1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
|
"""Tests for tensorflow.ops.tf.BatchMatMul."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class BatchMatmulOpTest(tf.test.TestCase):
# Uses numpy to compute batch_matmul(x, y, adj_x, adj_y).
def _npBatchMatmul(self, x, y, adj_x, adj_y):
assert x.ndim >= 3
assert y.ndim >= 3
# output's shape depends on adj[0] and adj[1]
d0 = x.shape[-2] if not adj_x else x.shape[-1]
d2 = y.shape[-1] if not adj_y else y.shape[-2]
batch_dims = x.shape[:-2]
num = np.prod(batch_dims)
z = np.empty(list(batch_dims) + [d0, d2], dtype=x.dtype)
xr = x.reshape([num, x.shape[-2], x.shape[-1]])
yr = y.reshape([num, y.shape[-2], y.shape[-1]])
zr = z.reshape([num, z.shape[-2], z.shape[-1]])
for i in range(num):
a = np.matrix(xr[i, :, :])
if adj_x:
a = a.transpose().conj()
b = np.matrix(yr[i, :, :])
if adj_y:
b = b.transpose().conj()
zr[i, :, :] = a * b
return z
# Test _npBatchMatMul works.
def testSimpleNpVersion(self):
x = np.array([0., 1., 2., 3.]).reshape([1, 2, 2])
y = np.array([1., 2., 3., 4.]).reshape([1, 2, 2])
z0 = self._npBatchMatmul(x, y, False, False)
z1 = np.array([3., 4., 11., 16.]).reshape([1, 2, 2])
self.assertTrue(np.array_equal(z0, z1))
x = np.array([1., (1j), (-1.), (-1j)]).reshape([1, 2, 2])
y = x * np.complex(1, 1) # rotate x 90 degree
z0 = self._npBatchMatmul(x, y, False, False)
z1 = np.array([2., (2.j), -2., (-2.j)]).reshape([1, 2, 2])
self.assertTrue(np.array_equal(z0, z1))
z0 = self._npBatchMatmul(x, y, False, True)
z1 = np.array([(2.-2.j), (-2.+2.j), (-2.+2.j), (2.-2.j)]).reshape([1, 2, 2])
self.assertTrue(np.array_equal(z0, z1))
z0 = self._npBatchMatmul(x, y, True, False)
z1 = np.array([(2.+2.j), (-2.+2.j), (2.-2.j), (2.+2.j)]).reshape([1, 2, 2])
self.assertTrue(np.array_equal(z0, z1))
# Compares _tfpBatchMatmul(x, y, alpha, adj) and _npBatchMatMul(x, y, alpha,
# adj)
def _compare(self, x, y, adj_x, adj_y, use_gpu=False):
with self.test_session(use_gpu=use_gpu):
z0 = tf.batch_matmul(x, y, adj_x=adj_x, adj_y=adj_y)
z0_val = z0.eval()
z1 = self._npBatchMatmul(x, y, adj_x, adj_y)
self.assertShapeEqual(z1, z0)
if z0_val.size != 0:
err = (np.abs(z0_val - z1) / np.maximum(1, np.abs(z0_val))).max()
tf.logging.info("error = %f", err)
self.assertTrue(err < 1e-4)
# Returns a random float np of "shape".
def _randFloat(self, shape):
vals = np.random.normal(0, 1, np.prod(shape)).reshape(shape)
return np.array(vals, dtype=np.float32)
def testSimpleFloat(self):
for use_gpu in [False, True]:
self._compare(self._randFloat([7, 2, 3]), self._randFloat([7, 3, 5]),
False, False, use_gpu)
self._compare(self._randFloat([7, 2, 3]), self._randFloat([7, 5, 3]),
False, True, use_gpu)
self._compare(self._randFloat([7, 3, 2]), self._randFloat([7, 3, 5]),
True, False, use_gpu)
self._compare(self._randFloat([7, 3, 2]), self._randFloat([7, 5, 3]),
True, True, use_gpu)
def testLargeFloat(self):
for use_gpu in [False, True]:
self._compare(self._randFloat([10, 64, 75]),
self._randFloat([10, 75, 30]), False, False, use_gpu)
self._compare(self._randFloat([10, 75, 64]),
self._randFloat([10, 75, 30]), True, False, use_gpu)
self._compare(self._randFloat([10, 64, 75]),
self._randFloat([10, 30, 75]), False, True, use_gpu)
self._compare(self._randFloat([10, 75, 64]),
self._randFloat([10, 30, 75]), True, True, use_gpu)
def testHighNDims(self):
for use_gpu in [False, True]:
self._compare(self._randFloat([5, 7, 2, 3]),
self._randFloat([5, 7, 3, 5]), False, False, use_gpu)
self._compare(self._randFloat([5, 7, 3, 2]),
self._randFloat([5, 7, 3, 5]), True, False, use_gpu)
self._compare(self._randFloat([5, 7, 2, 3]),
self._randFloat([5, 7, 5, 3]), False, True, use_gpu)
self._compare(self._randFloat([5, 7, 3, 2]),
self._randFloat([5, 7, 5, 3]), True, True, use_gpu)
# Returns a random complex numpy array of "shape".
def _randComplex(self, shape):
real = np.random.normal(0, 1, np.prod(shape))
imag = np.random.normal(0, 1, np.prod(shape))
vals = [np.complex(v[0], v[1]) for v in zip(real, imag)]
return np.array(vals, dtype=np.complex64).reshape(shape)
def testSimpleComplex(self):
self._compare(self._randComplex([7, 2, 3]),
self._randComplex([7, 3, 5]), False, False)
self._compare(self._randComplex([7, 2, 3]),
self._randComplex([7, 5, 3]), False, True)
self._compare(self._randComplex([7, 3, 2]),
self._randComplex([7, 3, 5]), True, False)
self._compare(self._randComplex([7, 3, 2]),
self._randComplex([7, 5, 3]), True, True)
def testLargeComplex(self):
self._compare(self._randComplex([10, 64, 75]),
self._randComplex([10, 75, 30]), False,
False)
self._compare(self._randComplex([10, 64, 75]),
self._randComplex([10, 30, 75]), False, True)
self._compare(self._randComplex([10, 75, 64]),
self._randComplex([10, 75, 30]), True, False)
self._compare(self._randComplex([10, 75, 64]),
self._randComplex([10, 30, 75]), True, True)
def testEmpty(self):
self._compare(np.empty([0, 3, 2]).astype(np.float32),
np.empty([0, 2, 4]).astype(np.float32), False, False)
self._compare(np.empty([3, 2, 0]).astype(np.float32),
np.empty([3, 0, 5]).astype(np.float32), False, False)
self._compare(np.empty([3, 0, 2]).astype(np.float32),
np.empty([3, 2, 5]).astype(np.float32), False, False)
self._compare(np.empty([3, 3, 2]).astype(np.float32),
np.empty([3, 2, 0]).astype(np.float32), False, False)
class BatchMatmulGradientTest(tf.test.TestCase):
# loss = sum(batch_matmul(x, y)). Verify dl/dx and dl/dy via the
# gradient checker.
def _checkGrad(self, x, y, adj_x, adj_y):
assert 3 == x.ndim
assert 3 == y.ndim
with self.test_session():
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
z = tf.batch_matmul(inx, iny, adj_x, adj_y)
loss = tf.reduce_sum(z)
epsilon = 1e-2
((x_jacob_t, x_jacob_n), (y_jacob_t, y_jacob_n)) = gc.ComputeGradient(
[inx, iny], [x.shape, y.shape], loss, [1],
x_init_value=[x, y], delta=epsilon)
tf.logging.info("x_jacob_t = %s", x_jacob_t.reshape(x.shape))
tf.logging.info("x_jacob_n = %s", x_jacob_n.reshape(x.shape))
self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)
tf.logging.info("y_jacob_t = %s", y_jacob_t.reshape(y.shape))
tf.logging.info("y_jacob_n = %s", y_jacob_n.reshape(y.shape))
self.assertAllClose(y_jacob_t, y_jacob_n, rtol=1e-2, atol=epsilon)
# Tests a batched matmul of x, and y: x is a 3D tensor of shape [b,
# n, k] y is a 3D tensor of shape [b, k, m] the batched matmul
# computes z of shape [b, n, m], where z[i, :, :] = x[i, :, :]
# matmul y[i, :, :]
def _compare(self, b, n, k, m):
x = np.random.normal(0, 1, b * n * k).astype(np.float32).reshape([b, n, k])
y = np.random.normal(0, 1, b * k * m).astype(np.float32).reshape([b, k, m])
self._checkGrad(x, y, False, False)
self._checkGrad(x.reshape([b, k, n]), y, True, False)
self._checkGrad(x, y.reshape([b, m, k]), False, True)
self._checkGrad(x.reshape([b, k, n]), y.reshape([b, m, k]), True, True)
def testSmall(self):
self._compare(1, 2, 3, 5)
def testMedium(self):
self._compare(3, 4, 7, 10)
# Can't do testLarge using very large inputs because gradient
# checker will take way too long time.
if __name__ == "__main__":
tf.test.main()
|
