streamlit_app.py

import streamlit as st
import jax.numpy as jnp
from transformers import AutoTokenizer
from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
from t5_vae_flax.src.t5_vae import FlaxT5VaeForAutoencoding


st.title('T5-VAE')
st.text('''
Try interpolating between lines of Python code using this T5-VAE.
''')


@st.cache(allow_output_mutation=True)
def get_model():
    tokenizer = AutoTokenizer.from_pretrained("t5-base")
    model = FlaxT5VaeForAutoencoding.from_pretrained("flax-community/t5-vae-python")
    assert model.params['t5']['shared']['embedding'].shape[0] == len(tokenizer), "T5 Tokenizer doesn't match T5Vae embedding size."
    return model, tokenizer


model, tokenizer = get_model()


def add_decoder_input_ids(examples):
    arr_input_ids = jnp.array(examples["input_ids"])
    pad = tokenizer.pad_token_id * jnp.ones((arr_input_ids.shape[0], 1), dtype=jnp.int32)
    arr_pad_input_ids = jnp.concatenate((arr_input_ids, pad), axis=1)
    examples['decoder_input_ids'] = shift_tokens_right(arr_pad_input_ids, tokenizer.pad_token_id, model.config.decoder_start_token_id)

    arr_attention_mask = jnp.array(examples['attention_mask'])
    ones = jnp.ones((arr_attention_mask.shape[0], 1), dtype=jnp.int32)
    examples['decoder_attention_mask'] = jnp.concatenate((ones, arr_attention_mask), axis=1)

    for k in ['decoder_input_ids', 'decoder_attention_mask']:
        examples[k] = examples[k].tolist()

    return examples


def prepare_inputs(inputs):
    for k, v in inputs.items():
        inputs[k] = jnp.array(v)
    return add_decoder_input_ids(inputs)


def get_latent(text):
    return model(**prepare_inputs(tokenizer([text]))).latent_codes[0]


def tokens_from_latent(latent_codes):
    model.config.is_encoder_decoder = True
    output_ids = model.generate(
        latent_codes=jnp.array([latent_codes]),
        bos_token_id=model.config.decoder_start_token_id,
        min_length=1,
        max_length=32,
    )
    return output_ids


def slerp(ratio, t1, t2):
    '''
        Perform a spherical interpolation between 2 vectors.
        Most of the volume of a high-dimensional orange is in the skin, not the pulp.
        This also applies for multivariate Gaussian distributions.
        To that end we can interpolate between samples by following the surface of a n-dimensional sphere rather than a straight line.

        Args:
            ratio: Interpolation ratio.
            t1: Tensor1
            t2: Tensor2
    '''
    low_norm = t1 / jnp.linalg.norm(t1, axis=1, keepdims=True)
    high_norm = t2 / jnp.linalg.norm(t2, axis=1, keepdims=True)
    omega = jnp.arccos((low_norm * high_norm).sum(1))
    so = jnp.sin(omega)
    res = (jnp.sin((1.0 - ratio) * omega) / so)[0] * t1 + (jnp.sin(ratio * omega) / so)[0] * t2
    return res


def decode(ratio, txt_1, txt_2):
    if not txt_1 or not txt_2:
        return ''
    lt_1, lt_2 = get_latent(txt_1), get_latent(txt_2)
    lt_new = slerp(ratio, lt_1, lt_2)
    tkns = tokens_from_latent(lt_new)
    return tokenizer.decode(tkns.sequences[0], skip_special_tokens=True)


# TODO while loop here?
st.text_input("x = 3",          key="in_1")
st.text_input("y += 'hello'",   key="in_2")
r = st.slider('Interpolation Ratio')
st.write(decode(r, st.session_state.in_1, st.session_state.in_2))