tensorflow/python/kernel_tests/math_ops/transpose_op_test.py
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functional tests for Transpose op."""
import itertools
import numpy as np
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.platform import test
class TransposeTest(test.TestCase):
def _np_transpose(self, x, perm):
ret = np.copy(x)
ret = ret.transpose(perm)
return ret
def _compareCpu(self, x, p, conjugate=False):
if p is None:
rank = x.ndim
perm = (rank - 1) - np.arange(rank)
else:
perm = p
np_ans = self._np_transpose(x, perm)
if conjugate:
np_ans = np.conj(np_ans)
with self.cached_session(use_gpu=False):
inx = ops.convert_to_tensor(x)
y = array_ops.transpose(inx, p, conjugate=conjugate)
tf_ans = self.evaluate(y)
self.assertShapeEqual(np_ans, y)
self.assertAllEqual(np_ans, tf_ans)
jacob_t = None
# Gradient check on CPU.
if x.dtype in [np.float32, np.complex64]:
jacob_t, jacob_n = gradient_checker_v2.compute_gradient(
lambda x: array_ops.transpose(x, p, conjugate=conjugate), [inx])
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
elif x.dtype in [np.float64, np.complex128]:
jacob_t, jacob_n = gradient_checker_v2.compute_gradient(
lambda x: array_ops.transpose(x, p, conjugate=conjugate), [inx])
self.assertAllClose(jacob_t, jacob_n, 1e-6, 1e-6)
return tf_ans, jacob_t
def _compareGpu(self, x, p, conjugate=False):
if p is None:
rank = x.ndim
perm = (rank - 1) - np.arange(rank)
else:
perm = p
np_ans = self._np_transpose(x, perm)
if conjugate:
np_ans = np.conj(np_ans)
with self.cached_session():
inx = ops.convert_to_tensor(x)
y = array_ops.transpose(inx, p, conjugate=conjugate)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
jacob_t = None
# Gradient check on GPU.
if x.dtype == np.float32:
jacob_t, jacob_n = gradient_checker_v2.compute_gradient(
lambda x: array_ops.transpose(x, p, conjugate=conjugate), [inx])
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
elif x.dtype == np.float64:
jacob_t, jacob_n = gradient_checker_v2.compute_gradient(
lambda x: array_ops.transpose(x, p, conjugate=conjugate), [inx])
self.assertAllClose(jacob_t, jacob_n, 1e-6, 1e-6)
return tf_ans, jacob_t
def _compare(self, x, use_gpu=False):
n = np.ndim(x)
# generate all permutations of [0, 1, ... n-1] in random order.
all_perm = np.random.permutation(
[p for p in itertools.permutations(range(n))]).astype(np.int32)
cs = [False, True] if x.dtype in [np.complex64, np.complex128] else [False]
for c in cs:
for p in all_perm[:2]:
self._compareCpu(x, p, conjugate=c)
if use_gpu:
self._compareGpu(x, p, conjugate=c)
# Test with an empty permutation
for c in cs:
self._compareCpu(x, None, conjugate=c)
if use_gpu:
self._compareGpu(x, None, conjugate=c)
def _compare_cpu_gpu(self, x):
n = np.ndim(x)
# generate all permutation of [0, 1, ... n-1] in random order,
# choose the first two.
perms = itertools.permutations(range(n))
for _ in range(2):
p = np.random.permutation(next(perms)).astype(np.int32) if n > 1 else None
tf_a_cpu, tf_g_cpu = self._compareCpu(x, p)
tf_a_gpu, tf_g_gpu = self._compareGpu(x, p)
assert tf_g_cpu is not None
assert tf_g_gpu is not None
if x.dtype == np.float32:
self.assertAllClose(tf_a_cpu, tf_a_gpu, 1e-3, 1e-3)
self.assertAllClose(tf_g_cpu, tf_g_gpu, 1e-3, 1e-3)
elif x.dtype == np.float64:
self.assertAllClose(tf_a_cpu, tf_a_gpu, 1e-6, 1e-6)
self.assertAllClose(tf_g_cpu, tf_g_gpu, 1e-6, 1e-6)
def _testBoth(self, x):
self._compare(x, use_gpu=False)
self._compare(x, use_gpu=True)
def testRank1(self):
self._compareCpu(np.arange(0., 2), [0])
def test1D(self):
vector = np.arange(0, 2).reshape((1, 1, 1, 2, 1))
self._compare(vector, use_gpu=False)
self._compare(vector, use_gpu=True)
def test5DGPU(self):
# If no GPU available, skip the test
if not test.is_gpu_available(cuda_only=True):
return
large_shapes = [[4, 10, 10, 10, 3], [4, 10, 10, 10, 8], [4, 10, 10, 10, 13],
[4, 3, 10, 10, 10], [4, 8, 10, 10, 10], [4, 13, 10, 10,
10]] * 3
perms = [[0, 4, 1, 2, 3]] * 3 + [[0, 2, 3, 4, 1]] * 3 + [[
4, 1, 2, 3, 0
]] * 6 + [[1, 2, 3, 4, 0]] * 6
datatypes = [
np.int8, np.float16, np.float32, np.float64, np.complex128,
dtypes.bfloat16.as_numpy_dtype
]
for datatype in datatypes:
for input_shape, perm in zip(large_shapes, perms):
with self.subTest(
datatype=datatype, input_shape=input_shape, perm=perm):
total_size = np.prod(input_shape)
inp = np.arange(
1, total_size + 1, dtype=datatype).reshape(input_shape)
np_ans = self._np_transpose(inp, perm)
with self.cached_session():
inx = ops.convert_to_tensor(inp)
y = array_ops.transpose(inx, perm)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
def test4DGPU(self):
# If no GPU available, skip the test
if not test.is_gpu_available(cuda_only=True):
return
large_shapes = [[4, 10, 10, 3], [4, 10, 10, 8], [4, 10, 10, 13],
[4, 3, 10, 10], [4, 8, 10, 10], [4, 13, 10, 10]] * 3
perms = [[0, 3, 1, 2]] * 3 + [[0, 2, 3, 1]] * 3 + [[3, 1, 2, 0]] * 6 + [[
1, 2, 3, 0
]] * 3 + [[2, 3, 0, 1]] * 3
for input_shape, perm in zip(large_shapes, perms):
with self.subTest(input_shape=input_shape, perm=perm):
total_size = np.prod(input_shape)
inp = np.arange(
1, total_size + 1, dtype=np.float32).reshape(input_shape)
np_ans = self._np_transpose(inp, perm)
with self.cached_session():
inx = ops.convert_to_tensor(inp)
y = array_ops.transpose(inx, perm)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
# shapes related to Inception (taken from conv_ops_test.py)
inception_shapes = [[4, 5, 5, 124], [4, 8, 8, 38], [4, 8, 8, 38], [
4, 8, 8, 204
], [4, 8, 8, 44], [4, 8, 8, 204], [4, 8, 8, 204], [4, 8, 8, 204], [
4, 8, 8, 176
], [4, 8, 8, 176], [4, 8, 8, 176], [4, 8, 8, 176], [4, 17, 17, 19], [
4, 17, 17, 19
], [4, 17, 17, 124], [4, 17, 17, 12], [4, 17, 17, 124], [4, 17, 17, 22], [
4, 17, 17, 19
], [4, 17, 17, 19], [4, 17, 17, 121], [4, 17, 17, 121], [4, 17, 17, 22], [
4, 17, 17, 19
], [4, 17, 17, 19], [4, 17, 17, 115], [4, 17, 17, 115], [4, 17, 17, 19], [
4, 17, 17, 16
], [4, 17, 17, 115], [4, 17, 17, 102], [4, 17, 17, 12], [4, 17, 17, 102], [
4, 17, 17, 12
], [4, 17, 17, 102], [4, 17, 17, 12], [4, 17, 17, 76], [4, 17, 17, 12], [
4, 17, 17, 12
], [4, 17, 17, 76], [4, 17, 17, 76], [4, 35, 35, 9], [4, 35, 35, 28], [
4, 35, 35, 6
], [4, 35, 35, 28], [4, 35, 35, 25], [4, 35, 35, 4], [4, 35, 35, 25],
[4, 35, 35, 9], [4, 35, 35, 19], [4, 35, 35, 19],
[4, 35, 35, 19], [4, 73, 73, 6], [4, 73, 73,
6], [4, 147, 147, 2]]
for input_shape in inception_shapes:
with self.subTest(input_shape=input_shape):
perm = [0, 3, 1, 2]
total_size = np.prod(input_shape)
inp = np.arange(
1, total_size + 1, dtype=np.float32).reshape(input_shape)
np_ans = self._np_transpose(inp, perm)
with self.cached_session():
inx = ops.convert_to_tensor(inp)
y = array_ops.transpose(inx, perm)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
def test3DGPU(self):
# If no GPU available, skip the test
if not test.is_gpu_available(cuda_only=True):
return
datatypes = [np.int8, np.float16, np.float32, np.float64, np.complex128]
large_shapes = [[4, 1000, 3], [4, 1000, 8], [4, 1000, 13], [4, 3, 1000],
[4, 8, 1000], [4, 13, 1000]] * 3
perms = [[0, 2, 1]] * 6 + [[2, 1, 0]] * 6 + [[1, 2, 0]] * 3 + [[2, 0, 1]
] * 3
for datatype in datatypes:
for input_shape, perm in zip(large_shapes, perms):
with self.subTest(
datatype=datatype, input_shape=input_shape, perm=perm):
total_size = np.prod(input_shape)
inp = np.arange(
1, total_size + 1, dtype=datatype).reshape(input_shape)
np_ans = self._np_transpose(inp, perm)
with self.cached_session():
inx = ops.convert_to_tensor(inp)
y = array_ops.transpose(inx, perm)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
def testLargeSizeGPU(self):
# If no GPU available, skip the test
if not test.is_gpu_available(cuda_only=True):
return
large_shapes = [[1000000, 31, 3], [3, 1000000, 31], [3, 31, 1000000],
[10000, 310, 3], [3, 10000, 310], [3, 310, 10000],
[2, 1000, 1000], [1000, 2, 1000], [1000, 1000, 2]]
perms = [[0, 2, 1]] * 9
for input_shape, perm in zip(large_shapes, perms):
with self.subTest(input_shape=input_shape, perm=perm):
total_size = np.prod(input_shape)
inp = np.arange(
1, total_size + 1, dtype=np.float32).reshape(input_shape)
np_ans = self._np_transpose(inp, perm)
with self.cached_session():
inx = ops.convert_to_tensor(inp)
y = array_ops.transpose(inx, perm)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
def testRandomizedSmallDimLargeSizeGPU(self):
# If no GPU available, skip the test
if not test.is_gpu_available(cuda_only=True):
return
# Draw 10 random shapes with large dimension sizes.
# 40% prob to generate dim[0] size within [1, 2047]
# 40% prob to generate dim[0] size within [2048, 4095]
# 20% prob to generate dim[0] size within [4096, 100000]
# 50% prob to use dim[1] as the small dim (<16)
num_samples = 10
total_size = 500000
small_size_limit = 2048
large_size_limit = 95905
small_size_percentage = 0.4
medium_size_percentage = 0.4
large_size_percentage = 0.2
perms = [[0, 2, 1]] * num_samples
dim_zero_sizes = []
dim_zero_sizes += list(
np.random.randint(
small_size_limit, size=int(small_size_percentage * num_samples)) +
1)
dim_zero_sizes += list(
np.random.randint(
small_size_limit, size=int(medium_size_percentage * num_samples)) +
small_size_limit)
dim_zero_sizes += list(
np.random.randint(
large_size_limit, size=int(large_size_percentage * num_samples)) +
small_size_limit * 2)
input_shapes = []
small_dim_limit = 16
for dim_zero_size in dim_zero_sizes:
small_dim_size = np.random.randint(small_dim_limit - 1) + 1
large_dim_size = int(
total_size / dim_zero_size / small_dim_size) + small_dim_limit
input_shapes += ([[dim_zero_size, small_dim_size, large_dim_size]]
if np.random.randint(2) else
[[dim_zero_size, large_dim_size, small_dim_size]])
for input_shape, perm in zip(input_shapes, perms):
# generate input data with random ints from 0 to 9.
with self.subTest(input_shape=input_shape, perm=perm):
inp = np.random.randint(10, size=input_shape)
np_ans = self._np_transpose(inp, perm)
with self.cached_session():
inx = ops.convert_to_tensor(inp)
y = array_ops.transpose(inx, perm)
tf_ans = self.evaluate(y)
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
self._ClearCachedSession()
def testNop(self):
self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
def testSimple(self):
self._compareCpu(
np.arange(0, 8).reshape([2, 4]).astype(np.float32),
np.array([1, 0]).astype(np.int32))
def testPermType(self):
for perm_dtype in [np.int64, np.int32]:
with self.subTest(perm_dtype=perm_dtype):
x = np.arange(0, 8).reshape([2, 4]).astype(np.float32)
p = np.array([1, 0]).astype(perm_dtype)
np_ans = np.copy(x).transpose(p)
with self.cached_session():
inx = ops.convert_to_tensor(x)
inp = constant_op.constant(p)
y = array_ops.transpose(inx, inp)
tf_ans = self.evaluate(y)
self.assertShapeEqual(np_ans, y)
self.assertAllEqual(np_ans, tf_ans)
def testHalf(self):
self._compare(np.arange(0, 21).reshape([3, 7]).astype(np.float16))
self._compare(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float16))
self._compare(
np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float16))
def testBfloat16(self):
self._compare(
np.arange(0, 21).reshape([3, 7]).astype(dtypes.bfloat16.as_numpy_dtype))
self._compare(
np.arange(0, 210).reshape([2, 3, 5,
7]).astype(dtypes.bfloat16.as_numpy_dtype))
self._compare(
np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1,
2]).astype(dtypes.bfloat16.as_numpy_dtype))
def testFloat(self):
self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float32))
self._compare_cpu_gpu(
np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float32))
self._compare_cpu_gpu(
np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float32))
def testDouble(self):
self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float64))
self._compare_cpu_gpu(
np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float64))
self._compare_cpu_gpu(
np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float64))
def testComplex64(self):
self._testBoth(np.array(1 + 2j).astype(np.complex64))
self._testBoth((1 + 2j) * np.arange(0, 21).astype(np.complex64))
self._testBoth(
(1 + 2j) * np.arange(0, 21).reshape([3, 7]).astype(np.complex64))
self._testBoth(
(1 + 2j) * np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.complex64))
self._testBoth(
(1 + 2j) *
np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.complex64))
def testComplex128(self):
self._testBoth(np.array(1 + 2j).astype(np.complex128))
self._testBoth((1 + 2j) * np.arange(0, 21).astype(np.complex128))
self._testBoth(
(1 + 2j) * np.arange(0, 21).reshape([3, 7]).astype(np.complex128))
self._testBoth(
(1 + 2j) *
np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.complex128))
self._testBoth(
(1 + 2j) *
np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.complex128))
def testInt8(self):
self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int8))
self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int8))
self._testBoth(
np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int8))
def testInt16(self):
self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int16))
self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int16))
self._testBoth(
np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int16))
def testInt32(self):
self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int32))
self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int32))
self._testBoth(
np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int32))
def testInt64(self):
self._testBoth(np.arange(0, 21).reshape([3, 7]).astype(np.int64))
self._testBoth(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int64))
self._testBoth(
np.arange(0, 1260).reshape([2, 3, 5, 7, 2, 3]).astype(np.int64))
def testTranspose2DAuto(self):
x_np = [[1, 2, 3], [4, 5, 6]]
for use_gpu in [False, True]:
with self.subTest(use_gpu=use_gpu):
with self.cached_session(use_gpu=use_gpu):
x_tf = array_ops.transpose(x_np)
self.assertAllEqual(x_tf, [[1, 4], [2, 5], [3, 6]])
def testSingletonDims(self):
# A singleton dimension is a dimension i with shape[i] == 1. Such dimensions
# can be collapsed and expanded using reshape without changing the
# underlying data storage. If all non-singleton dimensions remain in
# ascending order, the shuffled singletons will be transposed by a reshape,
# saving a memory allocation & copy. Since this gets a special code-path in
# transpose_op.cc, we test that the codepath is exercised and the results
# are as expected; we do not test that we save the memory allocation and
# copy here.
for shape in [[2, 1, 2], [2, 1, 2, 1, 1, 2], [1, 2, 2, 1, 1, 1],
[1, 1, 1, 2, 2, 2], [2, 2, 1, 1, 1]]:
with self.subTest(shape=shape):
self._compare_cpu_gpu(
np.arange(np.prod(shape)).reshape(shape).astype(np.float32))
def testTransposeShapes(self):
self.assertEqual([],
array_ops.transpose(
constant_op.constant(1, dtype=dtypes.int32,
shape=[])).get_shape().dims)
self.assertEqual([100],
array_ops.transpose(
constant_op.constant(
1, dtype=dtypes.int32,
shape=[100])).get_shape().dims)
self.assertEqual([37, 100],
array_ops.transpose(
constant_op.constant(
1, dtype=dtypes.int32,
shape=[100, 37])).get_shape().dims)
self.assertEqual([100, 37],
array_ops.transpose(
constant_op.constant(
1, dtype=dtypes.int32, shape=[100, 37]),
[0, 1]).get_shape().dims)
self.assertEqual([15, 37, 100],
array_ops.transpose(
constant_op.constant(
1, dtype=dtypes.int32,
shape=[100, 37, 15])).get_shape().dims)
self.assertEqual([15, 100, 37],
array_ops.transpose(
constant_op.constant(
1, dtype=dtypes.int32, shape=[100, 37, 15]),
[2, 0, 1]).get_shape().dims)
def testTransposeDynamicShapes(self):
@def_function.function(input_signature=[
tensor_spec.TensorSpec(shape=None, dtype=dtypes.int32),
])
def transpose(x):
y = array_ops.transpose(x)
self.assertEqual(y.shape, tensor_shape.TensorShape(None))
return y
x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) # Shape (2, 3)
expected_transpose = constant_op.constant([[1, 4], [2, 5],
[3, 6]]) # Shape (3, 2)
self.assertAllEqual(expected_transpose, transpose(x))
@def_function.function(input_signature=[
tensor_spec.TensorSpec(shape=None, dtype=dtypes.int32),
tensor_spec.TensorSpec(shape=None, dtype=dtypes.int32),
])
def transpose_with_perm(x, perm):
y = array_ops.transpose(x, perm)
self.assertEqual(y.shape, tensor_shape.TensorShape(None))
return y
self.assertAllEqual(x, transpose_with_perm(x, [0, 1]))
def testNullTensor(self):
with self.cached_session():
x = constant_op.constant([], dtype=dtypes.float32, shape=[1, 4, 0])
xt = array_ops.transpose(x, [0, 2, 1])
self.assertAllEqual(xt.shape, (1, 0, 4))
def testScalar(self):
with self.cached_session():
x = constant_op.constant(42, dtype=dtypes.float32, shape=[])
xt = array_ops.transpose(x)
self.assertAllEqual(xt, x)
def _testError(self, x, p, err):
with self.cached_session():
with self.assertRaisesOpError(err):
self.evaluate(array_ops.transpose(x, p))
def testError(self):
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
r"must be rank 1"):
array_ops.transpose(
np.arange(0., 30).reshape([2, 3, 5]), [[0, 1], [2, 3]])
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
r"3 is out of range"):
array_ops.transpose(np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 3])
self._testError(
np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 1], "2 is missing")
if __name__ == "__main__":
test.main()