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start testing with different dtypes
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@Bihaqo
Bihaqo committed Oct 27, 2018
1 parent 0e982cd commit 5b60886
Showing 1 changed file with 108 additions and 72 deletions.
180 changes: 108 additions & 72 deletions t3f/ops_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,13 +8,13 @@
from t3f import initializers


class TTTensorTest(tf.test.TestCase):
class _TTTensorTest():

def testFullTensor2d(self):
np.random.seed(1)
for rank in [1, 2]:
a = np.random.rand(10, rank).astype(np.float32)
b = np.random.rand(rank, 9).astype(np.float32)
a = np.random.rand(10, rank).astype(self.np_dtype)
b = np.random.rand(rank, 9).astype(self.np_dtype)
tt_cores = (a.reshape(1, 10, rank), b.reshape(rank, 9, 1))
desired = np.dot(a, b)
with self.test_session():
Expand All @@ -25,9 +25,9 @@ def testFullTensor2d(self):
def testFullTensor3d(self):
np.random.seed(1)
for rank_1 in [1, 2]:
a = np.random.rand(10, rank_1).astype(np.float32)
b = np.random.rand(rank_1, 9, 3).astype(np.float32)
c = np.random.rand(3, 8).astype(np.float32)
a = np.random.rand(10, rank_1).astype(self.np_dtype)
b = np.random.rand(rank_1, 9, 3).astype(self.np_dtype)
c = np.random.rand(3, 8).astype(self.np_dtype)
tt_cores = (a.reshape(1, 10, rank_1), b, c.reshape((3, 8, 1)))
# Basically do full by hand.
desired = a.dot(b.reshape((rank_1, -1)))
Expand Down Expand Up @@ -69,10 +69,11 @@ def testFlatInnerTTTensbySparseTens(self):
for rank in rank_list:
for num_elements in [1, 10]:
tt_1 = initializers.random_tensor(shape, tt_rank=rank)
sparse_flat_indices = np.random.choice(np.prod(shape), num_elements).astype(int)
sparse_flat_indices = np.random.choice(np.prod(shape), num_elements)
sparse_flat_indices = sparse_flat_indices.astype(int)
sparse_indices = np.unravel_index(sparse_flat_indices, shape)
sparse_indices = np.vstack(sparse_indices).transpose()
values = np.random.randn(num_elements).astype(np.float32)
values = np.random.randn(num_elements).astype(self.np_dtype)
sparse_2 = tf.SparseTensor(indices=sparse_indices, values=values,
dense_shape=shape)
res_actual = ops.flat_inner(tt_1, sparse_2)
Expand Down Expand Up @@ -144,11 +145,10 @@ def testCastFloat(self):
tt_x = initializers.random_tensor((2, 3, 2), tt_rank=2)

with self.test_session() as sess:
for dtype in [tf.float16, tf.float32, tf.float64]:
casted = ops.cast(tt_x, dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(dtype, casted.dtype)
self.assertTrue(dtype, casted_val.dtype)
casted = ops.cast(tt_x, self.tf_dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(self.tf_dtype, casted.dtype)
self.assertTrue(self.tf_dtype, casted_val.dtype)

def testCastIntFloat(self):
# Tests cast function from int to float for tensors.
Expand All @@ -159,11 +159,10 @@ def testCastIntFloat(self):
tt_int = TensorTrain([K_1, K_2, K_3], tt_ranks=[1, 2, 2, 1])

with self.test_session() as sess:
for dtype in [tf.float16, tf.float32, tf.float64]:
casted = ops.cast(tt_int, dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(dtype, casted.dtype)
self.assertTrue(dtype, casted_val.dtype)
casted = ops.cast(tt_int, self.tf_dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(self.tf_dtype, casted.dtype)
self.assertTrue(self.tf_dtype, casted_val.dtype)

def testCoreRenorm(self):
a = initializers.random_tensor(3 * (10,), tt_rank=7)
Expand All @@ -180,13 +179,13 @@ def testCoreRenorm(self):
* np.ones((len(b_cores))))


class TTMatrixTest(tf.test.TestCase):
class _TTMatrixTest():

def testFullMatrix2d(self):
np.random.seed(1)
for rank in [1, 2]:
a = np.random.rand(2, 3, rank).astype(np.float32)
b = np.random.rand(rank, 4, 5).astype(np.float32)
a = np.random.rand(2, 3, rank).astype(self.np_dtype)
b = np.random.rand(rank, 4, 5).astype(self.np_dtype)
tt_cores = (a.reshape(1, 2, 3, rank), b.reshape((rank, 4, 5, 1)))
# Basically do full by hand.
desired = a.reshape((-1, rank)).dot(b.reshape((rank, -1)))
Expand All @@ -201,9 +200,9 @@ def testFullMatrix2d(self):
def testFullMatrix3d(self):
np.random.seed(1)
for rank in [1, 2]:
a = np.random.rand(2, 3, rank).astype(np.float32)
b = np.random.rand(rank, 4, 5, rank).astype(np.float32)
c = np.random.rand(rank, 2, 2).astype(np.float32)
a = np.random.rand(2, 3, rank).astype(self.np_dtype)
b = np.random.rand(rank, 4, 5, rank).astype(self.np_dtype)
c = np.random.rand(rank, 2, 2).astype(self.np_dtype)
tt_cores = (a.reshape(1, 2, 3, rank), b.reshape(rank, 4, 5, rank),
c.reshape(rank, 2, 2, 1))
# Basically do full by hand.
Expand Down Expand Up @@ -237,7 +236,7 @@ def testTTMatTimesDenseVec(self):
inp_shape = (2, 3, 4)
out_shape = (3, 4, 3)
np.random.seed(1)
vec = np.random.rand(np.prod(inp_shape), 1).astype(np.float32)
vec = np.random.rand(np.prod(inp_shape), 1).astype(self.np_dtype)
with self.test_session() as sess:
tf_vec = tf.constant(vec)
tf.set_random_seed(1)
Expand All @@ -252,7 +251,8 @@ def testDenseMatTimesTTVec(self):
inp_shape = (3, 3, 3, 3)
out_shape = (3, 3, 3, 3)
np.random.seed(1)
mat = np.random.rand(np.prod(out_shape), np.prod(inp_shape)).astype(np.float32)
mat = np.random.rand(np.prod(out_shape), np.prod(inp_shape))
mat = mat.astype(self.np_dtype)
with self.test_session() as sess:
tf_mat = tf.constant(mat)
tf.set_random_seed(1)
Expand Down Expand Up @@ -296,7 +296,7 @@ def testFlatInnerTTMatbySparseMat(self):
sparse_flat_indices = sparse_flat_indices.astype(int)
sparse_indices = np.unravel_index(sparse_flat_indices, matrix_shape)
sparse_indices = np.vstack(sparse_indices).transpose()
values = np.random.randn(num_elements).astype(np.float32)
values = np.random.randn(num_elements).astype(self.np_dtype)
sparse_2 = tf.SparseTensor(indices=sparse_indices, values=values,
dense_shape=matrix_shape)
res_actual = ops.flat_inner(tt_1, sparse_2)
Expand Down Expand Up @@ -383,11 +383,10 @@ def testCastFloat(self):

with self.test_session() as sess:
for tt in [tt_mat, tt_vec]:
for dtype in [tf.float16, tf.float32, tf.float64]:
casted = ops.cast(tt, dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(dtype, casted.dtype)
self.assertTrue(dtype, casted_val.dtype)
casted = ops.cast(tt, self.tf_dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(self.tf_dtype, casted.dtype)
self.assertTrue(self.tf_dtype, casted_val.dtype)

def testCastIntFloat(self):
# Tests cast function from int to float for matrices.
Expand All @@ -398,16 +397,15 @@ def testCastIntFloat(self):
tt_int = TensorTrain([K_1, K_2, K_3], tt_ranks=[1, 2, 2, 1])

with self.test_session() as sess:
for dtype in [tf.float16, tf.float32, tf.float64]:
casted = ops.cast(tt_int, dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(dtype, casted.dtype)
self.assertTrue(dtype, casted_val.dtype)
casted = ops.cast(tt_int, self.tf_dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(self.tf_dtype, casted.dtype)
self.assertTrue(self.tf_dtype, casted_val.dtype)

def testUnknownRanksTTMatmul(self):
# Tests tt_tt_matmul for matrices with unknown ranks
K_1 = tf.placeholder(tf.float32, (1, 2, 2, None))
K_2 = tf.placeholder(tf.float32, (None, 3, 3, 1))
K_1 = tf.placeholder(self.tf_dtype, (1, 2, 2, None))
K_2 = tf.placeholder(self.tf_dtype, (None, 3, 3, 1))
tt_mat = TensorTrain([K_1, K_2])
res_actual = ops.full(ops.matmul(tt_mat, tt_mat))
res_desired = tf.matmul(ops.full(tt_mat), ops.full(tt_mat))
Expand All @@ -424,8 +422,8 @@ def testHalfKnownRanksTTMatmul(self):
# Tests tt_tt_matmul for the case when one matrice has known ranks
# and the other one doesn't
np.random.seed(1)
K_1 = tf.placeholder(tf.float32, (1, 2, 2, None))
K_2 = tf.placeholder(tf.float32, (None, 3, 3, 1))
K_1 = tf.placeholder(self.tf_dtype, (1, 2, 2, None))
K_2 = tf.placeholder(self.tf_dtype, (None, 3, 3, 1))
tt_mat_known_ranks = TensorTrain([K_1, K_2], tt_ranks=[1, 3, 1])
tt_mat = TensorTrain([K_1, K_2])
res_actual = ops.full(ops.matmul(tt_mat_known_ranks, tt_mat))
Expand All @@ -439,13 +437,13 @@ def testHalfKnownRanksTTMatmul(self):
self.assertAllClose(res_desired_val, res_actual_val)


class TTTensorBatchTest(tf.test.TestCase):
class _TTTensorBatchTest():

def testFullTensor2d(self):
np.random.seed(1)
for rank in [1, 2]:
a = np.random.rand(3, 10, rank).astype(np.float32)
b = np.random.rand(3, rank, 9).astype(np.float32)
a = np.random.rand(3, 10, rank).astype(self.np_dtype)
b = np.random.rand(3, rank, 9).astype(self.np_dtype)
tt_cores = (a.reshape(3, 1, 10, rank), b.reshape(3, rank, 9, 1))
desired = np.einsum('oib,obj->oij', a, b)
with self.test_session():
Expand All @@ -456,9 +454,9 @@ def testFullTensor2d(self):
def testFullTensor3d(self):
np.random.seed(1)
for rank_1 in [1, 2]:
a = np.random.rand(3, 10, rank_1).astype(np.float32)
b = np.random.rand(3, rank_1, 9, 3).astype(np.float32)
c = np.random.rand(3, 3, 8).astype(np.float32)
a = np.random.rand(3, 10, rank_1).astype(self.np_dtype)
b = np.random.rand(3, rank_1, 9, 3).astype(self.np_dtype)
c = np.random.rand(3, 3, 8).astype(self.np_dtype)
tt_cores = (a.reshape(3, 1, 10, rank_1), b, c.reshape((3, 3, 8, 1)))
# Basically do full by hand.
desired = np.einsum('oia,oajb,obk->oijk', a, b, c)
Expand Down Expand Up @@ -610,8 +608,8 @@ def testMultiplyBroadcasting(self):
self.assertAllClose(res_actual2_val, res_desired_val)

def testMultiplyUnknownBatchSizeBroadcasting(self):
c1 = tf.placeholder(tf.float32, [None, 1, 3, 2])
c2 = tf.placeholder(tf.float32, [None, 2, 3, 1])
c1 = tf.placeholder(self.tf_dtype, [None, 1, 3, 2])
c2 = tf.placeholder(self.tf_dtype, [None, 2, 3, 1])
tt_a = TensorTrainBatch([c1, c2])
tt_b = initializers.random_tensor_batch((3, 3), tt_rank=3, batch_size=1)
tt_c = initializers.random_tensor((3, 3), tt_rank=3)
Expand All @@ -632,10 +630,10 @@ def testMultiplyUnknownBatchSizeBroadcasting(self):
self.assertAllClose(ca, des_ac)

def testMultiplyTwoBatchesUnknownSize(self):
c1 = tf.placeholder(tf.float32, [None, 1, 3, 2])
c2 = tf.placeholder(tf.float32, [None, 2, 3, 1])
c3 = tf.placeholder(tf.float32, [None, 1, 3, 2])
c4 = tf.placeholder(tf.float32, [None, 2, 3, 1])
c1 = tf.placeholder(self.tf_dtype, [None, 1, 3, 2])
c2 = tf.placeholder(self.tf_dtype, [None, 2, 3, 1])
c3 = tf.placeholder(self.tf_dtype, [None, 1, 3, 2])
c4 = tf.placeholder(self.tf_dtype, [None, 2, 3, 1])
tt_a = TensorTrainBatch([c1, c2])
tt_b = TensorTrainBatch([c3, c4])
res_ab = ops.full(ops.multiply(tt_a, tt_b))
Expand All @@ -660,8 +658,8 @@ def testMultiplyTwoBatchesUnknownSize(self):
sess.run(to_run, feed_dict=feed_dict_err)

def testMultiplyUnknownSizeBatchAndBatch(self):
c1 = tf.placeholder(tf.float32, [None, 1, 3, 2])
c2 = tf.placeholder(tf.float32, [None, 2, 3, 1])
c1 = tf.placeholder(self.tf_dtype, [None, 1, 3, 2])
c2 = tf.placeholder(self.tf_dtype, [None, 2, 3, 1])
tt_b = initializers.random_tensor_batch((3, 3), tt_rank=2, batch_size=8)
tt_a = TensorTrainBatch([c1, c2])
res_ab = ops.full(ops.multiply(tt_a, tt_b))
Expand Down Expand Up @@ -722,13 +720,13 @@ def testCoreRenormBatch(self):
self.assertAllClose(b_cores_norms, b_cores_norms[0]
* np.ones((len(b_cores))))

class TTMatrixTestBatch(tf.test.TestCase):
class _TTMatrixTestBatch():

def testFullMatrix2d(self):
np.random.seed(1)
for rank in [1, 2]:
a = np.random.rand(3, 2, 3, rank).astype(np.float32)
b = np.random.rand(3, rank, 4, 5).astype(np.float32)
a = np.random.rand(3, 2, 3, rank).astype(self.np_dtype)
b = np.random.rand(3, rank, 4, 5).astype(self.np_dtype)
tt_cores = (a.reshape(3, 1, 2, 3, rank), b.reshape((3, rank, 4, 5, 1)))
# Basically do full by hand.
desired = np.einsum('oijb,obkl->oijkl', a, b)
Expand All @@ -743,9 +741,9 @@ def testFullMatrix2d(self):
def testFullMatrix3d(self):
np.random.seed(1)
for rank in [1, 2]:
a = np.random.rand(3, 2, 3, rank).astype(np.float32)
b = np.random.rand(3, rank, 4, 5, rank).astype(np.float32)
c = np.random.rand(3, rank, 2, 2).astype(np.float32)
a = np.random.rand(3, 2, 3, rank).astype(self.np_dtype)
b = np.random.rand(3, rank, 4, 5, rank).astype(self.np_dtype)
c = np.random.rand(3, rank, 2, 2).astype(self.np_dtype)
tt_cores = (a.reshape(3, 1, 2, 3, rank), b.reshape(3, rank, 4, 5, rank),
c.reshape(3, rank, 2, 2, 1))
# Basically do full by hand.
Expand Down Expand Up @@ -844,11 +842,10 @@ def testCastFloat(self):
batch_size=3)

with self.test_session() as sess:
for dtype in [tf.float16, tf.float32, tf.float64]:
casted = ops.cast(tt_mat, dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(dtype, casted.dtype)
self.assertTrue(dtype, casted_val.dtype)
casted = ops.cast(tt_mat, self.tf_dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(self.tf_dtype, casted.dtype)
self.assertTrue(self.tf_dtype, casted_val.dtype)

def testCastIntFloat(self):
# Tests cast function from int to float for matrices.
Expand All @@ -860,21 +857,60 @@ def testCastIntFloat(self):
tt_int_batch = shapes.expand_batch_dim(tt_int)

with self.test_session() as sess:
for dtype in [tf.float16, tf.float32, tf.float64]:
casted = ops.cast(tt_int_batch, dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(dtype, casted.dtype)
self.assertTrue(dtype, casted_val.dtype)
casted = ops.cast(tt_int_batch, self.tf_dtype)
casted_val = sess.run(ops.full(casted))
self.assertEqual(self.tf_dtype, casted.dtype)
self.assertTrue(self.tf_dtype, casted_val.dtype)


def _random_sparse(shape, non_zeros):
sparse_flat_indices = np.random.choice(np.prod(shape), non_zeros).astype(int)
sparse_indices = np.unravel_index(sparse_flat_indices, shape)
sparse_indices = np.vstack(sparse_indices).transpose()
values = np.random.randn(non_zeros).astype(np.float32)
values = np.random.randn(non_zeros).astype(self.np_dtype)
sparse = tf.SparseTensor(indices=sparse_indices, values=values,
dense_shape=shape)
return sparse


class TTTensorTestFloat32(tf.test.TestCase, _TTTensorTest):
np_dtype = np.float32
tf_dtype = tf.float32


class TTTensorTestFloat64(tf.test.TestCase, _TTTensorTest):
np_dtype = np.float64
tf_dtype = tf.float64


class TTMatrixTestFloat32(tf.test.TestCase, _TTMatrixTest):
np_dtype = np.float32
tf_dtype = tf.float32


class TTMatrixTestFloat64(tf.test.TestCase, _TTMatrixTest):
np_dtype = np.float64
tf_dtype = tf.float64


class TTTensorBatchTestFloat32(tf.test.TestCase, _TTTensorBatchTest):
np_dtype = np.float32
tf_dtype = tf.float32


class TTTensorBatchTestFloat64(tf.test.TestCase, _TTTensorBatchTest):
np_dtype = np.float64
tf_dtype = tf.float64

class TTMatrixTestBatchFloat32(tf.test.TestCase, _TTMatrixTestBatch):
np_dtype = np.float32
tf_dtype = tf.float32


class TTMatrixTestBatchFloat64(tf.test.TestCase, _TTMatrixTestBatch):
np_dtype = np.float64
tf_dtype = tf.float64


if __name__ == "__main__":
tf.test.main()

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