Text generation models have become a cornerstone of modern NLP, powering everything from summarization to translation. But when using TensorFlow, generation could be painfully slow. A new integration with XLA (Accelerated Linear Algebra) changes that, delivering up to 100x speedups—and in some cases, outperforming PyTorch.
Text Generation Basics
The 🤗 transformers library makes text generation accessible with the generate() function. By default, it uses greedy decoding (deterministic), but you can enable sampling for more creative outputs:
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = TFAutoModelForCausalLM.from_pretrained("gpt2")
model.config.pad_token_id = model.config.eos_token_id
inputs = tokenizer(["TensorFlow is"], return_tensors="tf")
generated = model.generate(**inputs, do_sample=True, seed=(42, 0))
print("Sampling output: ", tokenizer.decode(generated[0]))
# > Sampling output: TensorFlow is a great learning platform for learning about
# data structure and structure in data science..
You can control output length with max_new_tokens, and adjust randomness via temperature. For higher quality results, beam search (num_beams) explores multiple candidate sequences:
generated = model.generate(**inputs, num_beams=2)
print("Beam Search output:", tokenizer.decode(generated[0]))
# > Beam Search output: TensorFlow is an open-source, open-source,
# distributed-source application framework for the
Enter XLA: The Speed Boost
XLA is a compiler originally built for TensorFlow (and now used by JAX). It optimizes computation graphs, reducing overhead and enabling faster execution. By wrapping the generate() call with @tf.function(jit_compile=True), you can activate XLA compilation with a single line.
The results are dramatic: benchmarks show up to 100x speed improvements over standard TensorFlow, and in many cases, TensorFlow+XLA outpaces PyTorch—especially for larger models and longer sequences.
Benchmarks & Practical Impact
Tests on GPT-2 and other models confirm the gain is real. On a typical GPU, generation time drops from seconds to milliseconds. This makes TensorFlow a viable choice for real-time applications like chatbots and interactive writing assistants.
To try it yourself, check out the interactive Colab notebook.
With XLA, TensorFlow becomes a powerhouse for text generation—fast, efficient, and easy to use.