TfPyTh

Putting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters).

A light-weight differentiable adapter library to make TensorFlow and PyTorch interact.

Install

pip install tfpyth

Example

import tensorflow as tf
import torch as th
import numpy as np
import tfpyth

session = tf.Session()

def get_torch_function():
    a = tf.placeholder(tf.float32, name='a')
    b = tf.placeholder(tf.float32, name='b')
    c = 3 * a + 4 * b * b

    f = tfpyth.torch_from_tensorflow(session, [a, b], c).apply
    return f

f = get_torch_function()
a = th.tensor(1, dtype=th.float32, requires_grad=True)
b = th.tensor(3, dtype=th.float32, requires_grad=True)
x = f(a, b)

assert x == 39.

x.backward()

assert np.allclose((a.grad, b.grad), (3., 24.))

What it's got

torch_from_tensorflow

Creates a PyTorch function that is differentiable by evaluating a TensorFlow output tensor given input placeholders.

eager_tensorflow_from_torch

Creates an eager Tensorflow function from a PyTorch function.

tensorflow_from_torch

Creates a TensorFlow op/tensor from a PyTorch function.

Future work

• support JAX
• support higher-order derivatives