ggml_norm_back: Layer Norm Backward (Graph)
In ggmlR: 'GGML' Tensor Operations for Machine Learning
ggml_norm_back R Documentation
Layer Norm Backward (Graph)
Description
Creates a graph node for the backward pass of ggml_norm, i.e.
of LayerNorm without gamma/beta. Upstream ggml has no backward for
GGML_OP_NORM; ggmlR adds it with CPU and Vulkan kernels, which is what
makes ggml_layer_layer_norm trainable.
Usage
ggml_norm_back(ctx, a, b, eps = 1e-05)
Arguments
ctx
GGML context
a
Gradient tensor (dy from the forward output)
b
Input tensor (x from the forward pass)
eps
Epsilon for numerical stability (default 1e-5)
Value
Tensor representing the gradient with respect to input
See Also
ggml_norm, ggml_rms_norm_back
ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.
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| ggml_norm_back | R Documentation |
Layer Norm Backward (Graph)
Description
Creates a graph node for the backward pass of ggml_norm, i.e.
of LayerNorm without gamma/beta. Upstream ggml has no backward for
GGML_OP_NORM; ggmlR adds it with CPU and Vulkan kernels, which is what
makes ggml_layer_layer_norm trainable.
Usage
ggml_norm_back(ctx, a, b, eps = 1e-05)
Arguments
ctx |
GGML context |
a |
Gradient tensor (dy from the forward output) |
b |
Input tensor (x from the forward pass) |
eps |
Epsilon for numerical stability (default 1e-5) |
Value
Tensor representing the gradient with respect to input
See Also
ggml_norm, ggml_rms_norm_back
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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