public void Backward(FloatTensor grad = null, FloatTensor grad_origin = null)
{
//Debug.Log("Backward:" + this.id + " creation_op:" + creation_op);
if (autograd)
{
if (grad == null)
{
Debug.Log("Grad not Found... Creating Gradient of 1s");
grad = this.createOnesTensorLike();
grad.Autograd = false;
}
if (grad_origin != null)
{
int child_index = children_indices.IndexOf(grad_origin.Id);
if (children_counts[child_index] > 0)
{
throw new InvalidOperationException("Can't backprop more than once.");
}
else
{
children_counts[child_index] += 1;
}
}
if (this.Grad == null)
{
this.Grad = grad;
//Debug.Log("Setting Grad Tensor Id:" + this.id);
}
else
{
if (this.Grad.id == grad.id)
{
// do nothing
//Debug.Log("Not Updating For Tensor Id:" + this.id);
}
else
{
//Debug.Log("Updating For Tensor Id:" + this.id);
//this.Grad.Zero_();
this.Grad.Add(grad, inline: true);
}
}
// grads must not have grads of their own
if (this.Grad.autograd == true)
{
throw new InvalidOperationException("Sorry, grads cannot have grads");
}
// RULES FOR AUTOGRAD:
// 1) if you need to use "this" for calculating a gradient, copy it first and set autograd to false (see sigmoid)
// 2) if you use a method in your backprop logic that doesn't hook into the dynamic graph yet, backprop
// will not work!!! Make sure there's a "hookautograd" function in every method you use for backprop.
// 3) whenever backpropping into a method where the forward prop involved a scalar (such as scalar
// multiplication), current implementations assume you will NOT backprop into the scalar itself.
// 4) Because of rule (2), do NOT use "emptyTensorCopy" at all in backprop unless you know what you're
// doing.
// 5) I will be especially strict about Unit tests for all backprop logic as this is the most complex
// piece of functionality we have. Furthermore, most errors go completely undetected (not discovered
// by runtime errors). Autograd bugs just make convergence go slowly and sub-optimally.
// 6) If you use a forward propagation tensor to backprop, you MUST remember to turn off autograd
// when backpropagating (see "mm" below for example). Otherwise, it will cause autograd to break because
// whatever child you select will think it needs to wait for another gradient before backpropagating.
// only continue backpropping if there's something to backprop into
// only continue backpropping if all gradients (from children) are accounted for
// override waiting for children if "backprop" was called on this variable directly
if (this.creators != null && this.creators.Count > 0 && (grad_origin == null || AllAutogradChildrenAccountedFor()))
{
if (creation_op == "add_elem")
{
factory.Get(creators[0]).Backward(grad, this);
factory.Get(creators[1]).Backward(grad, this);
}
else if (creation_op == "add_scalar")
{
factory.Get(creators[0]).Backward(grad, this);
}
else if (creation_op == "copy")
{
factory.Get(creators[0]).Backward(grad, this);
}
else if (creation_op == "div_elem")
{
FloatTensor x = factory.Get(creators[0]);
FloatTensor y = factory.Get(creators[1]);
x.Backward(grad.Div(y));
FloatTensor y2 = y.Pow(2);
FloatTensor xn = x.Neg();
FloatTensor xny2 = xn.Div(y2);
FloatTensor gradxny2 = grad.Mul(xny2);
y.Backward(gradxny2);
}
else if (creation_op == "div_scalar")
{
factory.Get(creators[0]).Backward(grad.Div(factory.Get(creators[1]).data[0]), this);
}
else if (creation_op == "mul_elem")
{
factory.Get(creators[0]).Backward(grad.Mul(factory.Get(creators[1])), this);
factory.Get(creators[1]).Backward(grad.Mul(factory.Get(creators[0])), this);
}
else if (creation_op == "mul_scalar")
{
factory.Get(creators[0]).Backward(grad.Mul(factory.Get(creators[1]).data[0]), this);
}
else if (creation_op == "mm")
{
FloatTensor x = factory.Get(creators[1]).Transpose();
x.autograd = false;
FloatTensor y = factory.Get(creators[0]).Transpose();
y.autograd = false;
factory.Get(creators[0]).Backward(grad.MM(x), this);
factory.Get(creators[1]).Backward(y.MM(grad), this);
}
else if (creation_op == "neg")
{
factory.Get(creators[0]).Backward(grad.Neg(), this);
}
else if (creation_op == "pow_scalar")
{
FloatTensor x = factory.Get(creators[0]).Copy();
x.autograd = false;
factory.Get(creators[0]).Backward(x.Mul(grad).Mul(factory.Get(creators[1]).Data[0]), this);
}
else if (creation_op == "sub_elem")
{
factory.Get(creators[0]).Backward(grad, this);
factory.Get(creators[1]).Backward(grad.Neg(), this);
}
else if (creation_op == "sub_scalar")
{
factory.Get(creators[0]).Backward(grad, this);
}
else if (creation_op == "sigmoid")
{
FloatTensor self_nograd = this.Copy();
self_nograd.autograd = false;
factory.Get(creators[0]).Backward(self_nograd.Neg().Add((float)1).Mul(self_nograd).Mul(grad), this);
}
else if (creation_op == "transpose")
{
factory.Get(creators[0]).Backward(grad.Transpose());
}
else if (creation_op == "tanh")
{
FloatTensor c = this.Copy();
c.autograd = false;
factory.Get(creators[0]).Backward(c.Pow(2).Neg().Add(1f).Mul(grad), this);
}
else if (creation_op.Contains("softmax-"))
{
FloatTensor c = this.Copy();
c.autograd = false;
var dim = int.Parse(creation_op.Split('-')[1]);
factory.Get(creators[0]).Backward(Functional.SoftmaxGradient(this, grad, dim), this);
}
else if (creation_op.Contains("sum-"))
{
FloatTensor input = controller.getTensor(creators[0]).Copy();
input.autograd = false;
var dim = input.Shape.Length - 1;
var split = creation_op.Split('-');
if (split.Length > 1)
{
dim = int.Parse(split[1]);
}
// right now this function only supports grads the same size as the output
// and the grad must be contiguous
if (grad.Shape.SequenceEqual(this.Shape) && grad.Strides.SequenceEqual(this.Strides))
{
var res = SumGradient(input, grad, dim);
controller.getTensor(creators[0]).Backward(res, this);
}
else
{
throw new InvalidOperationException("Unable to calculate grad on output of different shape or stride");
}
}
else
{
Debug.Log("Autograd couldn't find matching operation for:" + creation_op);
}
}
}
else
{
Debug.Log("Autograd off - skipping backprop...");
}
}
