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Automatic differentiation and generation of Torch/Tensorflow operations with pystencils (

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PyPI version Documentation Status===================



Install via pip :

pip install pystencils-autodiff

or if you downloaded this repository using:

pip install -e .


Create a pystencils.AssignmentCollection with pystencils:

import sympy
import pystencils

z, x, y = pystencils.fields("z, y, x: [20,30]")

forward_assignments = pystencils.AssignmentCollection({
    z[0, 0]: x[0, 0] * sympy.log(x[0, 0] * y[0, 0])

Main Assignments:
     z[0,0]  y_C*log(x_C*y_C)

You can then obtain the corresponding backward assignments:

from pystencils.autodiff import AutoDiffOp, create_backward_assignments
backward_assignments = create_backward_assignments(forward_assignments)


You can see the derivatives with respective to the two inputs multiplied by the gradient diffz_C of the output z_C.

Main Assignments:
    \hat{y}[0,0]  diffz_C*(log(x_C*y_C) + 1)
    \hat{x}[0,0]  diffz_C*y_C/x_C

You can also use the class AutoDiffOp to obtain both the assignments (if you are curious) and auto-differentiable operations for Tensorflow…

op = AutoDiffOp(forward_assignments)
backward_assignments = op.backward_assignments

x_tensor = pystencils.autodiff.tf_variable_from_field(x)
y_tensor = pystencils.autodiff.tf_variable_from_field(y)
tensorflow_op = op.create_tensorflow_op({x: x_tensor, y: y_tensor}, backend='tensorflow')

… or Torch:

x_tensor = pystencils.autodiff.torch_tensor_from_field(x, cuda=False, requires_grad=True)
y_tensor = pystencils.autodiff.torch_tensor_from_field(y, cuda=False, requires_grad=True)

z_tensor = op.create_tensorflow_op({x: x_tensor, y: y_tensor}, backend='torch')

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