removing docs since interface is now the same of pytorch, LOC < 50 ;)

tinygrad/gradcheck.py

@@ -2,16 +2,6 @@ import numpy as np
 from tinygrad.tensor import Tensor
 
 def jacobian(func, input):
-  """
-  Compute the (analytical) Jacobian of func w.r.t. input.
-
-      func : A tinygrad func
-      input : An input
-
-  returns:
-
-      J : Jacobian
-  """
   output = func(input)
 
   ji = input.data.reshape(-1).shape[-1]
@@ -34,20 +24,6 @@ def mask_like(like, mask_inx, mask_value = 1.0):
   return mask.reshape(like.shape)
 
 def numerical_jacobian(func, input, eps = 1e-6):
-  """
-  Compute the Jacobian through Finite-Difference Approximation.
-  Somewhat inspired by [1] but not followed closely.
-
-      func : A tinygrad func
-      input : An input
-      eps : Perturbation step
-
-  returns:
-
-      NJ : an approx. of the Jacobian
-
-  [1]: https://timvieira.github.io/blog/post/2017/04/21/how-to-test-gradient-implementations/
-  """
   output = func(input)
 
   ji = input.data.reshape(-1).shape[-1]
@@ -67,18 +43,6 @@ def numerical_jacobian(func, input, eps = 1e-6):
   return NJ
 
 def gradcheck(func, input, eps = 1e-06, atol = 1e-5, rtol = 0.001):
-  """
-  Checks whether the numerical approx. of the Jacobian of func w.r.t input is close to the
-  analytical one.
-
-      func : A tinygrad func
-      input : An input
-      eps : Perturbation step
-      atol, rtol: Params for the numpy.allclose test
-
-  returns:
-      test_passed : Bool, whether the test passed
-  """
   NJ = numerical_jacobian(func, input, eps)
   J = jacobian(func, input)