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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))
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