inst/doc/data-parallel-training.R

In ggmlR: 'GGML' Tensor Operations for Machine Learning

## ----setup, include=FALSE-----------------------------------------------------
# Vignette code is executed locally (NOT_CRAN=true) but not on CRAN, where
# the CPU fallback would multi-thread and trip the "CPU time > elapsed" NOTE.
knitr::opts_chunk$set(eval = identical(Sys.getenv("NOT_CRAN"), "true"))

## -----------------------------------------------------------------------------
# library(ggmlR)

## -----------------------------------------------------------------------------
# data(iris)
# set.seed(42)
# 
# x_cm <- t(scale(as.matrix(iris[, 1:4])))    # [4, 150]
# y_oh <- t(model.matrix(~ Species - 1, iris)) # [3, 150]
# 
# # Dataset as list of (x, y) pairs — one sample each
# dp_data <- lapply(seq_len(ncol(x_cm)), function(i)
#   list(x = x_cm[, i, drop = FALSE],
#        y = y_oh[, i, drop = FALSE]))
# 
# # Model factory — called once per replica
# make_model <- function() {
#   ag_sequential(
#     ag_linear(4L, 32L, activation = "relu"),
#     ag_linear(32L, 3L)
#   )
# }
# 
# result <- dp_train(
#   make_model = make_model,
#   data       = dp_data,
#   loss_fn    = function(out, tgt) ag_softmax_cross_entropy_loss(out, tgt),
#   forward_fn = function(model, s)  model$forward(ag_tensor(s$x)),
#   target_fn  = function(s)         s$y,
#   n_gpu      = 1L,         # set to ggml_vulkan_device_count() for multi-GPU
#   n_iter     = 2000L,
#   lr         = 1e-3,
#   verbose    = TRUE
# )
# 
# cat("Final loss:", result$loss, "\n")
# model <- result$model

## -----------------------------------------------------------------------------
# n_gpu <- max(1L, ggml_vulkan_device_count())
# cat(sprintf("Training on %d GPU(s)\n", n_gpu))
# 
# result_mg <- dp_train(
#   make_model = make_model,
#   data       = dp_data,
#   loss_fn    = function(out, tgt) ag_softmax_cross_entropy_loss(out, tgt),
#   forward_fn = function(model, s)  model$forward(ag_tensor(s$x)),
#   target_fn  = function(s)         s$y,
#   n_gpu      = n_gpu,
#   n_iter     = 2000L,
#   lr         = 1e-3,
#   max_norm   = 5.0,      # gradient clipping
#   verbose    = FALSE
# )

## -----------------------------------------------------------------------------
# result <- dp_train(
#   make_model = make_model,
#   data       = dp_data,
#   loss_fn    = function(out, tgt) ag_softmax_cross_entropy_loss(out, tgt),
#   forward_fn = function(model, s)  model$forward(ag_tensor(s$x)),
#   target_fn  = function(s)         s$y,
#   n_gpu      = 1L,
#   n_iter     = 2000L,
#   lr         = 1e-3,
#   max_norm   = 1.0       # clip to unit norm
# )

## -----------------------------------------------------------------------------
# x_tr <- x_cm[, 1:120];  y_tr <- y_oh[, 1:120]
# 
# dl <- ag_dataloader(x_tr, y_tr, batch_size = 32L, shuffle = TRUE)
# 
# model2  <- make_model()
# params2 <- model2$parameters()
# opt2    <- optimizer_adam(params2, lr = 1e-3)
# 
# ag_train(model2)
# for (ep in seq_len(100L)) {
#   for (batch in dl$epoch()) {
#     with_grad_tape({
#       loss <- ag_softmax_cross_entropy_loss(
#         model2$forward(batch$x), batch$y$data)
#     })
#     grads <- backward(loss)
#     opt2$step(grads);  opt2$zero_grad()
#   }
# }

## -----------------------------------------------------------------------------
# # inst/examples/dp_train_demo.R

## -----------------------------------------------------------------------------
# # inst/examples/multi_gpu_example.R