LLaMA in R with Keras and TensorFlow

OpenAI’s chatGPT has woke up a collective consciousness of what Giant
Language Fashions (LLMs) are able to. With that awakening comes a each day
march of LLM information: new merchandise, new options, new fashions, new
capabilities, (and new worries). It appears we’re within the early phases of a
Cambrian explosion of LLMs and LLM powered instruments; it’s not but clear how
LLMs will impression and affect our skilled and private lives, however
it appears clear that they may, not directly.

Since LLMs are right here to remain, it’s worthwhile to take a while to
perceive how these fashions work from a first-principles perspective.
Beginning with the mechanics can assist foster sturdy intuitions that can
inform our utilization of those fashions now and sooner or later. (Particularly if
the longer term is one the place LLMs are a staple of the info scientist’s
toolbox, as frequent as an lm() operate name).

And what higher means is there to be taught than by doing. So with that
preamble, on this publish we’ll stroll by way of an implementation of an LLM,
LLaMA (Touvron et al. 2023)
particularly, in TensorFlow and Keras, with the objective being to develop
understanding first, functionality second.

Why LLaMA? With the sheer quantity of LLM associated content material and information out
there, it could actually appear formidable to know the place to get began. Nearly weekly
it appears there’s a new mannequin introduced. Searching some hubs of LLM
exercise (HuggingFace,
TFHub,
reddit,
HackerNews) muddies the waters even
extra. The way to choose a selected mannequin?

Of the various LLM-related information gadgets prior to now months, one which stands
head-and-shoulders above the gang is the launch of
LLaMA,
a contemporary, foundational LLM made obtainable to the general public by Meta AI in
February 2023. On frequent benchmarks, LLaMA outperforms OpenAI’s GPT-3,
whereas being considerably smaller (although nonetheless giant).

LLaMA is a good beginning place as a result of it’s a easy and trendy
structure, has glorious efficiency on benchmarks, and is open. The
mannequin structure has had only a few new concepts integrated into it since
the unique Transformer structure first described in,
“Consideration Is All You Want”
printed from Google (Vaswani et al. 2017). 4 completely different sizes of
LLaMA have been launched: 7 billion and 13 billion parameter fashions
educated on 1 Trillion tokens, and 33 billion and 65 billion parameter
fashions educated on 1.4 trillion tokens. This is a gigantic quantity of
coaching knowledge these fashions have seen–the most important 65B mannequin has been
educated on roughly the “Chinchilla
compute-optimum” (Hoffmann et al. 2022)
variety of tokens, whereas the smaller LLaMAs are considerably
past that optimum. On this weblog publish we’ll concentrate on the smallest, 7B
parameter LLaMA mannequin, which you’ll comfortably load domestically and run on
CPU with solely 64Gb of RAM.

Whereas not strictly needed, to comply with alongside domestically, you’ll most likely
wish to purchase the pre-trained LLaMA weights one
means or
one other. Notice, the
weights do include their very own license, which you’ll preview
right here.

So, with out additional ado, let’s get began.

Setup

First, we’ll wish to set up the required R and Python packages, and
configure a digital atmosphere:

remotes::install_github(c("rstudio/reticulate",
                          "rstudio/tensorflow",
                          "rstudio/keras"))
# reticulate::install_python("3.10:newest")                          
reticulate::virtualenv_create("./.venv", model = "3.10:newest")
tensorflow::install_tensorflow(envname = "./.venv", model = "launch",
                               extra_packages = "tensorflow-text")

library(purrr)
library(envir)

library(tensorflow)
library(tfautograph)
library(keras)

use_virtualenv("./.venv")
choices(tensorflow.extract.warn_tensors_passed_asis = FALSE)

attach_eval(>
        tokenizer$detokenize() )

# reticulate::py_install("torch", pip = TRUE)
torch <- reticulate::import("torch", convert = FALSE)
with_dir("~/github/facebookresearch/llama/weights/LLaMA/7B", > self$wq()
  okay <- x )

weights_path <- operate(filename) normalizePath(file.path(
  "~/github/facebookresearch/llama/weights/LLaMA/",
  glue(filename, .envir = dad or mum.body())), mustWork = TRUE)

params <- read_json(weights_path("7B/params.json"))
str(params)

Record of 6
 $ dim        : int 4096
 $ multiple_of: int 256
 $ n_heads    : int 32
 $ n_layers   : int 32
 $ norm_eps   : num 1e-06
 $ vocab_size : int -1

SentencePiece tokenizer from
Google. SentencePiece is offered as a TensorFlow graph operation
by way of
tf_text.SentencepieceTokenizer,
and likewise as a Keras layer in
keras_nlp.tokenizers.SentencepieceTokenizer.
By alternative of a coin flip, we’ll use the lower-level tf_text interface.

tf_text <- reticulate::import("tensorflow_text")
tokenizer_path <- weights_path("tokenizer.mannequin")
tokenizer <- tf_text$SentencepieceTokenizer(
  tf$io$gfile$GFile(tokenizer_path, "rb")$learn(),
  add_bos = TRUE, add_eos = FALSE,
)

immediate <- "One of the simplest ways to draw bees"
tokenizer$tokenize(immediate)

tf.Tensor([    1   450  1900   982   304 13978   367   267], form=(8), dtype=int32)

immediate |> tokenizer$tokenize() |> tokenizer$detokenize()

tf.Tensor(b'One of the simplest ways to draw bees', form=(), dtype=string)

show_tokens <- operate(what) 

    # norm and a focus
    x2 <- x 

show_tokens(immediate)

        1       450      1900       982       304     13978       367       267
       ""     "The"    "finest"     "means"      "to" "entice"      "be"      "es"

Notice that “bees” is 2 tokens. Not each token corresponds to a phrase.
For instance, one non-word token we are able to reliably count on to point out up in a
tokenizer educated on a corpus of English textual content is “ing.” Nonetheless, when the
“ing” token reveals up is not going to all the time comply with your intuitions, as a result of
frequent phrases get their very own token id, even when they are often decomposed into
a number of tokens.

    1  2348
   "" "ing"

        1      1985
       "" "working"

     1   8525    292
    "" "flex"  "ing"

     1   2113   9292
    ""  "gained" "king"

as.character(tokenizer$id_to_string(0L))

[1] ""

as.character(tokenizer$id_to_string(1L))

[1] ""

as.character(tokenizer$id_to_string(2L))

[1] ""

    1     0     2
   "" " ⁇ "    ""

as.integer(tokenizer$vocab_size())

[1] 32000

show_tokens(seq(50, len = 10))

 50  51  52  53  54  55  56  57  58  59
"/" "0" "1" "2" "3" "4" "5" "6" "7" "8"

show_tokens(seq(100, len = 10))

100 101 102 103 104 105 106 107 108 109
"a" "b" "c" "d" "e" "f" "g" "h" "i" "j"

show_tokens(seq(1000, len = 10))

   1000    1001    1002    1003    1004    1005    1006    1007    1008    1009
  "ied"    "ER"  "stat"   "fig"    "me"   "von" "inter"  "roid"  "ater" "their"

show_tokens(seq(10000, len = 10))

   10000    10001    10002    10003    10004    10005    10006    10007
   "ång"  "citep"    "Sick"   "rank" "sender"   "beim"    "рак" "compat"
   10008    10009
"happens"  "diese"

show_tokens(seq(20000, len = 10))

    20000     20001     20002     20003     20004     20005     20006     20007
  "admit" "Remark"     "стя"    "Vien"      "ці"  "permut"     "cgi"    "crít"
    20008     20009
"Console"    "ctic"

show_tokens(seq(to = as.integer(tokenizer$vocab_size()) - 1, len = 10))

31990 31991 31992 31993 31994 31995 31996 31997 31998 31999
  "ὀ"  "げ"  "べ"  "边"  "还"  "黃"  "왕"  "收"  "弘"  "给"

tok_embeddings <- keras$layers$Embedding(
  input_dim = tokenizer$vocab_size(),
  output_dim = params$dim,
  embeddings_initializer =
    (...) np$load(weights_path("7B/tok_embeddings.weight.npy"))
)

tok_embeddings(3L) |> str()

immediate |> # "One of the simplest ways to draw bees"
  tokenizer$tokenize() |>
  tok_embeddings() |>
  str()

weights_path("7B/params.json")  |> read_json() |> _$n_layers

[1] 32

weights_path("65B/params.json") |> read_json() |> _$n_layers

[1] 80

TransformerBlock(keras$layers$Layer) %py_class% 
  x <- x

We implement the TransformerBlock as a subclassed
keras.layers.Layer. That is offers us some niceties like the power to
compose with different Keras layers, however these are principally irrelevant to the
goal of this weblog publish; we may simply as simply implement this as,
for instance, a vanilla R6 class. Our TransformerBlock class has two
strategies: initialize, known as once we first create the block, and
name, known as once we run the ahead move of the block.

x2 <- x |> ...
x <- x + x2 # add residual x to x2

vanishing gradient
drawback. It’s
a skip-connection within the other-wise linear sequence of matrix
transformations. It reinjects info (throughout the ahead move), and
gradients (throughout again propagation), again into the trunk. You may suppose
of those residual connections as liberating the learnable layers in-between
(the ... within the pseudo code) from the burden of getting to
“pass-through” or “protect” info in x, permitting the weights to
as a substitute concentrate on studying transformations which might be, (in corporatese
vernacular), value-adding.

The following composition sample to notice is the repeating utilization of a
normalization layer:

x2 <- x |> norm() |> ...
x <- x + x2

x |> consideration() |> feed_forward()

In a second we’ll see that that feed_foward is a barely fancier
variation of a standard sequence of Dense layer. Earlier than we get
there we are able to we safely skip forward to distill the next instinct: a
TransformerBlock is mainly an Consideration layer adopted by just a few
(fancy) dense layers, with some easy composition patterns (methods)
that assist with coaching. Consideration is the guts of the mannequin: it’s the
most fascinating, and likewise probably the most concerned.

RMSNorm(keras$layers$Layer) %py_class% {
  initialize <-
    operate(eps = 1e-6, ..., block_id = NULL, feeds_into = NULL) > self$wv()

  # rotate q,okay to inject place info.
  # cross q,okay to calculate an consideration rating for every token pair.
  scores <- rotate(q) %*% rotate(okay)   

  construct <- operate(input_shape) {
    # input_shape == (batch_size, seqlen, params$dim)
    # self$w will broadcast over batch_size and seqlen dims.
    # w_shape == (1, 1, params$dim)
    w_shape <- rep(1L, size(input_shape))
    w_shape[length(input_shape)] <- as.integer(input_shape) |> tail(1L)

    # outline a neighborhood operate that can load
    # the pretrained-weights if we provided `block_id` and `feeds_into`
    import_from(>
    layer_transformer_block(attn_head_size = params$dim %/% params$n_heads,
                            attn_n_heads = params$n_heads,
                            norm_eps = params$norm_eps,
                            block_id = block_id)
, block_id, feeds_into)
    initializer <-if (is.null(block_id))
      "ones"
      else if (block_id >=0) {
        (...) weights_path("7B/layers.{block_id}.{feeds_into}_norm.weight.npy") |>
               np$load() |> np$expand_dims(0:1)
      } else if(block_id == -1)
        # load weights for the ultimate output normalization layer, which isn't
        # a part of a TransformerBlock
        (...) weights_path("7B/norm.weight.npy") |>
               np$load() |> np$expand_dims(0:1)

    self$w <- self$add_weight(form = w_shape,
                              initializer = initializer,
                              trainable = TRUE)
  }

  rrms <- operate(x) {
    # reciprocal root imply sq. alongside the final axis
    x %>% # (batch_size, seqlen, n_features)
      tf$math$sq.() %>%
      tf$reduce_mean(axis = -1L, keepdims = TRUE) %>% # (batch_size, seqlen, 1)
      tf$math$add(self$eps) %>% # for numerical stability
      tf$math$rsqrt()
  }

  name <- operate(x) {
    x * self$rrms(x) * self$w
  }
}

norm <- RMSNorm()
m <- matrix(c(0, 1,
              2, 3), nrow = 2)
norm(m)

tf.Tensor(
[[0.         1.4142132 ]
 [0.44721353 1.3416406 ]], form=(2, 2), dtype=float32)

tf.Tensor(
[[0.         1.4142137 ]
 [0.44721362 1.3416408 ]], form=(2, 2), dtype=float32)

tf.Tensor(
[[0.        1.4142137]
 [0.4472136 1.3416408]], form=(2, 2), dtype=float32)

FeedForward(keras$layers$Layer) %py_class% {

  initialize <- operate(hidden_dim, multiple_of = 256L,
                         ..., block_id = NULL) {
    tremendous$initialize()

    if(!is.null(multiple_of)) {
      hidden_dim <- hidden_dim %>%
        { as.integer( . * (2/3)) } %>%
        { (. + multiple_of - 1) %/% multiple_of } %>%
        { . * multiple_of }
    }

    self$hidden_dim <- hidden_dim
    self$block_id <- block_id
  }

  construct <- operate(input_shape) {
    output_dim <- input_shape |> as.integer() |> tail(1)

    if(is.null(self$block_id))
      load_weight <- (...) NULL
    else
      load_weight <- (title) (...) np$load(weights_path(
        "7B/layers.{self$block_id}.feed_forward.{title}.weight.npy"))$`T`

    self$w1 <- Dense(self$hidden_dim, use_bias = FALSE,
                     kernel_initializer = load_weight("w1"))
    self$w2 <- Dense(output_dim, use_bias = FALSE,
                     kernel_initializer = load_weight("w2"))
    self$w3 <- Dense(self$hidden_dim, use_bias = FALSE,
                     kernel_initializer = load_weight("w3"))

    tremendous$construct(input_shape)
  }

  name <- operate(x) {
    import_from({self}, w1, w2, w3)
    import_from(tf$nn, silu)

    x %>%
      { silu(w1(.)) * w3(.) } %>% # SwiGLU
      w2()
  }

}

Shazeer (2020)
of SwiGLU and different variations on GLU is an exemplar of the kinds
of explorations and enhancements across the Transformer structure
since its preliminary publication in
2017; a gradual accretion of
enhancements that has introduced us to at this time. The Feedforward$name() is
only a single SwiGLU adopted by a linear projection. In its essence,
it’s a intelligent composition of three (discovered) linear projections, an
element-wise multiplication, and a silu()
activation
operate.

Maybe probably the most stunning statement to make right here is the relative
dearth of activation features, and even non-linearities, not simply in
FeedForward, however total. The silu() on this feedforward, the
reciprocal-root-mean-square in RMSnorm(), and a softmax() in
Consideration() are the one non-linear transformations in the entire
sequence of TransformerBlocks. Every part else is a linear
transformation!

`Consideration`

Lastly, let’s flip our consideration to Consideration().

Consideration(keras$layers$Layer) %py_class% {
  initialize <- operate(head_size, n_heads,
                         ..., block_id = NULL) {
    tremendous$initialize(...)

    self$head_size <- head_size
    self$n_heads <- n_heads

    if (is.null(block_id))
      load_weight <- operate(title) NULL
    else
      load_weight <- (title) (...) np$load(weights_path(
        "7B/layers.{block_id}.consideration.{title}.weight.npy"))$`T`

    Dense <- operate(title) keras$layers$Dense(
      models = n_heads * head_size,
      use_bias = FALSE,
      kernel_initializer = load_weight(title)
    )

    self$wq <- Dense("wq")
    self$wk <- Dense("wk")
    self$wv <- Dense("wv")
    self$wo <- Dense("wo")
  }

  name <- operate(x) {
    c(batch_size, seqlen, n_features) %<-% tf$unstack(tf$form(x))

    # 1. challenge (linear rework) x into
    #    question, key, and worth tensors
    # 2. reshape q okay v, splitting out the final dim (n_features)
    #    into n_heads impartial subspaces,
    #    every with dimension head_size.
    #    (n_features == head_size * n_heads)
    split_heads_shape <- c(batch_size, seqlen,
                           self$n_heads, self$head_size)
    q <- x |> self$wq() |> tf$reshape(split_heads_shape)
    okay <- x |> self$wk() |> tf$reshape(split_heads_shape)
    v <- x |> self$wv() |> tf$reshape(split_heads_shape)

    # embed positional info in question and key
    # (bsz, seqlen, n_heads, head_size)
    q %<>% apply_rotary_embedding()
    okay %<>% apply_rotary_embedding()

    # reshape:
    #   transfer heads out of the final 2 axes,
    #   so later matmuls are carried out throughout the subspaces (heads)
    #   between (seqlen, head_size) axes
    v <- tf$transpose(v, c(0L, 2L, 1L, 3L)) # (bsz, n_heads, seqlen, head_size)
    q <- tf$transpose(q, c(0L, 2L, 1L, 3L)) # (bsz, n_heads, seqlen, head_size)
    okay <- tf$transpose(okay, c(0L, 2L, 3L, 1L)) # (bsz, n_heads, head_size, seqlen)

    # calculate and normalize consideration scores
    scores <- q %*% okay                       # (bsz, n_heads, seqlen, seqlen)
    scores <- scores / sqrt(self$head_size) # scale

    # apply causal masks, so the mannequin cannot "look forward" throughout coaching
    masks <- make_mask(seqlen, dtype = scores$dtype)
    scores %<>% { . + masks }

    scores <- tf$nn$softmax(scores, axis = -1L)

    # regulate values tensor with consideration scores
                      # scores (bsz, n_heads, seqlen, seqlen)
                      # v      (bsz, n_heads, seqlen, head_size)
    output <- scores %*% v   # (bsz, n_heads, seqlen, head_size)

    # mix heads again right into a single options dim,
    # so Consideration output_shape==input_shape
    output <- output |>
      tf$transpose(c(0L, 2L, 1L, 3L)) |> # (bsz, seqlen, n_heads, head_size)
      tf$reshape(tf$form(x))            # (bsz, seqlen, n_heads * head_size)

    # yet one more trainable linear projection for good luck
    output <- self$wo(output) # (bsz, seqlen, n_heads * head_size)

    output
  }
}

authentic Transformers
paper (and obtainable as a keras
builtin underneath keras$layers$MultiHeadAttention()). The core novelty is
the addition of the apply_rotary_embedding() operate, which we’ll
describe shortly. The extra novelty is balanced by the simplicity
from the truth that the layer is performing self-attention—we don’t want
to move in numerous question, key, and worth tensors (or motive about what
which means), for the reason that similar enter serves all three roles. Notice that the
typical MultiHeadAttention() layer is roofed fairly completely in
the 2nd Version of Deep Studying with R,
together with a full implementation of consideration in base R.

To develop an understanding of the mechanics in a layer like this, it’s
useful to briefly unsee a few of the minutia that may act as a fog
obscuring the essence of the operation. On this occasion, if we
briefly strip out the transpose()s and reshape()s (as intelligent and
important as they’re), that is what’s left:

name <- operate(x) >  normalize_scores()

  # regulate the third projection with the eye scores
  output <- scores %*% v

  self$wo(output) # yet one more discovered linear projection for good luck

Returning to the transpose()s and reshapes(), you may observe that
their goal is to make it in order that the eye calculations are
carried out throughout n_heads impartial subspaces, moderately than in a
single bigger house. The identical reasoning drives this determination as that
driving utilization of depthwise-separable convolutions in picture fashions.
Empirically, for the fastened compute finances, factoring options into
impartial subspaces performs higher than doing the identical core
operations in single bigger function house. As with all issues, there may be
a stability to strike between n_heads (the variety of subspaces) and
head_dim (the dimensions of every subspace). The LLaMA authors have struck
the stability like this on the numerous mannequin sizes:

lapply(c("7B", "13B", "30B", "65B"), (dimension) {
  p <- read_json(weights_path("{dimension}/params.json"))
  with(p, listing(llama_size = dimension,
               n_heads = n_heads,
               head_dim = dim %/% n_heads))
}) |> dplyr::bind_rows()

# A tibble: 4 × 3
  llama_size n_heads head_dim
              
1 7B              32      128
2 13B             40      128
3 30B             52      128
4 65B             64      128

make_mask <- operate(seqlen, dtype = k_floatx()) {
  x <- tf$vary(seqlen)
  masks <- tf$the place(x[, tf$newaxis] < x[tf$newaxis, ],
                   tf$fixed(-Inf, dtype = dtype),
                   tf$fixed(0, dtype = dtype))

  # broadcast over batch and heads dim
  masks[tf$newaxis, tf$newaxis, , ] # (1, 1, seqlen, seqlen)
}

The masks is a strictly higher triangular matrix crammed with -Inf
values. Including the masks to the eye scores prevents the mannequin from
having the ability to “look forward” and see the eye rating for a token
pairing it hasn’t seen but at a selected place within the sequence.
This want for a masks is finest regarded as a vestige from coaching,
an equipment that the mannequin wanted to be taught with and now it could actually’t operate with out.
Throughout coaching, gradients are calculated for predictions from all
token positions in a sequence, together with predictions tokens the place the right
reply is proper there, because the very subsequent token in similar sequence. The masks
prevents the mannequin from having the ability to cheat and look forward into the longer term,
one thing it gained’t have the ability to do as soon as it’s we’re working it for inference.

tf.Tensor(
[[[[  0. -inf -inf -inf -inf]
   [  0.   0. -inf -inf -inf]
   [  0.   0.   0. -inf -inf]
   [  0.   0.   0.   0. -inf]
   [  0.   0.   0.   0.   0.]]]], form=(1, 1, 5, 5), dtype=float32)

Su et al. (2022) within the paper titled
“RoFormer: Enhanced Transformer with Rotary Place Embedding”.

Some context:

The naked Consideration() mechanism doesn’t go away any risk for a
token’s place in a sequence to have an effect on the eye scores, since
solely token-pairs are scored. Consideration treats its enter like a
bag-of-tokens.
The place of a token in a sequence is clearly essential, and the
consideration layer ought to have entry to that info.
Absolutely the place of a token in a sequence is much less essential
than the relative place between tokens. (Particularly so for lengthy
sequences).

Which leads us into the complicated airplane. If we think about the options as
complicated numbers, we are able to rotate them, and we are able to calculate angles between
them. From the Roformers paper:

Particularly, incorporating the relative place embedding is
simple: merely rotate the affine-transformed phrase embedding
vector by quantity of angle multiples of its place index and thus
interprets the instinct behind Rotary Place Embedding

Increasing barely: the rotation matrix is designed in order that
subsequently, after rotating our q and okay token sequence embedding
the identical means, the angle between token options is a operate of the
relative distance between these tokens within the token sequence. The
relative angle between two tokens is invariant to absolutely the
place of these tokens within the full sequence.

Briefly, the rotation injects positional info. The which means or
interpretability of that positional info, or how it’s meant to
be used, and even extracted from the results of q %*% okay, is left to the
mannequin to be taught.

Right here is the code:

apply_rotary_embedding <- operate(x) {
  c(., seqlen, ., head_size) %<-%
    tf$unstack(tf$form(x))

  rotation_matrix <- compute_rotation_matrix(seqlen, head_size)

  x %>%
    view_as_complex() %>%
    { . * rotation_matrix } %>%
    view_as_real()

}

compute_rotation_matrix <-
  operate(seqlen, feature_dim, theta = 10000) {
    # `feature_dim` right here goes to be consideration$head_size
    # `seqlen` goes to match the token sequence size.

    t <- tf$vary(seqlen, dtype = tf$float32)
    freqs <- tf$vary(begin = 0, restrict = 1, delta = 1 / (feature_dim %/% 2),
                      dtype = tf$float32)
    tf_assert(tf$dimension(freqs) == feature_dim %/% 2)
    freqs <- 1.0 / (theta ^ freqs)

    # outer product; (seqlen, head_size/2)
    freqs <- tf$einsum('a,b->ab', t, freqs)

    rot_mat <- tf$complicated(tf$cos(freqs), tf$sin(freqs))

    # the positional embedding will probably be broadcast throughout batch and heads dim
    rot_mat[tf$newaxis, , tf$newaxis, ] #(1, seqlen, 1, headdim/2)
  }

view_as_complex <- operate(x) {
  tf$complicated(x[all_dims(), `::2`],
             x[all_dims(), `2::2`])
}

view_as_real <- operate(x) {
  # xs = (..., f);  xs2 = (..., f*2)
  xs <- tf$form(x)
  xs2 <- tf$concat(listing(xs[1:(length(xs)-1)],
                        xs[length(xs), drop = FALSE] * 2L),
                   axis = 0L)

  x2 <- tf$stack(listing(Re(x), Im(x)), axis = -1L)

  # (..., f, 2) -> (..., f*2)
  tf$reshape(x2, xs2)
}

As you may see, to think about the embedding options as present within the
complicated airplane, we merely deal with adjoining pairs of floats within the
underlying array as the true and imaginary a part of a fancy quantity. We
rotate the embeddings within the complicated airplane, then return to imagining
the options as present in the true airplane. Once more, the job of
decoding the which means of the options after rotation is left to the
mannequin to be taught.

close to <- operate (x, y, tol = 1e-6) abs(x - y) < tol
all(close to(1, Mod(compute_rotation_matrix(2048L, 128L))))

tf.Tensor(True, form=(), dtype=bool)

There may be yet one more trick to watch earlier than shifting on: due to a few of
the mathematical properties of the rotation matrix, it’s attainable to
keep away from doing a full complicated multiply operation and nonetheless arrive on the
similar consequence. Additionally, for the reason that rotation matrix by no means modifications, it makes
sense to solely compute it as soon as and cache it, like so:

precomputed_rotation_matrix <- compute_rotation_matrix(
  seqlen = 2048L, # LLaMA max seqlen
  feature_dim = with(params, dim %/% n_heads)  # head_size
)

apply_rotary_embedding_faster <- operate(x) {

  rotate_every_two <- operate(x) {
    x1 <- x[all_dims(), `::2`]
    x2 <- x[all_dims(), `2::2`]
    x_ <- tf$stack(listing(-x2, x1), axis = -1L)
    tf$reshape(x_, tf$form(x))
  }

  repeat_each_twice <- operate(x) {
    tf$`repeat`(x, 2L, axis = -1L)
  }

  seqlen <- tf$form(x)[2]
  rot <- precomputed_rotation_matrix[, NA:seqlen, , ]

  cos <- Re(rot) |> repeat_each_twice()
  sin <- Im(rot) |> repeat_each_twice()

  (x * cos) + (rotate_every_two(x) * sin)
}

rand <- tf$random$uniform(form(3, 8, params$n_heads, 128))
all(apply_rotary_embedding(rand) ==
    apply_rotary_embedding_faster(rand))

tf.Tensor(True, form=(), dtype=bool)

apply_rotary_embedding <- apply_rotary_embedding_faster

Lastly, word that the rotary positional embeddings are utilized inside
every Consideration layer. That is completely different from the unique Transformer
implementation, the place a positional embedding was solely added as soon as on the
head of the mannequin. Much like residual connections, you may consider the
presence of those repeated injections of positional info as
relieving the remaining trainable layers from the burden of allocating
a few of their weights to the duty of “passing by way of” or “preserving”
the positional info for later layers.

Falbel and Keydana 2023),
so time spent understanding them higher is time nicely
spent. For the needs of this weblog publish we’ve lined the factors
wanted and we’ll transfer on to tying all items collectively. To go deeper and
develop a extra mathematically knowledgeable perceive of RoPE, two glorious
beginning factors are:

The unique paper by Su et al. (2022)
This weblog publish by
Biderman et al. (2021)

Tying all of it collectively

With Tokenizer, Embedding, TransformerBlock (RMSNorm,
Consideration FeedForward and apply_rotary_embedding) all lined,
it’s time to tie all of the items collectively right into a Transformer mannequin. We
may do that utilizing %py_class% like with the opposite layers above, however
it’s simply as simple to maneuver over to utilizing the Keras useful API at this
level.

layer_transformer_block <- create_layer_wrapper(TransformerBlock)
layer_rms_norm <- create_layer_wrapper(RMSNorm)

# enter to the mannequin will probably be output from the tokenizer
enter <- layer_input(form(NA)) #, dtype = "int32")

x <- enter |>
  tok_embeddings()  # instantiated earlier within the blog-post

for(block_id in seq_len0(params$n_layers)) >
    layer_transformer_block(attn_head_size = params$dim %/% params$n_heads,
                            attn_n_heads = params$n_heads,
                            norm_eps = params$norm_eps,
                            block_id = block_id)


# last output projection into logits of output tokens
x <- x |>
  layer_rms_norm(block_id = -1, eps = params$norm_eps) |>
  layer_dense(
    tokenizer$vocab_size(), use_bias = FALSE,
    kernel_initializer = (...) np$load(weights_path("7B/output.weight.npy"))$`T`
  )

# slice out the logits for the final token
with_options(c(tensorflow.extract.warn_negatives_pythonic = FALSE), {
  output <- x[, -1, ]
})

llama <- keras_model(enter, output) %>%
  compile(jit_compile = TRUE)

next_token_probs <- immediate %>%
  tokenizer$tokenize() %>%
  llama()

next_token_probs

tf.Tensor(
[[-2.4503722e+00 -3.4463339e+00  1.3200411e+01 ...  4.8804146e-01
  -1.3277926e+00  9.9985600e-03]], form=(1, 32000), dtype=float32)

Deep Studying with
R ebook), however this weblog publish is lengthy sufficient
already. So for now, let’s simply take the argmax().

sampler <- (logits) tf$argmax(logits, axis = -1L, output_type = "int32")

(next_token <- sampler(next_token_probs))

tf.Tensor([304], form=(1), dtype=int32)

tokenizer$detokenize(next_token) |> as.character()

[1] "to"

prompt_tokens <- tokenizer$tokenize("One of the simplest ways to draw bees")

for (i in 1:20) {

  next_token_probs <- prompt_tokens |> llama()
  next_token <- sampler(next_token_probs)

  prompt_tokens %<>% { tf$concat(c(., next_token), axis = -1L) }

  # finish of sentence
  if (as.logical(next_token == tokenizer$string_to_id(".")))
    break
}

prompt_tokens |>
  tokenizer$detokenize() |>
  as.character() |>
  strwrap(60) |> writeLines()

One of the simplest ways to draw bees to your backyard is to plant a
number of flowers that bloom at completely different instances.

On this weblog publish we’ve walked by way of the LLaMA structure
carried out in R TensorFlow, together with the right way to load pretrained weights,
after which run the mannequin to generate a sentence. Notice, a lot of the code in
this weblog publish is tailor-made for didactic functions. Whereas the
implementation of the LLaMA structure lined on this weblog publish is
acceptable for coaching, there are just a few modifications you’ll wish to
make earlier than doing lots of textual content technology. These embody issues like:

The modifications required to implement these optimizations for inference
balloon the code dimension and are principally about book-keeping, so we gained’t go
by way of them on this weblog publish. Nonetheless, yow will discover a fuller
implementation of LLaMA in R Tensorflow, together with a cache-aware
generate() technique that solely feeds the mannequin one token at a time throughout
the primary inference loop, (and compiles to XLA!),
right here.

That’s all for now. Thanks for studying and glad travels to all
exploring this thrilling LLM terrain!

Photograph by Sébastien Goldberg on Unsplash

Biderman, Stella, Sid Black, Charles Foster, Leo Gao, Eric Hallahan, Horace He, Ben Wang, and Phil Wang. 2021. “Rotary Embeddings: A Relative Revolution.” weblog.eleuther.ai/rotary-embeddings/.

Falbel, Daniel, and Sigrid Keydana. 2023. “Posit AI Weblog: De-Noising Diffusion with Torch.”

Hoffmann, Jordan, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, et al. 2022. “Coaching Compute-Optimum Giant Language Fashions.” https://arxiv.org/abs/2203.15556.

Shazeer, Noam. 2020. “GLU Variants Enhance Transformer.” https://arxiv.org/abs/2002.05202.

Su, Jianlin, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. 2022. “RoFormer: Enhanced Transformer with Rotary Place Embedding.” https://arxiv.org/abs/2104.09864.

Touvron, Hugo, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, et al. 2023. “LLaMA: Open and Environment friendly Basis Language Fashions.” https://doi.org/10.48550/ARXIV.2302.13971.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. “Consideration Is All You Want.” https://arxiv.org/abs/1706.03762.

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