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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.math_ops.matmul."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import operator
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test as test_lib
# TODO(yangzihao): Currently matmul autotuning is disabled by default. Use
# os.environ["TF_MATMUL_AUTOTUNE_ENABLE"] = "1" to enable it.
def _AddTest(test, op_name, testcase_name, fn):
test_name = "_".join(["test", op_name, testcase_name])
if hasattr(test, test_name):
raise RuntimeError("Test %s defined more than once" % test_name)
setattr(test, test_name, fn)
def _GetTransposedMatrices(x, x_name, kwargs):
if kwargs["transpose_" + x_name] is True:
return x.T
elif kwargs["adjoint_" + x_name] is True:
return np.conj(x.T)
else:
return x
class MatMulTest(test_lib.TestCase):
pass # Filled in below
def _GetMatMulTest(a_np_, b_np_, use_static_shape_, **kwargs_):
def Test(self):
np_val = np.matrix(a_np_) * np.matrix(b_np_)
use_gpu = True
if a_np_.dtype is np.float16 and (
not test_util.CudaSupportsHalfMatMulAndConv()):
use_gpu = False
print("Built without fp16 matmul support for Cuda, running test on CPU.")
# Transpose and possibly conjugate a_np_ and b_np_ according to the
# attributes such that tf.matmul(effective_a_np, effective_b_np, **kwargs)
# results in a valid matrix multiplication and produces the same result as
# np.matrix(a_np_) * np.matrix(b_np_)
effective_a_np = _GetTransposedMatrices(a_np_, "a", kwargs_)
effective_b_np = _GetTransposedMatrices(b_np_, "b", kwargs_)
with self.test_session(use_gpu=use_gpu) as sess:
if use_static_shape_:
a = constant_op.constant(effective_a_np)
b = constant_op.constant(effective_b_np)
res = math_ops.matmul(a, b, **kwargs_)
tf_val = res.eval()
else:
a = array_ops.placeholder(a_np_.dtype)
b = array_ops.placeholder(b_np_.dtype)
res = math_ops.matmul(a, b, **kwargs_)
tf_val = sess.run(res, feed_dict={a: effective_a_np, b: effective_b_np})
self.assertAllCloseAccordingToType(
tf_val,
np_val,
float_rtol=2e-5,
float_atol=2e-5,
half_rtol=0.2,
half_atol=0.2)
return Test
class MatMulGradientTest(test_lib.TestCase):
pass # Will be filled in below.
def _GetMatMulGradientTest(a_np_, b_np_, use_static_shape_, **kwargs_):
def Test(self):
if not use_static_shape_ or a_np_.dtype in (np.int32, np.float16):
self.skipTest("Skipping infeasible gradient test.")
# Transpose and possibly conjugate a_np_ and b_np_ according to the
# attributes such that tf.matmul(effective_a_np, effective_b_np, **kwargs)
# results in a valid matrix multiplication and produces the same result as
# np.matrix(a_np_) * np.matrix(b_np_)
effective_a_np = _GetTransposedMatrices(a_np_, "a", kwargs_)
effective_b_np = _GetTransposedMatrices(b_np_, "b", kwargs_)
epsilon = np.finfo(a_np_.dtype).eps
delta = epsilon**(1.0 / 3.0)
tol = 20 * delta
with self.test_session(use_gpu=True):
a = constant_op.constant(effective_a_np)
b = constant_op.constant(effective_b_np)
res = math_ops.matmul(a, b, **kwargs_)
for x, x_init in [a, effective_a_np], [b, effective_b_np]:
theoretical, numerical = gradient_checker.compute_gradient(
x,
x_init.shape,
res, [a_np_.shape[0], b_np_.shape[1]],
x_init_value=x_init,
delta=delta)
self.assertAllClose(theoretical, numerical, rtol=tol, atol=tol)
return Test
class MatMulStatsTest(test_lib.TestCase):
def testSimpleStatistics(self):
g = ops.Graph()
with g.as_default():
a = variables.Variable(random_ops.random_normal([25, 16]))
b = variables.Variable(random_ops.random_normal([16, 9]))
math_ops.matmul(a, b)
for op in g.get_operations():
flops = ops.get_stats_for_node_def(g, op.node_def, "flops").value
if op.name == "MatMul":
self.assertEqual(7200, flops)
def testTransposedStatistics(self):
g = ops.Graph()
with g.as_default():
a = variables.Variable(random_ops.random_normal([16, 25]))
b = variables.Variable(random_ops.random_normal([16, 9]))
math_ops.matmul(a, b, transpose_a=True)
for op in g.get_operations():
flops = ops.get_stats_for_node_def(g, op.node_def, "flops").value
if op.name == "MatMul":
self.assertEqual(7200, flops)
try:
# @ operator supported since python 3.5.
infix_matmul = operator.matmul
except AttributeError:
# For earlier versions of python, emulate regular behavior.
# Useful to build and test for 3.5+ on earlier versions.
def infix_matmul(x, y): # pylint: disable=invalid-name
try:
r = type(x).__matmul__(x, y)
except AttributeError:
r = NotImplemented
if r is NotImplemented and type(x) is not type(y):
try:
r = type(y).__rmatmul__(y, x)
except AttributeError:
r = NotImplemented
if r is NotImplemented:
raise TypeError("unsupported operand type(s) for @: '{}' and '{}'"
.format(type(x).__name__, type(y).__name__))
return r
class MatMulInfixOperatorTest(test_lib.TestCase):
def testMismatchedShape(self):
with self.assertRaisesWithPredicateMatch(ValueError,
lambda e: "Shape must" in str(e)):
infix_matmul(
ops.convert_to_tensor([10.0, 20.0, 30.0]),
ops.convert_to_tensor([[40.0, 50.0], [60.0, 70.0]]))
def testMismatchedDimensions(self):
with self.assertRaisesWithPredicateMatch(
ValueError, lambda e: "Dimensions must" in str(e)):
infix_matmul(
ops.convert_to_tensor([[10.0, 20.0, 30.0]]),
ops.convert_to_tensor([[40.0, 50.0], [60.0, 70.0]]))
def testInfixMatmulIsTfMatmul(self):
a = ops.convert_to_tensor([[10.0, 20.0, 30.0]])
b = ops.convert_to_tensor([[40.0, 50.0], [60.0, 70.0], [80.0, 90.0]])
c = infix_matmul(a, b)
self.assertEqual(c.op.type, "MatMul")
def testInfixMatmulDoesDotProduct(self):
a = ops.convert_to_tensor([[10.0, 20.0, 30.0]])
b = ops.convert_to_tensor([[40.0, 50.0], [60.0, 70.0], [80.0, 90.0]])
c = infix_matmul(a, b)
d = math_ops.matmul(a, b)
with self.cached_session():
self.assertAllEqual(c.eval(), d.eval())
if __name__ == "__main__":
sizes = [1, 3, 5]
trans_options = [[False, False], [True, False], [False, True]]
for use_static_shape in [False, True]:
for dtype in (np.int32, np.float16, np.float32, np.float64, np.complex64,
np.complex128):
if not use_static_shape and dtype == np.int32:
# TODO(rmlarsen): Re-enable this test when we have fixed the underlying
# bug in Windows (b/35935459).
continue
for m in sizes:
for n in sizes:
for k in sizes:
# Construct compatible random matrices a_np of size [m, k] and b_np
# of size [k, n].
a_np = np.random.normal(-5, 5, m * k).astype(dtype).reshape([m, k])
if dtype in (np.complex64, np.complex128):
a_np.imag = np.random.normal(-5, 5,
m * k).astype(dtype).reshape([m, k])
b_np = np.random.normal(-5, 5, k * n).astype(dtype).reshape([k, n])
if dtype in (np.complex64, np.complex128):
b_np.imag = np.random.normal(-5, 5,
k * n).astype(dtype).reshape([k, n])
for adjoint_a, transpose_a in trans_options:
for adjoint_b, transpose_b in trans_options:
name = "%s_%s_%s_%s_%s_%s_%s_%s_%s" % (
use_static_shape, dtype.__name__, m, n, k, adjoint_a,
transpose_a, adjoint_b, transpose_b)
_AddTest(MatMulTest, "MatMulTest", name,
_GetMatMulTest(
a_np,
b_np,
use_static_shape,
adjoint_a=adjoint_a,
transpose_a=transpose_a,
adjoint_b=adjoint_b,
transpose_b=transpose_b))
_AddTest(MatMulGradientTest, "MatMulGradientTest", name,
_GetMatMulGradientTest(
a_np,
b_np,
use_static_shape,
adjoint_a=adjoint_a,
transpose_a=transpose_a,
adjoint_b=adjoint_b,
transpose_b=transpose_b))
test_lib.main()
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