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# Copyright 2015 Google Inc. 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 numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
class MatMulTest(tf.test.TestCase):
def _testCpuMatmul(self, x, y, transpose_x=False, transpose_y=False):
x_mat = np.matrix(x).T if transpose_x else np.matrix(x)
y_mat = np.matrix(y).T if transpose_y else np.matrix(y)
np_ans = x_mat * y_mat
with self.test_session(use_gpu=False):
tf_ans = tf.matmul(x, y, transpose_x, transpose_y).eval()
self.assertAllClose(np_ans, tf_ans)
self.assertAllEqual(np_ans.shape, tf_ans.shape)
def _testGpuMatmul(self, x, y, transpose_x=False, transpose_y=False):
x_mat = np.matrix(x).T if transpose_x else np.matrix(x)
y_mat = np.matrix(y).T if transpose_y else np.matrix(y)
np_ans = x_mat * y_mat
with self.test_session(use_gpu=True):
tf_ans = tf.matmul(x, y, transpose_x, transpose_y).eval()
self.assertAllClose(np_ans, tf_ans)
self.assertAllEqual(np_ans.shape, tf_ans.shape)
def _randMatrix(self, rows, cols, dtype):
if dtype is np.complex64:
real = self._randMatrix(rows, cols, np.float32)
imag = self._randMatrix(rows, cols, np.float32)
return real + np.complex(0, 1) * imag
else:
return np.random.uniform(low=1.0, high=100.0, size=rows * cols).reshape(
[rows, cols]).astype(dtype)
# Basic test:
# [ [1],
# [2],
# [3], * [1, 2]
# [4] ]
def testFloatBasic(self):
x = np.arange(1., 5.).reshape([4, 1]).astype(np.float32)
y = np.arange(1., 3.).reshape([1, 2]).astype(np.float32)
self._testCpuMatmul(x, y)
self._testGpuMatmul(x, y)
def testDoubleBasic(self):
x = np.arange(1., 5.).reshape([4, 1]).astype(np.float64)
y = np.arange(1., 3.).reshape([1, 2]).astype(np.float64)
self._testCpuMatmul(x, y)
def testInt32Basic(self):
x = np.arange(1., 5.).reshape([4, 1]).astype(np.int32)
y = np.arange(1., 3.).reshape([1, 2]).astype(np.int32)
self._testCpuMatmul(x, y)
def testSComplexBasic(self):
x = np.arange(1., 5.).reshape([4, 1]).astype(np.complex64)
y = np.arange(1., 3.).reshape([1, 2]).astype(np.complex64)
self._testCpuMatmul(x, y)
# Tests testing random sized matrices.
def testFloatRandom(self):
for _ in range(10):
n, k, m = np.random.randint(1, 100, size=3)
x = self._randMatrix(n, k, np.float32)
y = self._randMatrix(k, m, np.float32)
self._testCpuMatmul(x, y)
self._testGpuMatmul(x, y)
def testDoubleRandom(self):
for _ in range(10):
n, k, m = np.random.randint(1, 100, size=3)
x = self._randMatrix(n, k, np.float64)
y = self._randMatrix(k, m, np.float64)
self._testCpuMatmul(x, y)
def testInt32Random(self):
for _ in range(10):
n, k, m = np.random.randint(1, 100, size=3)
x = self._randMatrix(n, k, np.int32)
y = self._randMatrix(k, m, np.int32)
self._testCpuMatmul(x, y)
def testSComplexRandom(self):
for _ in range(10):
n, k, m = np.random.randint(1, 100, size=3)
x = self._randMatrix(n, k, np.complex64)
y = self._randMatrix(k, m, np.complex64)
self._testCpuMatmul(x, y)
# Test the cases that transpose the matrices before multiplying.
# NOTE(keveman): The cases where only one of the inputs is
# transposed are covered by tf.matmul's gradient function.
def testFloatRandomTransposeBoth(self):
for _ in range(10):
n, k, m = np.random.randint(1, 100, size=3)
x = self._randMatrix(k, n, np.float32)
y = self._randMatrix(m, k, np.float32)
self._testCpuMatmul(x, y, True, True)
self._testGpuMatmul(x, y, True, True)
def testDoubleRandomTransposeBoth(self):
for _ in range(10):
n, k, m = np.random.randint(1, 100, size=3)
x = self._randMatrix(k, n, np.float64)
y = self._randMatrix(m, k, np.float64)
self._testCpuMatmul(x, y, True, True)
def testMatMul_OutEmpty_A(self):
n, k, m = 0, 8, 3
x = self._randMatrix(n, k, np.float32)
y = self._randMatrix(k, m, np.float32)
self._testCpuMatmul(x, y)
self._testGpuMatmul(x, y)
def testMatMul_OutEmpty_B(self):
n, k, m = 3, 8, 0
x = self._randMatrix(n, k, np.float32)
y = self._randMatrix(k, m, np.float32)
self._testCpuMatmul(x, y)
self._testGpuMatmul(x, y)
def testMatMul_Inputs_Empty(self):
n, k, m = 3, 0, 4
x = self._randMatrix(n, k, np.float32)
y = self._randMatrix(k, m, np.float32)
self._testCpuMatmul(x, y)
self._testGpuMatmul(x, y)
def testShapeErrors(self):
a = tf.placeholder(tf.float32, [32, 37])
b = tf.placeholder(tf.float32, [36, 2])
c = tf.placeholder(tf.float32, [37])
with self.assertRaisesRegexp(
ValueError,
r"Dimensions Dimension\(37\) and Dimension\(36\) are not compatible"):
tf.matmul(a, b)
with self.assertRaisesRegexp(ValueError, "must have rank 2"):
tf.matmul(a, c)
# TODO(zhifengc): Figures out how to test matmul gradients on GPU.
class MatMulGradientTest(tf.test.TestCase):
def testGradientInput0(self):
with self.test_session(use_gpu=False):
x = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
dtype=tf.float64, name="x")
y = tf.constant([1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7],
shape=[2, 4], dtype=tf.float64, name="y")
m = tf.matmul(x, y, name="matmul")
err = tf.test.compute_gradient_error(x, [3, 2], m, [3, 4])
print("matmul input0 gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientInput1(self):
with self.test_session(use_gpu=False):
x = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
dtype=tf.float64, name="x")
y = tf.constant([1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7],
shape=[2, 4], dtype=tf.float64, name="y")
m = tf.matmul(x, y, name="matmul")
err = tf.test.compute_gradient_error(y, [2, 4], m, [3, 4])
print("matmul input1 gradient err = ", err)
self.assertLess(err, 1e-10)
def _VerifyInput0(self, transpose_a, transpose_b):
shape_x = [3, 2]
shape_y = [2, 4]
if transpose_a:
shape_x = list(reversed(shape_x))
if transpose_b:
shape_y = list(reversed(shape_y))
with self.test_session(use_gpu=False):
x = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=shape_x,
dtype=tf.float64, name="x")
y = tf.constant([1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7],
shape=shape_y, dtype=tf.float64, name="y")
m = tf.matmul(x, y, transpose_a, transpose_b, name="matmul")
err = tf.test.compute_gradient_error(x, shape_x, m, [3, 4])
print("matmul input0 gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientInput0WithTranspose(self):
self._VerifyInput0(transpose_a=True, transpose_b=False)
self._VerifyInput0(transpose_a=False, transpose_b=True)
self._VerifyInput0(transpose_a=True, transpose_b=True)
def _VerifyInput1(self, transpose_a, transpose_b):
shape_x = [3, 2]
shape_y = [2, 4]
if transpose_a:
shape_x = list(reversed(shape_x))
if transpose_b:
shape_y = list(reversed(shape_y))
with self.test_session(use_gpu=False):
x = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=shape_x,
dtype=tf.float64, name="x")
y = tf.constant([1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7],
shape=shape_y, dtype=tf.float64, name="y")
m = tf.matmul(x, y, transpose_a, transpose_b, name="matmul")
err = tf.test.compute_gradient_error(y, shape_y, m, [3, 4])
print("matmul input1 gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientInput1WithTranspose(self):
self._VerifyInput1(transpose_a=True, transpose_b=False)
self._VerifyInput1(transpose_a=False, transpose_b=True)
self._VerifyInput1(transpose_a=True, transpose_b=True)
class MatMulStatsTest(tf.test.TestCase):
def testSimpleStatistics(self):
g = tf.Graph()
with g.as_default():
a = tf.Variable(tf.random_normal([25, 16]))
b = tf.Variable(tf.random_normal([16, 9]))
tf.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 = tf.Graph()
with g.as_default():
a = tf.Variable(tf.random_normal([16, 25]))
b = tf.Variable(tf.random_normal([16, 9]))
tf.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)
if __name__ == "__main__":
tf.test.main()
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