LLM on Android with Keras and TensorFlow Lite

Using GPT-2, we used Keras to build a large language model to run on an Android device using TensorFlow Lite serving

Transcript

1. LLM on Android with Keras and TensorFlow Lite Cloud Kampala
2. What are LLMs? Large language models (LLMs) are ML models trained on a large text data to generate outputs for various NLP tasks, such as text generation, question answering, and machine translation. LLMs are just deep learning neural networks, based on the Transformer architecture invented by Google researchers in 2017. Ex: Google LaMDA, PaLM, GPT-2.
3. What is KerasNLP? KerasNLP is a natural language processing library that supports users through the entire development cycle of building language models. KerasNLP comes with pre-trained models such as GPT-2 and is supported in the TensorFlow ecosystem for deployment to mobile devices with TensorFlow Lite
4. The work plan… - Model building - Model conversion - Quantization - Android App integration
5. Model building… • KerasNLP • GPT-2 Cloud Kampala
7. gpt2_tokenizer = 
 keras_nlp.models.GPT2Tokenizer.from_preset("gpt2_base_en")
 
 gpt2_preprocessor = 
 keras_nlp.models.GPT2CausalLMPreprocessor.from_preset(
 "gpt2_base_en",
 sequence_length=256,
 add_end_token=True,
 )
 
 gpt2_lm = 
 keras_nlp.models.GPT2CausalLM.from_preset(
 "gpt2_base_en",
 preprocessor=gpt2_preprocessor
 )

8. Model conversion… • Keras -> TFLite Cloud Kampala
10. @tf.function
 def generate(prompt, max_length):
 
 return gpt2_lm.generate(prompt, max_length)
 
 concrete_func = generate.get_concrete_function(tf.TensorSpec([], tf.string), 100)

11. Note: You can also use from_keras_model() from TFLiteConverter in order to perform the conversion.
12. def run_inference(input, generate_tflite):
 interp = interpreter.InterpreterWithCustomOps(
 model_content=generate_tflite,
 custom_op_registerers=
 tf_text.tflite_registrar.SELECT_TFTEXT_OPS
 )
 
 interp.get_signature_list()
 
 generator = interp.get_signature_runner('serving_default')
 output = generator(prompt=np.array([input]))

13. Note: TF text ops are not built-in ops in the TFLite runtime We need to add these custom ops in order for the interpreter to make inference on this model. The helper function accepts an input and a function that performs the conversion, namely the generator() function defined above.
14. gpt2_lm.jit_compile = False
 converter = tf.lite.TFLiteConverter.from_concrete_functions(
 [concrete_func],
 gpt2_lm)
 
 """
 Code…
 """
 
 converter._experimental_guarantee_all_funcs_one_use = True
 generate_tflite = converter.convert()
 run_inference("I'm at DevFest Kampala", generate_tflite)

15. Quantization… • Dynamic range • FP16 • Full integer Cloud Kampala
17. gpt2_lm.jit_compile = False
 converter = tf.lite.TFLiteConverter.from_concrete_functions(
 [concrete_func],
 gpt2_lm)
 
 """
 Code…
 """
 
 converter._experimental_guarantee_all_funcs_one_use = True
 quant_generate_tflite = converter.convert()
 run_inference("I'm at DevFest Kampala", quant_generate_tflite)
 

18. Android integration… • Android Studio 2022.2.1 or above • Android device/emulator Cloud Kampala
19. Let’s see this in play… Cloud Kampala
20. Resources Because these slides clearly aren’t enough to become an ‘LLM Pro’ - Colab Notebook - Codelab - TensorFlow tutorial
21. Cloud Kampala Thank you! Any questions? Wesley Kambale @weskambale kambale.dev