gptneox-chat / model-session.R
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tokenize from the main session
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model_session <- R6::R6Class(
lock_objects = FALSE,
public = list(
initialize = function() {
self$task_q <- NULL
self$temperature <- 1
self$top_k <- 50
self$is_loaded <- NULL
},
load_model = function(repo) {
if (!is.null(self$sess)) {
cat("Model is already loaded.", "\n")
return(self$task_q$push(function() "done"))
}
# the tokenizer doesn't need to live in the remote session.
self$tok <- tok::tokenizer$from_pretrained(repo)
self$task_q <- callq::task_q$new(num_workers = 1)
self$task_q$push(args = list(repo = repo), function(repo) {
library(torch)
library(zeallot)
library(minhub)
device <- if (cuda_is_available()) "cuda" else "cpu"
model <<- minhub::gptneox_from_pretrained(repo)
model$eval()
if (device == "cuda") {
model$to(dtype=torch_half())
model$to(device=device)
} else {
model$to(dtype = torch_float())
}
"done"
})
},
generate = function(idx) {
if (is.null(self$task_q)) {
cat("Model is not loaded, error.", "\n")
return(self$task_q$push(function() stop("Model is not loaded")))
}
args <- list(
idx = idx,
temperature = self$temperature,
top_k = self$top_k
)
self$task_q$push(args = args, function(idx, temperature, top_k) {
device <- if (cuda_is_available()) "cuda" else "cpu"
idx <- torch_tensor(idx, device=device)$view(c(1, -1))
with_no_grad({
logits <- model(idx + 1L)$to(dtype="float", device="cpu")
})
logits <- logits[,-1,]/temperature
c(prob, ind) %<-% logits$topk(top_k)
logits <- torch_full_like(logits, -1e7)$scatter_(-1, ind, prob)
logits <- nnf_softmax(logits, dim = -1)
id_next <- torch::torch_multinomial(logits, num_samples = 1)$cpu() - 1L
as.integer(id_next)
})
}
)
)