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# 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.
# ==============================================================================
"""Tests for tensorflow.ops.tf.BatchMatMul."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class BatchMatmulOpTest(test.TestCase):
# Uses numpy to compute batch_matmul(x, y, adjoint_a, adjoint_b).
def _npBatchMatmul(self, x, y, adjoint_a, adjoint_b):
# output's shape depends on adj[0] and adj[1]
d0 = x.shape[-2] if not adjoint_a else x.shape[-1]
d2 = y.shape[-1] if not adjoint_b 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 adjoint_a:
a = a.transpose().conj()
b = np.matrix(yr[i, :, :])
if adjoint_b:
b = b.transpose().conj()
zr[i, :, :] = a * b
return z
# Test _npBatchMatMul works.
def testNpVersion(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_in, y_in, adjoint_a, adjoint_b, static_shape=True):
x_t_shape = x_in.shape[:-2] + (x_in.shape[-1], x_in.shape[-2])
y_t_shape = y_in.shape[:-2] + (y_in.shape[-1], y_in.shape[-2])
x = x_in if not adjoint_a else x_in.reshape(x_t_shape)
y = y_in if not adjoint_b else y_in.reshape(y_t_shape)
is_floating = x.dtype != np.int32
tol = 100 * np.finfo(x.dtype).eps if is_floating else 0
with self.test_session(use_gpu=is_floating) as sess:
if static_shape:
z0 = math_ops.matmul(x, y, adjoint_a=adjoint_a, adjoint_b=adjoint_b)
z0_val = z0.eval()
else:
x_ph = array_ops.placeholder(x.dtype)
y_ph = array_ops.placeholder(y.dtype)
z0 = math_ops.matmul(
x_ph, y_ph, adjoint_a=adjoint_a, adjoint_b=adjoint_b)
z0_val = sess.run(z0, feed_dict={x_ph: x, y_ph: y})
z1 = self._npBatchMatmul(x, y, adjoint_a, adjoint_b)
self.assertAllClose(z0_val, z1, rtol=tol, atol=tol)
def _rand(self, shape, dtype):
vals = np.array(np.random.normal(-10, 10, np.prod(shape)), dtype=dtype)
if dtype in (np.complex64, np.complex128):
imag = np.array(np.random.normal(-10, 10, np.prod(shape)), dtype=dtype)
vals += 1j * imag
return vals.reshape(shape)
def _testNonEmpty(self, dtype, adjoint_a, adjoint_b, use_static_shape):
def compareNonEmpty(self, a_shape, b_shape):
self._compare(
self._rand(a_shape, dtype),
self._rand(b_shape, dtype), adjoint_a, adjoint_b, use_static_shape)
compareNonEmpty(self, [1, 2, 3], [1, 3, 5])
compareNonEmpty(self, [1, 2, 3], [1, 3, 1])
compareNonEmpty(self, [1, 1, 3], [1, 3, 5])
compareNonEmpty(self, [1, 2, 3], [1, 3, 5])
compareNonEmpty(self, [7, 1, 3], [7, 3, 5])
compareNonEmpty(self, [7, 2, 3], [7, 3, 1])
compareNonEmpty(self, [7, 2, 3], [7, 3, 5])
compareNonEmpty(self, [10, 64, 75], [10, 75, 30])
compareNonEmpty(self, [5, 7, 2, 3], [5, 7, 3, 5])
def _testEmpty(self, dtype, adjoint_a, adjoint_b, use_static_shape):
def compareEmpty(self, a_shape, b_shape):
self._compare(
np.zeros(a_shape).astype(dtype),
np.zeros(b_shape).astype(dtype), adjoint_a, adjoint_b,
use_static_shape)
compareEmpty(self, [0, 3, 2], [0, 2, 4])
compareEmpty(self, [3, 0, 2], [3, 2, 5])
compareEmpty(self, [3, 3, 2], [3, 2, 0])
def _GetBatchMatmulOpTest(dtype, adjoint_a, adjoint_b, use_static_shape):
def Test(self):
np.random.seed(42)
self._testNonEmpty(dtype, adjoint_a, adjoint_b, use_static_shape)
self._testEmpty(dtype, adjoint_a, adjoint_b, use_static_shape)
return Test
class BatchMatmulGradientTest(test.TestCase):
# loss = sum(batch_matmul(x, y)). Verify dl/dx and dl/dy via the
# gradient checker.
def _checkGrad(self, x_in, y_in, adjoint_a, adjoint_b):
x_t_shape = x_in.shape[:-2] + (x_in.shape[-1], x_in.shape[-2])
y_t_shape = y_in.shape[:-2] + (y_in.shape[-1], y_in.shape[-2])
x = x_in if not adjoint_a else x_in.reshape(x_t_shape)
y = y_in if not adjoint_b else y_in.reshape(y_t_shape)
epsilon = np.finfo(x.dtype).eps
delta = epsilon**(1.0 / 3.0)
with self.test_session(use_gpu=True):
inx = constant_op.constant(x)
iny = constant_op.constant(y)
z = math_ops.matmul(inx, iny, adjoint_a, adjoint_b)
loss = math_ops.reduce_sum(z)
((x_jacob_t, x_jacob_n),
(y_jacob_t, y_jacob_n)) = gradient_checker.compute_gradient(
[inx, iny], [x.shape, y.shape],
loss, [1],
x_init_value=[x, y],
delta=delta)
tol = 20 * delta
self.assertAllClose(x_jacob_t, x_jacob_n, rtol=tol, atol=tol)
self.assertAllClose(y_jacob_t, y_jacob_n, rtol=tol, atol=tol)
# 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, dtype, adjoint_a, adjoint_b):
np.random.seed(42)
x = np.random.normal(0, 1, b * n * k).astype(dtype).reshape([b, n, k])
if dtype in (np.complex64, np.complex128):
x.imag = np.random.normal(0, 1,
b * n * k).astype(dtype).reshape([b, n, k])
y = np.random.normal(0, 1, b * k * m).astype(dtype).reshape([b, k, m])
if dtype in (np.complex64, np.complex128):
y.imag = np.random.normal(0, 1,
b * k * m).astype(dtype).reshape([b, k, m])
self._checkGrad(x, y, adjoint_a, adjoint_b)
def _GetBatchMatmulGradientTest(dtype, adjoint_a, adjoint_b):
def Test(self):
self._compare(1, 2, 3, 5, dtype, adjoint_a, adjoint_b)
self._compare(3, 4, 7, 10, dtype, adjoint_a, adjoint_b)
return Test
if __name__ == "__main__":
for dtype_ in [
np.float16, np.float32, np.float64, np.complex64, np.complex128, np.int32
]:
for adjoint_a_ in False, True:
for adjoint_b_ in False, True:
name = "%s_%s_%s" % (dtype_.__name__, adjoint_a_, adjoint_b_)
for use_static_shape in True, False:
setattr(BatchMatmulOpTest,
"testBatchMatmulOp_" + name + ("_%s" % use_static_shape),
_GetBatchMatmulOpTest(dtype_, adjoint_a_, adjoint_b_,
use_static_shape))
if dtype_ is not np.int32:
setattr(BatchMatmulGradientTest, "testBatchMatmulGradient_" + name,
_GetBatchMatmulGradientTest(dtype_, adjoint_a_, adjoint_b_))
test.main()
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