
/// with the result of the operation).
pub trait TanhPointwise<F>: NN<F> {
/// Computes the [hyperbolic Tangent][tanh] over the input Tensor `x`.
/// [tanh]: https://en.wikipedia.org/wiki/Hyperbolic_function
///
/// Saves the result back to `x`.
fn tanh_pointwise(&self, x: &mut SharedTensor<F>) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of [tanh][tanh] over the input Tensor `x`.
/// [tanh]: https://en.wikipedia.org/wiki/Hyperbolic_function
///
/// Saves the result back to `x_diff`.
fn tanh_pointwise_grad(
&self,
x: &SharedTensor<F>,
x_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provide the functionality for a Backend to support RNN operations
pub trait Rnn<F>: NN<F> {
/// Create a RnnConfig
fn new_rnn_config(
&self,
src: &SharedTensor<F>,
dropout_probability: Option<f32>,
dropout_seed: Option<u64>,
sequence_length: i32,
network_mode: RnnNetworkMode,
input_mode: RnnInputMode,
direction_mode: DirectionMode,
algorithm: RnnAlgorithm,
hidden_size: i32,
num_layers: i32,
batch_size: i32,
// RC being RNNConfig
) -> Result<Self::CRNN, crate::co::error::Error>;
/// Generate Weights for RNN
fn generate_rnn_weight_description(
&self,
rnn_config: &Self::CRNN,
input_size: i32,
) -> Result<Vec<usize>, crate::co::error::Error>;
/// Train a LSTM Network and Return Results
// TODO: Create alternate rnn_forward or alternate path to work with pretrained networks
/// # Arguments
/// * `weight_desc` Previously initialised FilterDescriptor for Weights
fn rnn_forward(
&self,
src: &SharedTensor<F>,
output: &mut SharedTensor<F>,
rnn_config: &Self::CRNN,
weight: &SharedTensor<F>,
workspace: &mut SharedTensor<u8>,
) -> Result<(), crate::co::error::Error>;
/// Calculates RNN Gradients for Input/Hidden/Cell
fn rnn_backward_data(
&self,
src: &SharedTensor<F>,
src_gradient: &mut SharedTensor<F>,
output: &SharedTensor<F>,
output_gradient: &SharedTensor<F>,
rnn_config: &Self::CRNN,
weight: &SharedTensor<F>,
workspace: &mut SharedTensor<u8>,
) -> Result<(), crate::co::error::Error>;
/// Calculates RNN Gradients for Weights
fn rnn_backward_weights(
&self,
src: &SharedTensor<F>,
output: &SharedTensor<F>,
filter: &mut SharedTensor<F>,
rnn_config: &Self::CRNN,
workspace: &mut SharedTensor<u8>,
) -> Result<(), crate::co::error::Error>;
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Network Type for RNN Networks [cudnnRNNMOde_t][1]
/// [1]: https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnRNNMode_t
pub enum RnnNetworkMode {
/// CUDNN_RNN_RELU - Single gate RNN with a ReLU activation function
ReLU,
/// Single-gate RNN with a tanh activation function
Tanh,
/// Four-gate LSTM Network with no peephole connection
LSTM,
/// Three-gate network with Gated Recurrent Units
GRU,
}
impl std::fmt::Display for RnnNetworkMode {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
let result = match self {
RnnNetworkMode::ReLU => "RelU",
RnnNetworkMode::Tanh => "Tanh",
RnnNetworkMode::LSTM => "LSTM",
RnnNetworkMode::GRU => "GRU",
};
write!(f, "{}", result)
}
}
impl RnnNetworkMode {
/// Convert RnnNetworkMode to String Representation
pub fn from_string(input: &str) -> Result<Self, &str> {
match input {
"GRU" => Ok(RnnNetworkMode::GRU),
"LSTM" => Ok(RnnNetworkMode::LSTM),
"ReLU" => Ok(RnnNetworkMode::ReLU),
"Tanh" => Ok(RnnNetworkMode::Tanh),
_ => Err("Unknown RnnType used - variants are GRU, LSTM, ReLU, and Tanhd"),
}
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Input Modes for RNN [cudnnRNNInputMode_t][1]
/// [1]: https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnRNNInputMode_t
pub enum RnnInputMode {
/// CUDNN_LINEAR_INPUT - A biased matrix multiplication is performed at the input of the first
/// recurrent layer
LinearInput,
/// CUDNN_SKIP_INPUT - No operation is performed at the input of the first recurrent layer -
/// if this is used then the leading dimension of the input tensor must be equal to the hidden
/// state size of the network.
SkipInput,
}
impl std::fmt::Display for RnnInputMode {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
let result = match self {
RnnInputMode::LinearInput => "LinearInput",
RnnInputMode::SkipInput => "SkipInput",
};
write!(f, "{}", result)
}
}
impl RnnInputMode {
/// Convert to RnnInputMode from String Representation
pub fn from_string(input: &str) -> Result<Self, &str> {
match input {
"LinearInput" => Ok(RnnInputMode::LinearInput),
"SkipInput" => Ok(RnnInputMode::SkipInput),
_ => Err("Unknown RnnInputMode used - variants are LinearInput, SkipInput"),
}
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Direction Mode for RNN [cudnnDirectionMode_t][1]
/// [1]: https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnDirectionMode_t
pub enum DirectionMode {
/// CUDNN_UNIDIRECTIONAL - The network iterates from first to last
UniDirectional,
/// CUDNN_BIDIRECTION - Concats recurrent output of First -> Last && Last -> First
BiDirectional,
}
impl std::fmt::Display for DirectionMode {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
let result = match self {
DirectionMode::UniDirectional => "UniDirectional",
DirectionMode::BiDirectional => "BiDirectional",
};
write!(f, "{}", result)
}
}
impl DirectionMode {
/// Convert to DirectionMode from String Representation
pub fn from_string(input: &str) -> Result<Self, &str> {
match input {
"UniDirectional" => Ok(DirectionMode::UniDirectional),
"BiDirectional" => Ok(DirectionMode::BiDirectional),
_ => Err("Unknown DirectionMode used - variants are UniDirectional, BiDirectional"),
}
}
}
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
/// Algorithm for RNN [cudnnRNNAlgo_t][1]
/// [1]: https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnRNNAlgo_t
///
/// Persist Static requires v6+
pub enum RnnAlgorithm {
/// Sequence of Operations for each RNN Layer
Standard,
/// Uses a Persistent Kernel - fast when the first D of the input is small
PersistStatic,
/// RNN parts use a persistent kernel. Fast when the first dimension is small, and when it can
/// reuse plans in repeated calls.
PersistDynamic,
/// Count - Cannot find in docs but is in Generated - FIXME
Count,
}
impl std::fmt::Display for RnnAlgorithm {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
let result = match self {
RnnAlgorithm::Standard => "Standard",
RnnAlgorithm::PersistStatic => "PersistStatic",
RnnAlgorithm::PersistDynamic => "PersistDynamic",
RnnAlgorithm::Count => unreachable!(),
};
write!(f, "{}", result)
}
}
impl RnnAlgorithm {
/// Convert to RnnAlgorithm from String Representation
fn from_string(input: &str) -> Result<Self, &str> {
match input {
"Standard" => Ok(RnnAlgorithm::Standard),
"PersistStatic" => Ok(RnnAlgorithm::PersistStatic),
"PersistDynamic" => Ok(RnnAlgorithm::PersistDynamic),
_ => Err(
"Unknown RnnAlgorithm used - variants are Standard, PersistStatic, PersistDynamic",
),
}
}
}
#[derive(Debug, Copy, Clone)]
/// Enables/Disables the padded input/output [cudnnRNNPaddingMode_t][1]
/// [1]: https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnRNNPaddingMode_t
pub enum RnnPaddingMode {
/// Padding disabled
Disabled,
/// Padding enabled
Enabled,
}
#[derive(Debug, Copy, Clone)]
/// Indicate if Tensor Core Operations are permitted [cudnnMathType_t][1]
/// [1]: https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnMathType_t
pub enum MathType {
/// No Tensor Core ops
Default,
/// Uses Tensor Core ops
TensorOPMath,
/// Uses FP32 Tensors for input/output
TensorOPMathAllowConversion,
}
/// Provides the functionality for a Backend to support Convolution operations.
pub trait Convolution<F>: NN<F> {
/// Creates a new ConvolutionConfig, which needs to be passed to further
/// convolution Operations.
fn new_convolution_config(
&self,
src: &SharedTensor<F>,
dest: &SharedTensor<F>,
filter: &SharedTensor<F>,
algo_fwd: ConvForwardAlgo,
algo_bwd_filter: ConvBackwardFilterAlgo,
algo_bwd_data: ConvBackwardDataAlgo,
stride: &[i32],
zero_padding: &[i32],
) -> Result<Self::CC, crate::co::error::Error>;
/// Computes a [CNN convolution][convolution] over the input Tensor `x`.
/// [convolution]: https://en.wikipedia.org/wiki/Convolutional_neural_network
///
/// Saves the result to `result`.
fn convolution(
&self,
filter: &SharedTensor<F>,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
workspace: &mut SharedTensor<u8>,
config: &Self::CC,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a [CNN convolution][convolution] with respect to the filter.
/// [convolution]: https://en.wikipedia.org/wiki/Convolutional_neural_network
///
/// Saves the result to `filter_diff`.
fn convolution_grad_filter(
&self,
src_data: &SharedTensor<F>,
dest_diff: &SharedTensor<F>,
filter_diff: &mut SharedTensor<F>,
workspace: &mut SharedTensor<u8>,
config: &Self::CC,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a [CNN convolution][convolution] over the input
/// Tensor `x` with respect to the data.
/// [convolution]: https://en.wikipedia.org/wiki/Convolutional_neural_network
///
/// Saves the result to `result_diff`.
fn convolution_grad_data(
&self,
filter: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
workspace: &mut SharedTensor<u8>,
config: &Self::CC,
) -> Result<(), crate::co::error::Error>;
// /// Computes the backward Convolution function w.r.t the bias.
// ///
// /// Writes the result of the computation to `bias_data`.
// pub fn convolution_backward_bias<T>(
// &self,
// dest_grad_desc: &TensorDescriptor,
// dest_grad_data: *const ::libc::c_void,
// bias_desc: &TensorDescriptor,
// bias_data: *mut ::libc::c_void,
// scale: ScalParams<T>,
// }
//
// /// Computes the backward Convolution function w.r.t the filter.
// ///
// /// Writes the result of the computation to `filter_data`.
// pub fn convolution_backward_filter<T>(
// &self,
// conv_config: &ConvolutionConfig,
// src_desc: &TensorDescriptor,
// src_data: *const ::libc::c_void,
// dest_grad_desc: &TensorDescriptor,
// dest_grad_data: *const ::libc::c_void,
// filter_data: *mut ::libc::c_void,
// scale: ScalParams<T>,
// }
}
/// Provides the functionality for a Backend to support Softmax operations.
pub trait Softmax<F>: NN<F> {
/// Computes a [Softmax][softmax] over the input Tensor `x`.
/// [softmax]: https://en.wikipedia.org/wiki/Softmax_function
///
/// Saves the result to `result`.
fn softmax(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a [Softmax][softmax] over the input Tensor `x`.
/// [softmax]: https://en.wikipedia.org/wiki/Softmax_function
///
/// Saves the result to `result_diff`.
fn softmax_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for a Backend to support LogSoftmax operations.
pub trait LogSoftmax<F>: NN<F> {
/// Computes a logarithmic softmax over the input Tensor `x`.
///
/// Saves the result to `result`.
fn log_softmax(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a logarithmic softmax over the input Tensor `x`.
///
/// Saves the result to `result_diff`.
fn log_softmax_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for a Backend to support Local Response Normalization operations.
pub trait LRN<F>: NN<F> {
/// Creates a new (Local Response Normalization) LRNConfig, which needs to be
/// passed to further LRN Operations.
fn new_lrn_config(
&self,
n: u32,
alpha: f64,
beta: f64,
k: f64,
) -> Result<Self::CLRN, crate::co::error::Error>;
/// Computes a [LRN][lrn] over the input Tensor `x`.
/// [lrn]: https://en.wikipedia.org/wiki/lrnal_neural_network
///
/// Saves the result to `result`.
fn lrn(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
config: &Self::CLRN,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of a [LRN][lrn] over the input Tensor `x`.
/// [lrn]: https://en.wikipedia.org/wiki/lrnal_neural_network
///
/// Saves the result to `result_diff`.
fn lrn_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
config: &Self::CLRN,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for a Backend to support Pooling operations.
pub trait Pooling<F>: NN<F> {
/// Creates a new PoolingConfig, which needs to be passed to further pooling Operations.
fn new_pooling_config(
&self,
window: &[i32],
stride: &[i32],
padding: &[i32],
) -> Result<Self::CPOOL, crate::co::error::Error>;
/// Computes non-linear down-sampling ([max Pooling][pooling]) over the input Tensor `x`.
/// [pooling]: https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer
///
/// Saves the result to `result`.
fn pooling_max(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
config: &Self::CPOOL,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of [max Pooling][pooling] over the input Tensor `x`.
/// [pooling]: https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer
///
/// Saves the result to `result_diff`.
fn pooling_max_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
config: &Self::CPOOL,
) -> Result<(), crate::co::error::Error>;
/// Computes non-linear down-sampling ([average Pooling][pooling]) over the input Tensor `x`.
/// [pooling]: https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer
///
/// Saves the result to `result`.
fn pooling_avg(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
config: &Self::CPOOL,
) -> Result<(), crate::co::error::Error>;
/// Computes the gradient of [average Pooling][pooling] over the input Tensor `x`.
/// [pooling]: https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer
///
/// Saves the result to `result_diff`.
fn pooling_avg_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
config: &Self::CPOOL,
) -> Result<(), crate::co::error::Error>;
}
/// Provides the functionality for a Backend to support Dropout operations.
pub trait Dropout<F>: NN<F> {
/// Creates a new DropoutConfig, which needs to be passed to further dropout Operations.
fn new_dropout_config(
&self,
dropout: f32,
seed: u64,
) -> Result<Self::CDROP, crate::co::error::Error>;
/// Computes non-linear down-sampling ([max Pooling][pooling]) over the input Tensor `x`.
/// [pooling]: https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer
///
/// Saves the result to `result`.
fn dropout(
&self,
x: &SharedTensor<F>,
result: &mut SharedTensor<F>,
config: &Self::CDROP,
) -> Result<(), crate::co::error::Error>;
/// Computes non-linear down-sampling ([max Pooling][pooling]) over the input Tensor `x`.
/// [pooling]: https://en.wikipedia.org/wiki/Dropout_(neural_networks)
///
/// Saves the result to `result`.
fn dropout_grad(
&self,
x: &SharedTensor<F>,
x_diff: &SharedTensor<F>,
result: &SharedTensor<F>,
result_diff: &mut SharedTensor<F>,
config: &Self::CDROP,
) -> Result<(), crate::co::error::Error>;
}