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# Copyright 2018 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 low-level eager execution primitives."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import core
from tensorflow.python.eager import test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import resource_variable_ops
class Tests(test.TestCase):
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_MatMulCorrectResponse(self):
a_2_by_2 = random_ops.random_uniform((2, 2))
b_2_by_2 = random_ops.random_uniform((2, 2))
a_100_by_784 = random_ops.random_uniform((100, 784))
b_100_by_784 = random_ops.random_uniform((100, 784))
ctx = context.context()
self.assertAllClose(
math_ops.matmul(a_2_by_2, b_2_by_2),
pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2,
b_2_by_2, "transpose_a", False, "transpose_b", False))
self.assertAllClose(
math_ops.matmul(a_100_by_784, b_100_by_784, transpose_b=True),
pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_100_by_784,
b_100_by_784, "transpose_a", False, "transpose_b", True))
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_ResourceVariableMatMulCorrectResponse(self):
ctx = context.context()
a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
m = resource_variable_ops.ResourceVariable(a_2_by_2)
x = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a",
False, "transpose_b", False)
y = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, a_2_by_2,
"transpose_a", False, "transpose_b", False)
self.assertAllEqual(x, y)
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_MixedPrecisionVariableMatMulCorrectResponse(self):
ctx = context.context()
a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
m = resource_variable_ops.ResourceVariable(a_2_by_2)
m = resource_variable_ops._MixedPrecisionVariable(
m, read_dtype=dtypes.float16)
x = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a",
False, "transpose_b", False)
y = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16,
a_2_by_2_fp16, "transpose_a", False, "transpose_b", False)
self.assertEqual(x.dtype, dtypes.float16)
self.assertAllEqual(x, y)
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_TapeWrite(self):
ctx = context.context()
with backprop.GradientTape(persistent=True) as tape:
a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
tape.watch(a_2_by_2)
z = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2,
a_2_by_2, "transpose_a", False, "transpose_b", False)
dz_dy = tape.gradient(z, [a_2_by_2])[0]
self.assertAllEqual(dz_dy.numpy(),
constant_op.constant(4.0, shape=[2, 2]).numpy())
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_ResourceVariableTapeWrite(self):
ctx = context.context()
with backprop.GradientTape(persistent=True) as tape:
a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
m = resource_variable_ops.ResourceVariable(a_2_by_2)
tape.watch(m)
z = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, m, m,
"transpose_a", False, "transpose_b", False)
dz_dy = tape.gradient(z, [m])[0]
self.assertAllEqual(dz_dy.numpy(),
constant_op.constant(4.0, shape=[2, 2]).numpy())
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_MixedPrecisionVariableTapeWrite(self):
ctx = context.context()
with backprop.GradientTape(persistent=True) as tape:
a_2_by_2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]],
dtype=dtypes.float32)
a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
m1 = resource_variable_ops.ResourceVariable(a_2_by_2)
m2 = resource_variable_ops._MixedPrecisionVariable(
m1, read_dtype=dtypes.float16)
tape.watch(m2)
z = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16, m2,
"transpose_a", False, "transpose_b", False)
dz_dy = tape.gradient(z, [m2])[0]
self.assertEqual(dz_dy.dtype, dtypes.float16)
expected_grads = math_ops.matmul(
array_ops.transpose(a_2_by_2_fp16),
constant_op.constant(1., shape=[2, 2], dtype=dtypes.float16)).numpy()
self.assertAllEqual(dz_dy.numpy(), expected_grads)
# Tests homogeneous list op
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_AddNCorrectResponse(self):
ctx = context.context()
a_2_by_2 = random_ops.random_uniform((2, 2))
b_2_by_2 = random_ops.random_uniform((2, 2))
self.assertAllClose(
math_ops.add_n([a_2_by_2, b_2_by_2]),
pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
"AddN", None, None,
[a_2_by_2, b_2_by_2]))
# Tests homogeneous list op
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_AddNTapeWrite(self):
ctx = context.context()
a_2_by_2 = random_ops.random_uniform((2, 2))
b_2_by_2 = random_ops.random_uniform((2, 2))
with backprop.GradientTape(persistent=True) as tape:
tape.watch(a_2_by_2)
tape.watch(b_2_by_2)
z1 = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "AddN", None, None,
[a_2_by_2, b_2_by_2])
z2 = math_ops.add_n([a_2_by_2, b_2_by_2])
dz1_dy = tape.gradient(z1, [a_2_by_2])[0]
dz2_dy = tape.gradient(z2, [a_2_by_2])[0]
self.assertAllEqual(dz1_dy.numpy(), dz2_dy.numpy())
# Tests heterogeneous list op
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_IdentityNCorrectResponse(self):
ctx = context.context()
a_2_by_2 = random_ops.random_uniform((2, 2))
b_2_by_2 = random_ops.random_uniform((2, 2))
self.assertAllClose(
array_ops.identity_n([a_2_by_2, b_2_by_2]),
pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
"IdentityN", None, None,
[a_2_by_2, b_2_by_2]))
# Tests heterogeneous list op
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_IdentityNTapeWrite(self):
ctx = context.context()
a_2_by_2 = random_ops.random_uniform((2, 2))
b_2_by_2 = random_ops.random_uniform((2, 2))
with backprop.GradientTape(persistent=True) as tape:
tape.watch(a_2_by_2)
tape.watch(b_2_by_2)
z1 = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "IdentityN", None, None,
[a_2_by_2, b_2_by_2])
z2 = array_ops.identity_n([a_2_by_2, b_2_by_2])
dz1_dy = tape.gradient(z1[0], [a_2_by_2])[0]
dz2_dy = tape.gradient(z2[0], [a_2_by_2])[0]
self.assertAllEqual(dz1_dy.numpy(), dz2_dy.numpy())
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastpathExecute_InvalidInputs(self):
a_2_by_2 = random_ops.random_uniform((2, 2))
ctx = context.context()
assert ctx.executing_eagerly(
), "The prototype doesn't contain C code for graph construction"
ctx_handle = ctx._handle # pylint: disable=protected-access
# Not enough base params
with self.assertRaisesRegexp(ValueError,
"at least 5 items in the input tuple"):
pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name,
"Identity")
# Not enough inputs
with self.assertRaisesRegexp(ValueError,
"Expected to be at least 6, was 5"):
pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx_handle,
"Identity", None, [])
# Bad type
with self.assertRaisesRegexp(TypeError, "expected a string for op_name"):
pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name,
ctx_handle, None, [], a_2_by_2)
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testFastPathExecute_InvalidAttributes(self):
split_dim = constant_op.constant(0, dtype=dtypes.int32)
value = constant_op.constant([0, 1, 2, 3], dtype=dtypes.float32)
ctx = context.context()
ctx_handle = ctx._handle
with self.assertRaises(core._FallbackException):
pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name,
"Split", None, None, split_dim,
value, "num_split", -1)
@test_util.assert_no_new_tensors
@test_util.assert_no_garbage_created
def testInvalidNumOutputs(self):
with self.assertRaisesRegexp(
Exception,
"Value for attr 'num_split' of -1 must be at least minimum 1"):
array_ops.split(value=[1, 2, 3], num_or_size_splits=-1)
if __name__ == "__main__":
test.main()
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