pth_load: Read the tensors of a PyTorch checkpoint

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Read the tensors of a PyTorch checkpoint

Description

Reads a .pth file saved by torch.save() into plain R arrays. No Python and no torch: a checkpoint is a zip of one metadata pickle and one entry per tensor, and both are read here directly.

Usage

pth_load(path, names = NULL)

Arguments

path	Path to a .pth file saved by torch.save().
names	Optional character vector of tensor names to read. The default reads all of them. Use pth_catalogue to see what is there.

Value

A named list of numeric arrays, in the checkpoint's own order. Scalars come back as length-1 vectors; everything else carries a dim. The attribute "file" carries the path.

Row-major, and what that means for shapes

PyTorch stores a tensor row-major and R stores an array column-major, so the same bytes describe transposed arrays in the two languages. The tensors come back with their dimensions reversed — a PyTorch [768, 256] weight arrives as a 256 x 768 R matrix — which keeps the elements where they belong without moving a single one. For a linear layer that is the happy case: x %*% w is then the layer's own arithmetic, no transpose needed.

See Also

pth_catalogue to list a checkpoint without reading it.

Examples

## Not run: 
w <- pth_load("model.pth")
dim(w$output_layer.weight)

# just the one tensor
pth_load("model.pth", names = "output_layer.bias")

## End(Not run)

ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.