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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.matmul."""
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.framework import dtypes
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
def RandMatrix(rows, cols, tr, round_bfloat=False):
if tr:
rows, cols = cols, rows
rand_func = np.random.randint if round_bfloat else np.random.uniform
return (np.clip(
rand_func(
low=-256.0, high=256.0, size=rows * cols), -64,
64) / 128.0).reshape([rows, cols]).astype(np.float32)
class SparseMatMulTest(test.TestCase):
def _testCpuMatmul(self,
x,
y,
tr_a=False,
tr_b=False,
sp_a=True,
sp_b=False,
x_dtype=dtypes.float32,
y_dtype=dtypes.float32):
with self.test_session(use_gpu=False):
tf_x = math_ops.cast(x, x_dtype)
tf_y = math_ops.cast(y, y_dtype)
tf_ans = math_ops.matmul(
tf_x,
tf_y,
transpose_a=tr_a,
transpose_b=tr_b,
a_is_sparse=sp_a,
b_is_sparse=sp_b)
out = tf_ans.eval()
np_x = math_ops.cast(tf_x, dtypes.float32).eval()
np_y = math_ops.cast(tf_y, dtypes.float32).eval()
if tr_a:
np_x = np.transpose(np_x)
if tr_b:
np_y = np.transpose(np_y)
np_ans = np.matrix(np_x) * np.matrix(np_y)
self.assertShapeEqual(np_ans, tf_ans)
self.assertAllCloseAccordingToType(np_ans, out, rtol=1e-4, atol=1e-4)
def testBasic(self):
x = np.arange(0., 4.).reshape([4, 1]).astype(np.float32)
y = np.arange(-1., 1.).reshape([1, 2]).astype(np.float32)
for x_dtype in (dtypes.float32, dtypes.bfloat16):
for y_dtype in (dtypes.float32, dtypes.bfloat16):
self._testCpuMatmul(x, y, x_dtype=x_dtype, y_dtype=y_dtype)
def testZeroDim(self):
x = np.ones((4, 0)).astype(np.float32)
y = np.ones((0, 3)).astype(np.float32)
for x_dtype in (dtypes.float32, dtypes.bfloat16):
for y_dtype in (dtypes.float32, dtypes.bfloat16):
self._testCpuMatmul(x, y, x_dtype=x_dtype, y_dtype=y_dtype)
def testEmpty(self):
x = np.ones((0, 0)).astype(np.float32)
y = np.ones((0, 0)).astype(np.float32)
for x_dtype in (dtypes.float32, dtypes.bfloat16):
for y_dtype in (dtypes.float32, dtypes.bfloat16):
self._testCpuMatmul(x, y, x_dtype=x_dtype, y_dtype=y_dtype)
# Tests setting one dimension to be a high value.
def testLarge(self):
r1 = np.random.randint(6000, 20000)
r2 = np.random.randint(1, 10)
r3 = np.random.randint(1, 10)
for m, k, n in [(r1, r2, r3), (r2, r1, r3), (r2, r3, r1)]:
for x_dtype in (dtypes.float32, dtypes.bfloat16):
for y_dtype in (dtypes.float32, dtypes.bfloat16):
x = RandMatrix(m, k, False)
y = RandMatrix(k, n, False)
self._testCpuMatmul(x, y, x_dtype=x_dtype, y_dtype=y_dtype)
# Tests random sized matrices.
def testRandom(self):
for tr_a in [True, False]:
for tr_b in [True, False]:
for sp_a in [True, False]:
for sp_b in [True, False]:
for x_dtype in (dtypes.float32, dtypes.bfloat16):
for y_dtype in (dtypes.float32, dtypes.bfloat16):
n, k, m = np.random.randint(1, 100, size=3)
x = RandMatrix(n, k, tr_a)
y = RandMatrix(k, m, tr_b)
self._testCpuMatmul(
x,
y,
tr_a,
tr_b,
sp_a,
sp_b,
x_dtype=x_dtype,
y_dtype=y_dtype)
class MatMulGradientTest(test.TestCase):
def _testGradients(self, tr_a, tr_b, sp_a, sp_b, a_dtype, b_dtype, delta,
name):
with self.cached_session():
a = constant_op.constant(
RandMatrix(
3, 2, tr_a, round_bfloat=True), dtype=dtypes.float32)
b = constant_op.constant(
RandMatrix(
2, 4, tr_b, round_bfloat=True), dtype=dtypes.float32)
tf_a = math_ops.cast(a, a_dtype) if a_dtype != dtypes.float32 else a
tf_b = math_ops.cast(b, b_dtype) if b_dtype != dtypes.float32 else b
m = math_ops.matmul(
tf_a,
tf_b,
name=name,
transpose_a=tr_a,
transpose_b=tr_b,
a_is_sparse=sp_a,
b_is_sparse=sp_b)
err = (gradient_checker.compute_gradient_error(
a, [2, 3] if tr_a else [3, 2],
m, [3, 4],
x_init_value=a.eval(),
delta=delta) + gradient_checker.compute_gradient_error(
b, [4, 2] if tr_b else [2, 4],
m, [3, 4],
x_init_value=b.eval(),
delta=delta))
self.assertLessEqual(err, delta / 2.)
def testGradientInput(self):
for tr_a in [True, False]:
for tr_b in [True, False]:
for sp_a in [True, False]:
for sp_b in [True, False]:
for a_dtype in (dtypes.float32, dtypes.bfloat16):
for b_dtype in (dtypes.float32, dtypes.bfloat16):
# Note: bfloat16 only has 7 mantissa bits, versus float32 with
# 10. Hence, we shift by 2 bits to pass the test.
if a_dtype == dtypes.bfloat16 and b_dtype == dtypes.bfloat16:
delta = 1 / 16.
else:
delta = 1 / 64.
name = "sparse_matmul_%s_%s_%s_%s" % (tr_a, tr_b, sp_a, sp_b)
self._testGradients(tr_a, tr_b, sp_a, sp_b, a_dtype, b_dtype,
delta, name)
if __name__ == "__main__":
test.main()
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