Inspectus is a versatile visualization tool for large language models. It runs smoothly in Jupyter notebooks via an easy-to-use Python API. Inspectus provides multiple views, offering diverse insights into language model behaviors.
Click a token to select it and deselect others. Clicking again will select all again. To change the state of only one token, do shift+click
Attention Matrix: Visualizes the attention scores between tokens, highlighting how each token focuses on others during processing.
Query Token Heatmap: Shows the sum of attention scores between each query and selected key tokens
Key Token Heatmap: Shows the sum of attention scores between each key and selected query tokens
Dimension Heatmap: Shows the sum of attention scores for each item in dimensions (Layers and Heads) normalized over the dimension.
pip install inspectusImport the library
import inspectusSimple usage
# attn: Attention map; a 2-4D tensor or attention maps from Huggingface transformers
inspectus.attention(attn, tokens)For different query and key tokens
inspectus.attention(attns, query_tokens, key_tokens)For detailed API documentation, please refer to the official documentation - wip.
from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig
import torch
import inspectus
# Initialize the tokenizer and model
context_length = 128
tokenizer = AutoTokenizer.from_pretrained("huggingface-course/code-search-net-tokenizer")
config = AutoConfig.from_pretrained(
"gpt2",
vocab_size=len(tokenizer),
n_ctx=context_length,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
model = GPT2LMHeadModel(config)
# Tokenize the input text
text= 'The quick brown fox jumps over the lazy dog'
tokenized = tokenizer(
text,
return_tensors='pt',
return_offsets_mapping=True
)
input_ids = tokenized['input_ids']
tokens = [text[s: e] for s, e in tokenized['offset_mapping'][0]]
with torch.no_grad():
res = model(input_ids=input_ids.to(model.device), output_attentions=True)
# Visualize the attention maps using the Inspectus library
inspectus.attention(res['attentions'], tokens)Check out the notebook here: Huggingface Tutorial
import numpy as np
import inspectus
# 2D attention representing attention values between Query and Key tokens
attn = np.random.rand(3, 3)
# Visualize the attention values using the Inspectus library
# The first argument is the attention matrix
# The second argument is the list of query tokens
# The third argument is the list of key tokens
inspectus.attention(arr, ['a', 'b', 'c'], ['d', 'e', 'f'])Check out the notebook here: Custom attention map tutorial
If you use Inspectus for academic research, please cite the library using the following BibTeX entry.
@misc{inspectus,
author = {Varuna Jayasiri, Lakshith Nishshanke},
title = {inspectus: A visualization and analytics tool for large language models},
year = {2024},
url = {https://github.com/labmlai/inspectus},
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent