add chargpt model

vignettes/examples/chargpt.Rmd

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+---
+title: "CharGPT"
+desc: "Train a character-level GPT-2 on Shakespeare texts."
+category: 'intermediate'
+---
+
+This example is inspired by the [chargpt](https://github.com/karpathy/minGPT/tree/master/projects/chargpt) project by Andrey Karpathy.
+We are going to train character-level language model on Shakespeare texts.
+
+We first load the libraries that we plan to use:
+
+```{r, eval = FALSE}
+library(torch)
+library(luz)
+library(zeallot)
+```
+
+Next we define the torch dataset that will pre-process data for the model. It splits the text into a character vector,
+each element containing exactly one character. 
+
+Then lists all unique characters into the `vocab` attribute. The order of the characters in the vocabulary is used to 
+encode each character to an integer value, that will be used in the embedding layer.
+
+The `.getitem()` method, can take chunks of `block_size` characters and encode them into their integer representation.
+
+```{r, eval=FALSE}
+url <- "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
+
+char_dataset <- torch::dataset(
+    initialize = function(data, block_size = 128) {
+        self$block_size <- block_size
+        self$data <- stringr::str_split_1(data, "")
+
+        self$data_size <- length(self$data)
+        self$vocab <- unique(self$data)
+        self$vocab_size <- length(self$vocab)
+    },
+    .getitem = function(i) {
+        chunk <- self$data[i + seq_len(self$block_size + 1)]
+        idx <- match(chunk, self$vocab)
+        list(
+            x = head(idx, self$block_size),
+            y = tail(idx, self$block_size)
+        )
+    },
+    .length = function() {
+        self$data_size - self$block_size - 1L # this is to account the last value
+    }
+)
+
+dataset <- char_dataset(readr::read_file(url))
+dataset[1] # this allows us to see an element of the dataset
+```
+
+We then define the neural net we are going to train. Defining a GPT-2 model is quite verbose, so we are going
+to use the minhub implementation directly. You can find the full model definition [here](https://github.com/mlverse/minhub/blob/main/R/gpt2.R), 
+and this code is entirely self-contained, so you don't need to install minhub, if you don't want to.
+
+We also implemented the `generate` method for the model, that allows one to generate completions using the model.
+It applies the model in a loop, at each iteration prediction what's the next character.
+
+```{r, eval=FALSE}
+model <- torch::nn_module(
+    initialize = function(vocab_size) {
+        # remotes::install_github("mlverse/minhub")
+        self$gpt <- minhub::gpt2(
+            vocab_size = vocab_size,
+            n_layer = 6,
+            n_head = 6,
+            n_embd = 192
+        )
+    },
+    forward = function(x) {
+        # we have to transpose to make the vocabulary the last dimension
+        self$gpt(x)$transpose(2,3)
+    },
+    generate = function(x, temperature = 1, iter = 50, top_k = 10) {
+        # samples from the model givn a context vector.
+        for (i in seq_len(iter)) {
+            logits <- self$forward(x)[,,-1]
+            logits <- logits/temperature
+            c(prob, ind) %<-% logits$topk(top_k)
+            logits <- torch_full_like(logits, -Inf)$scatter_(-1, ind, prob)
+            logits <- nnf_softmax(logits, dim = -1)
+            id_next <- torch_multinomial(logits, num_samples = 1)
+            x <- torch_cat(list(x, id_next), dim = 2)
+        }
+        x
+    }
+)
+```
+
+Next, we implemented a callback that is used for nicely displaying generated samples during the model
+training:
+
+```{r, eval=FALSE}
+# samples from the model using the context.
+generate <- function(model, vocab, context, ...) {
+  local_no_grad() # disables gradient for sampling
+  x <- match(stringr::str_split_1(context, ""), vocab)
+  x <- torch_tensor(x)[NULL,]$to(device = model$device)
+  content <- as.integer(model$generate(x, ...)$cpu())
+  paste0(vocab[content], collapse = "")
+}
+
+display_cb <- luz_callback(
+  initialize = function(iter = 500) {
+    self$iter <- iter # print every 500 iterations
+  },
+  on_train_batch_end = function() {
+    if (!(ctx$iter %% self$iter == 0))
+      return()
+
+    ctx$model$eval()
+    with_no_grad({
+      # sample from the model...
+      context <- "O God, O God!"
+      text <- generate(ctx$model, dataset$vocab, context, iter = 100)
+      cli::cli_h3(paste0("Iter ", ctx$iter))
+      cli::cli_text(text)
+    })
+
+  }
+)
+```
+
+Finally, you can train the model using `fit`:
+
+```{r, eval=FALSE}
+fitted <- model |>
+    setup(
+        loss = nn_cross_entropy_loss(),
+        optimizer = optim_adam
+    ) |>
+    set_opt_hparams(lr = 5e-4) |>
+    set_hparams(vocab_size = dataset$vocab_size) |>
+    fit(
+      dataset,
+      dataloader_options = list(batch_size = 128, shuffle = TRUE),
+      epochs = 1,
+      callbacks = list(
+        display_cb(iter = 500),
+        luz_callback_gradient_clip(max_norm = 1)
+      )
+    )
+```
+
+One epoch, is reasonable for this dataset and takes ~1h on the M1 MBP.
+You can generate new samples with:
+
+```{r, eval=FALSE}
+context <- "O God, O God!"
+text <- generate(ctx$model, dataset$vocab, context, iter = 100)
+```
+
+