# singleGateRNNGradients(_:recurrentWeight:sourceGradient:zState:inputWeight:bias:initState:descriptor:name:) Creates a single-gate RNN gradient operation and returns the gradient tensor values. ``` func singleGateRNNGradients(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, sourceGradient: MPSGraphTensor, zState: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, initState: MPSGraphTensor?, descriptor: MPSGraphSingleGateRNNDescriptor, name: String?) -> [MPSGraphTensor] ``` ## Parameters `source` A tensor that contains the source data `x[t]` with the data layout [T,N,I]. In case `inputWeight = nil` and `bidirectional = NO` then the layout is [T,N,H] and for `inputWeight = nil` and `bidirectional = YES` the layout is [T,N,2H]. `recurrentWeight` A tensor containing the recurrent weights `R`. For `bidirectional` the layout is [2,H,H] and otherwise it is [H,H]. Note: For `bidirectional` this tensor must have a static shape. `sourceGradient` The input gradient, that is the gradient of a tensor with respect to the first output of the forward pass. `zState` The second output of [`singleGateRNN(_:recurrentWeight:inputWeight:bias:initState:mask:descriptor:name:)`](/documentation/MetalPerformanceShadersGraph/MPSGraph/singleGateRNN(_:recurrentWeight:inputWeight:bias:initState:mask:descriptor:name:)) with `descriptor.training = YES`. `inputWeight` A tensor containing the input weights matrix `W` - optional, if missing the operation assumes a diagonal unit-matrix. For `bidirectional` the layout is [2H,I] and otherwise it is [H,I]. `bias` A tensor containing the bias `b` - optional, if missing the operation assumes zeroes. For `bidirectional` the layout is [2H] and otherwise it is [H]. `initState` The initial internal state of the RNN `h[-1]` - optional, if missing the operation assumes zeroes. For `bidirectional` the layout is [N,2H] and otherwise it is [N,H]. `descriptor` A descriptor that defines the parameters for the RNN operation. `name` The name for the operation. ## Return Value A valid `MPSGraphTensor` array containing gradients for each input tensor, except for `sourceGradient` and `mask`. In case an input is `nil`, no gradient will be returned for it. The order of the gradients will be: for `source`, for `recurrentWeight`, for `inputWeight`, for `bias` and finally for `initState`. ## Discussion For details of this operation and parameters, refer to documentation of [`singleGateRNN(_:recurrentWeight:inputWeight:bias:initState:mask:descriptor:name:)`](/documentation/MetalPerformanceShadersGraph/MPSGraph/singleGateRNN(_:recurrentWeight:inputWeight:bias:initState:mask:descriptor:name:)). --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)