mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
3b0b3a2e64
* rtoposort is fast, can replace rangeify with this * fast rangeify * work * fast rangeify works for mnist * should work * progress * pad fix * FAST * tests passing * don't delete those shape ops * put in rangeify map * ending ranges fix * tests * mstack/mselect no hacks * move to indexing.py * touch up tests + add comments * disable failing test * actually make the file readable * failing * error
74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
import unittest, functools
|
|
from tinygrad import Tensor
|
|
import numpy as np
|
|
|
|
def orthogonality_helper(A:Tensor, tolerance=1e-5):
|
|
b_shape,m = A.shape[0:-2],A.shape[-2] #outer dimension should be the dim along orthogonality
|
|
A_identity = (Tensor.eye(m).reshape((1,)*len(b_shape)+(m,m)).expand(b_shape+(m,m)))
|
|
np.testing.assert_allclose((A @ A.transpose(-2,-1)).numpy(),A_identity.numpy(),atol=tolerance,rtol=tolerance)
|
|
|
|
def reconstruction_helper(A:list[Tensor],B:Tensor, tolerance=1e-5):
|
|
reconstructed_tensor = functools.reduce(Tensor.matmul, A)
|
|
np.testing.assert_allclose(reconstructed_tensor.numpy(),B.numpy(),atol=tolerance,rtol=tolerance)
|
|
|
|
class TestLinAlg(unittest.TestCase):
|
|
@unittest.skip("TODO: reenable this")
|
|
def test_svd_general(self):
|
|
sizes = [(2,2),(5,3),(3,5),(3,4,4),(2,2,2,2,3)]
|
|
for size in sizes:
|
|
a = Tensor.randn(size).realize()
|
|
U,S,V = a.svd()
|
|
b_shape,m,n = size[0:-2],size[-2],size[-1]
|
|
k = min(m,n)
|
|
s_diag = (S.unsqueeze(-2) * Tensor.eye(k).reshape((1,) * len(b_shape) + (k,k)))
|
|
s_diag = s_diag.expand(b_shape + (k,k)).pad(tuple([None]*len(b_shape) + [(0,m-k), (0,n-k)]))
|
|
orthogonality_helper(U)
|
|
orthogonality_helper(V)
|
|
reconstruction_helper([U,s_diag,V],a)
|
|
|
|
def test_svd_nonfull(self):
|
|
sizes = [(2,2),(5,3),(3,5),(2,2,2,2,3)]
|
|
for size in sizes:
|
|
a = Tensor.randn(size).realize()
|
|
U,S,V = a.svd(full_matrices=False)
|
|
b_shape,m,n = size[0:-2],size[-2],size[-1]
|
|
k = min(m,n)
|
|
s_diag = (S.unsqueeze(-2) * Tensor.eye(k).reshape((1,) * len(b_shape) + (k,k)).expand(b_shape + (k,k)))
|
|
#reduced U,V is only orthogonal along smaller dim
|
|
if (m < n): orthogonality_helper(U),orthogonality_helper(V)
|
|
else: orthogonality_helper(U.transpose(-2,-1)),orthogonality_helper(V.transpose(-2,-1))
|
|
reconstruction_helper([U,s_diag,V],a)
|
|
|
|
@unittest.skip("very big. recommend wrapping with TinyJit around inner function")
|
|
def test_svd_large(self):
|
|
size = (1024,1024)
|
|
a = Tensor.randn(size).realize()
|
|
U,S,V = a.svd()
|
|
b_shape,m,n = size[0:-2],size[-2],size[-1]
|
|
k = min(m,n)
|
|
s_diag = (S.unsqueeze(-2) * Tensor.eye(k).reshape((1,) * len(b_shape) + (k,k)))
|
|
s_diag = s_diag.expand(b_shape + (k,k)).pad(tuple([None]*len(b_shape) + [(0,m-k), (0,n-k)]))
|
|
orthogonality_helper(U,tolerance=1e-3)
|
|
orthogonality_helper(V,tolerance=1e-3)
|
|
reconstruction_helper([U,s_diag,V],a,tolerance=1e-3)
|
|
|
|
def test_qr_general(self):
|
|
sizes = [(3,3),(3,6),(6,3),(2,2,2,2,2)]
|
|
for size in sizes:
|
|
a = Tensor.randn(size).realize()
|
|
Q,R = a.qr()
|
|
orthogonality_helper(Q)
|
|
reconstruction_helper([Q,R],a)
|
|
|
|
def test_newton_schulz(self):
|
|
coefficients = [(2, -1.5, 0.5), (2.0, -1.4, 0.2, 0.2)]#these params map to the sign function
|
|
sizes = [(2,2), (3,2), (2,3), (2,2,2)]
|
|
for coefs in coefficients:
|
|
for size in sizes:
|
|
a = Tensor.randn(size)
|
|
b = a.newton_schulz(steps=20, params=coefs, eps=0.0)
|
|
# ns(A) = U @ Vt -> (U @ Vt) @ (U @ Vt)t = I
|
|
orthogonality_helper(b if size[-1] > size[-2] else b.transpose(-2, -1), tolerance=1e-3)
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main() |