tfpyth 1.0

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

TfPyTh

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

A light-weight adapter library to make things interact that were not supposed to interact initially.

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 haz

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.

TODOs

• [] support JAX
• [] support higher-order derivatives