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import numpy as np
import unittest
from caffe2.python import core, workspace, tt_core
import caffe2.python.hypothesis_test_util as hu
class TestTTSVD(hu.HypothesisTestCase):
def test_full_tt_svd(self):
size = 256
np.random.seed(1234)
X = np.expand_dims(
np.random.rand(size).astype(np.float32), axis=0)
W = np.random.rand(size, size).astype(np.float32)
b = np.zeros(size).astype(np.float32)
inp_sizes = [4, 4, 4, 4]
out_sizes = [4, 4, 4, 4]
op_fc = core.CreateOperator(
"FC",
["X", "W", "b"],
["Y"],
)
workspace.FeedBlob("X", X)
workspace.FeedBlob("W", W)
workspace.FeedBlob("b", b)
workspace.RunOperatorOnce(op_fc)
Y_fc = workspace.FetchBlob("Y").flatten()
# Testing TT-decomposition with high ranks
full_tt_ranks = [1, 16, 256, 16, 1]
full_cores = tt_core.matrix_to_tt(W, inp_sizes, out_sizes,
full_tt_ranks)
full_op_tt = core.CreateOperator(
"TT",
["X", "b", "cores"],
["Y"],
inp_sizes=inp_sizes,
out_sizes=out_sizes,
tt_ranks=full_tt_ranks,
)
workspace.FeedBlob("X", X)
workspace.FeedBlob("b", b)
workspace.FeedBlob("cores", full_cores)
workspace.RunOperatorOnce(full_op_tt)
Y_full_tt = workspace.FetchBlob("Y").flatten()
assert(len(Y_fc) == len(Y_full_tt))
self.assertAlmostEquals(np.linalg.norm(Y_fc - Y_full_tt), 0, delta=1e-3)
# Testing TT-decomposition with minimal ranks
sparse_tt_ranks = [1, 1, 1, 1, 1]
sparse_cores = tt_core.matrix_to_tt(W, inp_sizes, out_sizes,
sparse_tt_ranks)
sparse_op_tt = core.CreateOperator(
"TT",
["X", "b", "cores"],
["Y"],
inp_sizes=inp_sizes,
out_sizes=out_sizes,
tt_ranks=sparse_tt_ranks,
)
workspace.FeedBlob("X", X)
workspace.FeedBlob("b", b)
workspace.FeedBlob("cores", sparse_cores)
workspace.RunOperatorOnce(sparse_op_tt)
Y_sparse_tt = workspace.FetchBlob("Y").flatten()
assert(len(Y_fc) == len(Y_sparse_tt))
self.assertAlmostEquals(np.linalg.norm(Y_fc - Y_sparse_tt),
39.974, delta=1e-3)
if __name__ == '__main__':
unittest.main()
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