ggml_rms_norm_back

Creates a graph node for backward pass of RMS normalization.
Used in training for computing gradients.

Provides 'R' bindings to the 'GGML' tensor library for machine
learning, designed primarily for 'Vulkan' GPU acceleration with full CPU
fallback. 'Vulkan' support is auto-detected at build time on Linux (when
'libvulkan-dev' and 'glslc' are installed) and on Windows (when 'Vulkan'
'SDK' is installed and 'VULKAN_SDK' environment variable is set); all
operations fall back to CPU transparently when no GPU is available.
Implements tensor operations, neural network layers, quantization, and a
'Keras'-like sequential model API for building and training networks.
Includes 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient
Descent) optimizers with 'MSE' (Mean Squared Error) and cross-entropy
losses. Also provides a dynamic 'autograd' engine ('PyTorch'-style) with
data-parallel training via 'dp_train()', broadcast arithmetic, 'f16'
(half-precision) support on 'Vulkan' GPU, and a multi-head attention layer
for building Transformer architectures. Serves as backend for 'LLM' (Large
Language Model) inference via 'llamaR' and Stable Diffusion image
generation via 'sdR'. See <https://github.com/ggml-org/ggml> for more
information about the underlying library.

Yuri Baramykov

ggmlR

'GGML' Tensor Operations for Machine Learning

Georgi Gerganov

Jeffrey Quesnelle

Bowen Peng

Mozilla Foundation 

ggml_rms_norm_back function

<dl><dt>ctx</dt>
<dd>GGML context</dd>
<dt>a</dt>
<dd>Input tensor (x from forward pass)</dd>
<dt>b</dt>
<dd>Gradient tensor (dy)</dd>
<dt>eps</dt>
<dd>Epsilon for numerical stability (default 1e-5)</dd></dl>

Arguments

RMS Norm Backward (Graph) — ggml_rms_norm_back

<dl>

<dt>ctx</dt>
<dd>GGML context</dd>


<dt>a</dt>
<dd>Input tensor (x from forward pass)</dd>


<dt>b</dt>
<dd>Gradient tensor (dy)</dd>


<dt>eps</dt>
<dd>Epsilon for numerical stability (default 1e-5)</dd>

</dl>

ggml_rms_norm_back: RMS Norm Backward (Graph)

Description

Usage

Value

Arguments