# Decoding Methods in Massive Language Fashions

The tokenizer, Byte-Pair Encoding on this occasion, interprets every token within the enter textual content right into a corresponding token ID. Then, GPT-2 makes use of these token IDs as enter and tries to foretell the subsequent most definitely token. Lastly, the mannequin generates logits, that are transformed into possibilities utilizing a softmax perform.

For instance, the mannequin assigns a chance of 17% to the token for “of” being the subsequent token after “I’ve a dream”. This output basically represents a ranked checklist of potential subsequent tokens within the sequence. Extra formally, we denote this chance as *P(of | I’ve a dream) = 17%*.

Autoregressive fashions like GPT predict the subsequent token in a sequence primarily based on the previous tokens. Take into account a sequence of tokens *w = (w*₁*, w*₂*, …, w*ₜ*)*. The joint chance of this sequence *P(w)* may be damaged down as:

For every token *wᵢ* within the sequence, *P(wᵢ | w₁, w₂, …, wᵢ₋₁)* represents the conditional chance of *wᵢ* given all of the previous tokens (*w₁, w₂, …, wᵢ₋₁*). GPT-2 calculates this conditional chance for every of the 50,257 tokens in its vocabulary.

This results in the query: how can we use these possibilities to generate textual content? That is the place decoding methods, similar to grasping search and beam search, come into play.

Grasping search is a decoding methodology that takes essentially the most possible token at every step as the subsequent token within the sequence. To place it merely, it solely retains the most definitely token at every stage, discarding all different potential choices. Utilizing our instance:

**Step 1**: Enter: “I’ve a dream” → Most definitely token: “ of”**Step 2**: Enter: “I’ve a dream of” → Most definitely token: “ being”**Step 3**: Enter: “I’ve a dream of being” → Most definitely token: “ a”**Step 4**: Enter: “I’ve a dream of being a” → Most definitely token: “ physician”**Step 5**: Enter: “I’ve a dream of being a health care provider” → Most definitely token: “.”

Whereas this strategy may sound intuitive, it’s essential to notice that the grasping search is short-sighted: it solely considers essentially the most possible token at every step with out contemplating the general impact on the sequence. This property makes it quick and environment friendly because it doesn’t must maintain monitor of a number of sequences, but it surely additionally signifies that it could actually miss out on higher sequences that may have appeared with barely much less possible subsequent tokens.

Subsequent, let’s illustrate the grasping search implementation utilizing graphviz and networkx. We choose the ID with the best rating, compute its log chance (we take the log to simplify calculations), and add it to the tree. We’ll repeat this course of for 5 tokens.

`import matplotlib.pyplot as plt`

import networkx as nx

import numpy as np

import timedef get_log_prob(logits, token_id):

# Compute the softmax of the logits

possibilities = torch.nn.useful.softmax(logits, dim=-1)

log_probabilities = torch.log(possibilities)

# Get the log chance of the token

token_log_probability = log_probabilities[token_id].merchandise()

return token_log_probability

def greedy_search(input_ids, node, size=5):

if size == 0:

return input_ids

outputs = mannequin(input_ids)

predictions = outputs.logits

# Get the anticipated subsequent sub-word (right here we use top-k search)

logits = predictions[0, -1, :]

token_id = torch.argmax(logits).unsqueeze(0)

# Compute the rating of the anticipated token

token_score = get_log_prob(logits, token_id)

# Add the anticipated token to the checklist of enter ids

new_input_ids = torch.cat([input_ids, token_id.unsqueeze(0)], dim=-1)

# Add node and edge to graph

next_token = tokenizer.decode(token_id, skip_special_tokens=True)

current_node = checklist(graph.successors(node))[0]

graph.nodes[current_node]['tokenscore'] = np.exp(token_score) * 100

graph.nodes[current_node]['token'] = next_token + f"_{size}"

# Recursive name

input_ids = greedy_search(new_input_ids, current_node, length-1)

return input_ids

# Parameters

size = 5

beams = 1

# Create a balanced tree with top 'size'

graph = nx.balanced_tree(1, size, create_using=nx.DiGraph())

# Add 'tokenscore', 'cumscore', and 'token' attributes to every node

for node in graph.nodes:

graph.nodes[node]['tokenscore'] = 100

graph.nodes[node]['token'] = textual content

# Begin producing textual content

output_ids = greedy_search(input_ids, 0, size=size)

output = tokenizer.decode(output_ids.squeeze().tolist(), skip_special_tokens=True)

print(f"Generated textual content: {output}")

`Generated textual content: I've a dream of being a health care provider.`

Our grasping search generates the identical textual content because the one from the transformers library: “I’ve a dream of being a health care provider.” Let’s visualize the tree we created.

`import matplotlib.pyplot as plt`

import networkx as nx

import matplotlib.colours as mcolors

from matplotlib.colours import LinearSegmentedColormapdef plot_graph(graph, size, beams, rating):

fig, ax = plt.subplots(figsize=(3+1.2*beams**size, max(5, 2+size)), dpi=300, facecolor='white')

# Create positions for every node

pos = nx.nx_agraph.graphviz_layout(graph, prog="dot")

# Normalize the colours alongside the vary of token scores

if rating == 'token':

scores = [data['tokenscore'] for _, information in graph.nodes(information=True) if information['token'] shouldn't be None]

elif rating == 'sequence':

scores = [data['sequencescore'] for _, information in graph.nodes(information=True) if information['token'] shouldn't be None]

vmin = min(scores)

vmax = max(scores)

norm = mcolors.Normalize(vmin=vmin, vmax=vmax)

cmap = LinearSegmentedColormap.from_list('rg', ["r", "y", "g"], N=256)

# Draw the nodes

nx.draw_networkx_nodes(graph, pos, node_size=2000, node_shape='o', alpha=1, linewidths=4,

node_color=scores, cmap=cmap)

# Draw the perimeters

nx.draw_networkx_edges(graph, pos)

# Draw the labels

if rating == 'token':

labels = {node: information['token'].break up('_')[0] + f"n{information['tokenscore']:.2f}%" for node, information in graph.nodes(information=True) if information['token'] shouldn't be None}

elif rating == 'sequence':

labels = {node: information['token'].break up('_')[0] + f"n{information['sequencescore']:.2f}" for node, information in graph.nodes(information=True) if information['token'] shouldn't be None}

nx.draw_networkx_labels(graph, pos, labels=labels, font_size=10)

plt.field(False)

# Add a colorbar

sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)

sm.set_array([])

if rating == 'token':

fig.colorbar(sm, ax=ax, orientation='vertical', pad=0, label='Token chance (%)')

elif rating == 'sequence':

fig.colorbar(sm, ax=ax, orientation='vertical', pad=0, label='Sequence rating')

plt.present()

# Plot graph

plot_graph(graph, size, 1.5, 'token')