tensorflow/tensorflow/python/eager/core_test.py at master · codeplaysoftware/tensorflow · GitHub

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# 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 core."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import threading

import numpy as np

from tensorflow.core.protobuf import config_pb2

from tensorflow.python import pywrap_tensorflow

from tensorflow.python.eager import context

from tensorflow.python.eager import core

from tensorflow.python.eager import execute as execute_lib

from tensorflow.python.eager import test

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import errors

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 gen_resource_variable_ops

from tensorflow.python.ops import nn_ops

from tensorflow.python.ops import resource_variable_ops

def execute(op_name, num_outputs, inputs, attrs=None):

return execute_lib.execute(

op_name, num_outputs, inputs, attrs, context.context())

def truncated_normal(shape):

return execute(

b'TruncatedNormal',

inputs=[shape],

attrs=('dtype', dtypes.float32.as_datatype_enum, 'T',

shape.dtype.as_datatype_enum, 'seed', 0, 'seed2', 0))[0]

class TFETest(test_util.TensorFlowTestCase):

def testContext(self):

ctx = context.Context()

self.assertTrue(ctx.executing_eagerly())

self.assertEqual('', ctx.scope_name)

ctx.scope_name = 'foo'

self.assertEqual('foo', ctx.scope_name)

self.assertEqual(context.SYNC, ctx.get_execution_mode())

ctx.set_execution_mode(context.ASYNC)

self.assertEqual(context.ASYNC, ctx.get_execution_mode())

ctx.set_execution_mode(context.SYNC)

self.assertEqual(context.SYNC, ctx.get_execution_mode())

with ctx.execution_mode(context.ASYNC):

self.assertEqual(context.ASYNC, ctx.get_execution_mode())

ctx.set_execution_mode(context.SYNC)

self.assertEqual(context.SYNC, ctx.get_execution_mode())

self.assertIsNone(ctx.summary_writer_resource)

ctx.summary_writer_resource = 'mock'

self.assertEqual('mock', ctx.summary_writer_resource)

self.assertEqual('', ctx.device_name)

self.assertEqual(ctx.device_name, ctx.device_spec.to_string())

with ctx.device('GPU:0'):

self.assertEqual('/job:localhost/replica:0/task:0/device:GPU:0',

ctx.device_name)

self.assertEqual(ctx.device_name, ctx.device_spec.to_string())

with ctx.device(None):

self.assertEqual('', ctx.device_name)

self.assertEqual(ctx.device_name, ctx.device_spec.to_string())

with ctx.device('CPU:0'):

self.assertEqual('/job:localhost/replica:0/task:0/device:CPU:0',

ctx.device_name)

self.assertEqual(ctx.device_name, ctx.device_spec.to_string())

has_cpu_device = False

for x in ctx.devices():

has_cpu_device = has_cpu_device or 'CPU' in x

self.assertTrue(has_cpu_device)

del ctx

def testAsyncBasic(self):

ctx = context.Context(execution_mode=context.ASYNC)

has_cpu_device = False

for x in ctx.devices():

has_cpu_device = has_cpu_device or 'CPU' in x

self.assertTrue(has_cpu_device)

del ctx

def testRunMetadata(self):

context.enable_run_metadata()

t = constant_op.constant(1.0)

_ = t + t # Runs an operation which will be in the RunMetadata

run_metadata = context.export_run_metadata()

context.disable_run_metadata()

step_stats = run_metadata.step_stats

self.assertGreater(len(step_stats.dev_stats), 0)

cpu_stats = step_stats.dev_stats[0]

self.assertEqual('/job:localhost/replica:0/task:0/device:CPU:0',

cpu_stats.device)

self.assertGreaterEqual(len(cpu_stats.node_stats), 1)

def testShouldCopy(self):

if not context.context().num_gpus():

self.skipTest('No devices other than CPUs found')

with ops.device('gpu:0'):

x = constant_op.constant(1.0)

y = array_ops.identity(x)

# The value we're testing y.device against will depend on what the behavior

# of not explicitly specifying a device in the context is. This behavior is

# subject to change (for example, in the future we may want to use GPUs, if

# available, when no device is explicitly provided)

self.assertEqual(y.device, '/job:localhost/replica:0/task:0/device:CPU:0')

def testContextSwitchStackContainsEagerMode(self):

# Eager execution has been enabled, and no other context switch has

# occurred, so `context_switches` should contain exactly one entry.

self.assertEqual(len(context.context().context_switches.stack), 1)

switch = context.context().context_switches.stack[0]

# The entry should log that eager mode was entered.

self.assertIs(switch.enter_context_fn, context.eager_mode)

# It is not possible to build a graph function when eager execution

# is enabled; the stack entry should reflect this fact.

self.assertFalse(switch.is_building_function)

def testInt32GPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

with ops.device('gpu:0'):

xent = nn_ops.sparse_softmax_cross_entropy_with_logits(

logits=[[0.0, 0.0]], labels=[0])

self.assertAllClose(xent, [0.69314718])

def _runInThread(self, target, args):

t = threading.Thread(target=target, args=args)

try:

t.start()

t.join()

except Exception as e:

raise e

# Test that different thread local values are initialized to the same values

# in different threads.

def testContextThreadLocalMembers(self):

def get_context_values(ctx):

return [

ctx.executing_eagerly(), ctx.scope_name, ctx.summary_writer_resource,

ctx.device_name,

ctx.num_gpus()

]

def get_values(ctx, values):

values.extend(get_context_values(ctx))

context_values = []

ctx = context.Context()

self._runInThread(get_values, (ctx, context_values))

self.assertAllEqual(context_values, get_context_values(ctx))

def testContextConfig(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

ctx = context.Context(config=config_pb2.ConfigProto(

device_count={'GPU': 0}))

self.assertEquals(0, ctx.num_gpus())

def testTensorPlacement(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

x = constant_op.constant(1.).gpu()

with context.device('gpu:0'):

y = constant_op.constant(2.)

# Add would fail if t2 were not on GPU

result = execute(

b'Add', 1, inputs=[x, y],

attrs=('T', x.dtype.as_datatype_enum))[0].cpu().numpy()

self.assertEqual(3, result)

def testResourceTensorPlacement(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

with context.device('gpu:0'):

v = resource_variable_ops.ResourceVariable(1.0)

with context.device('cpu:0'):

# Check that even though we specified the cpu device we'll run the read op

# in the device where the handle is.

self.assertAllEqual(

gen_resource_variable_ops.read_variable_op(v.handle, v.dtype), 1.0)

def testCopyBetweenDevices(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

x = constant_op.constant([[1., 2.], [3., 4.]])

x = x.cpu()

x = x.gpu()

x = x.cpu()

# Invalid device

with self.assertRaises(RuntimeError):

x.gpu(context.context().num_gpus() + 1)

def testCopyBetweenDevicesAsync(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

with context.execution_mode(context.ASYNC):

x = constant_op.constant([[1., 2.], [3., 4.]])

x = x.cpu()

x = x.gpu()

x = x.cpu()

context.async_wait()

# Invalid device

with self.assertRaises(RuntimeError):

x.gpu(context.context().num_gpus() + 1)

context.async_wait()

context.async_clear_error()

def testCopyScope(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

constant = constant_op.constant(1.0)

with ops.device('gpu:0'):

with context.context().device_policy(context.DEVICE_PLACEMENT_SILENT):

c = constant + 1.0

self.assertAllEqual(c, 2.0)

def testNumpyForceCPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

cpu = constant_op.constant([[1., 2.], [3., 4.]])

c2g = cpu.gpu()

self.assertAllEqual(c2g, cpu.numpy())

def testCopyFromCPUToCPU(self):

ta = constant_op.constant([[1, 2], [3, 4]])

tb = ta.cpu()

self.assertNotEqual(id(ta), id(tb))

self.assertAllEqual(ta, tb.numpy())

def testRegisterExceptionClass(self):

with self.assertRaises(TypeError):

pywrap_tensorflow.TFE_Py_RegisterExceptionClass(str)

pywrap_tensorflow.TFE_Py_RegisterExceptionClass(core._NotOkStatusException) # pylint: disable=protected-access

# TODO(agarwal): add tests passing incorrect typed values to attrs.

def testExecuteBasic(self):

three = constant_op.constant(3)

five = constant_op.constant(5)

product = execute(

b'Mul',

num_outputs=1,

inputs=[three, five],

attrs=('T', three.dtype.as_datatype_enum))[0]

self.assertAllEqual(15, product)

def testExecuteBasicAsync(self):

with context.execution_mode(context.ASYNC):

three = constant_op.constant(3)

five = constant_op.constant(5)

product = execute(

b'Mul',

num_outputs=1,

inputs=[three, five],

attrs=('T', three.dtype.as_datatype_enum))[0]

self.assertAllEqual(15, product)

# Error: Invalid arguments

context.set_execution_mode(context.ASYNC)

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'MatMul',

num_outputs=1,

inputs=[three, five],

attrs=('transpose_a', False, 'transpose_b', False, 'T',

three.dtype.as_datatype_enum))

context.async_wait()

context.async_clear_error()

context.set_execution_mode(context.SYNC)

def testExecuteTooManyNumOutputs(self):

# num_outputs provided is 50, but only one output is produced.

product = execute(

b'Mul',

num_outputs=50,

inputs=[constant_op.constant(3),

constant_op.constant(5)],

attrs=('T', dtypes.int32.as_datatype_enum))[0]

self.assertAllEqual(15, product)

def testExecuteTooFewNumOutputs(self):

# num_outputs provided is 0, but one output is produced.

with self.assertRaises(errors.InvalidArgumentError):

_ = execute(

b'Mul',

num_outputs=0,

inputs=[constant_op.constant(3),

constant_op.constant(5)],

attrs=('T', dtypes.int32.as_datatype_enum))[0]

def testMatMulGPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

three = constant_op.constant([[3.]]).gpu()

five = constant_op.constant([[5.]]).gpu()

product = execute(

b'MatMul',

num_outputs=1,

inputs=[three, five],

attrs=('transpose_a', False, 'transpose_b', False, 'T',

three.dtype.as_datatype_enum))[0]

self.assertAllEqual([[15.0]], product)

def testExecuteStringAttr(self):

checked_three = execute(

b'CheckNumerics',

num_outputs=1,

inputs=[constant_op.constant(3.)],

attrs=('message', 'just checking', 'T',

dtypes.float32.as_datatype_enum))[0]

self.assertEqual([[3]], checked_three.numpy())

def testExecuteStringAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

_ = execute(

b'CheckNumerics',

num_outputs=1,

inputs=[constant_op.constant(3.)],

attrs=('message', 1, 'T', dtypes.float32.as_datatype_enum))

def testExecuteFloatAttr(self):

almost_equal = execute(

b'ApproximateEqual',

num_outputs=1,

inputs=[constant_op.constant(3.0), constant_op.constant(2.9)],

attrs=('tolerance', 0.3, 'T', dtypes.float32.as_datatype_enum))[0]

self.assertTrue(almost_equal)

def testExecuteFloatAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

_ = execute(

b'ApproximateEqual',

num_outputs=1,

inputs=[constant_op.constant(3.0), constant_op.constant(2.9)],

attrs=('tolerance', '0.3', 'T', dtypes.float32.as_datatype_enum))

def testExecuteIntAttr(self):

total = execute(

b'AddN',

num_outputs=1,

inputs=[constant_op.constant(3), constant_op.constant(4)],

attrs=('T', dtypes.int32.as_datatype_enum, 'N', 2))[0]

self.assertAllEqual(7, total)

def testExecuteIntAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

_ = execute(

b'AddN',

num_outputs=1,

inputs=[constant_op.constant(3), constant_op.constant(4)],

attrs=('T', dtypes.int32.as_datatype_enum, 'N', '2'))

# Looks like we don't have an existing op with list(bool) attrs.

def testExecuteBoolAttr(self):

product = execute(

b'MatMul',

num_outputs=1,

inputs=[constant_op.constant([[3]]),

constant_op.constant([[5]])],

attrs=('transpose_a', True, 'transpose_b', False, 'T',

dtypes.int32.as_datatype_enum))[0]

self.assertAllEqual([[15]], product)

def testExecuteShapeAttr(self):

execute(

b'VarHandleOp',

num_outputs=1,

inputs=[],

attrs=('shape', [1, 2], 'dtype', dtypes.int32.as_datatype_enum,

'container', '', 'shared_name', ''))

def testExecuteShapeAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'VarHandleOp',

num_outputs=1,

inputs=[],

attrs=('shape', 1, 'dtype', dtypes.int32.as_datatype_enum,

'container', '', 'shared_name', ''))

def testExecuteListStringAttr(self):

execute(

b'TensorSummary',

num_outputs=1,

inputs=[constant_op.constant(3.0)],

attrs=('T', dtypes.float32.as_datatype_enum, 'description',

'tensor_summary', 'labels', ['3',

'summary'], 'display_name', 'test'))

def testExecuteListStringAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'TensorSummary',

num_outputs=1,

inputs=[constant_op.constant(3.0)],

attrs=('T', dtypes.float32.as_datatype_enum, 'description', '',

'labels', 3, 'display_name', 'test'))

def testExecuteListStringAttrBadListValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'TensorSummary',

num_outputs=1,

inputs=[constant_op.constant(3.0)],

attrs=('T', dtypes.float32.as_datatype_enum, 'description', '',

'labels', [3], 'display_name', 'test'))

def testExecuteListFloatAttr(self):

b = execute(

b'Bucketize',

num_outputs=1,

inputs=[constant_op.constant([3.0, 5.0, 7.0])],

attrs=('T', dtypes.float32.as_datatype_enum, 'boundaries', [4.0,

6.0]))[0]

self.assertAllEqual([0, 1, 2], b)

def testExecuteListFloatAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Bucketize',

num_outputs=1,

inputs=[constant_op.constant([3.0, 5.0, 7.0])],

attrs=('T', dtypes.float32.as_datatype_enum, 'boundaries', 4.0))

def testExecuteListFloatAttrBadListValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Bucketize',

num_outputs=1,

inputs=[constant_op.constant([3.0, 5.0, 7.0])],

attrs=('T', dtypes.float32.as_datatype_enum, 'boundaries',

['4.0', '6.0']))

def testExecuteListIntAttr(self):

b = execute(

b'Squeeze',

num_outputs=1,

inputs=[constant_op.constant([[[3.0]]])],

attrs=('T', dtypes.float32.as_datatype_enum, 'squeeze_dims', [0, 2]))[0]

self.assertAllEqual([3], b)

def testExecuteListIntAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Squeeze',

num_outputs=1,

inputs=[constant_op.constant([[[3.0]]])],

attrs=('T', dtypes.float32.as_datatype_enum, 'squeeze_dims', 0))

def testExecuteListIntAttrBadListValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Squeeze',

num_outputs=1,

inputs=[constant_op.constant([[[3.0]]])],

attrs=('T', dtypes.float32.as_datatype_enum, 'squeeze_dims',

['0', '2']))

def testExecuteListTypeListShapeAttr(self):

execute(

b'Barrier',

num_outputs=1,

inputs=[],

attrs=('component_types', [dtypes.float64.as_datatype_enum], 'shapes',

[[1, 2]], 'capacity', -1, 'container', '', 'shared_name', ''))

def testExecuteListTypeAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Barrier',

num_outputs=1,

inputs=[],

attrs=('component_types', dtypes.float64.as_datatype_enum, 'shapes',

[[1, 2]], 'capacity', -1, 'container', '', 'shared_name', ''))

def testExecuteListTypeAttrBadListValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Barrier',

num_outputs=1,

inputs=[],

attrs=('component_types', '1', 'shapes', [[1, 2]], 'capacity', -1,

'container', '', 'shared_name', ''))

def testExecuteListShapeAttrBadValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Barrier',

num_outputs=1,

inputs=[],

attrs=('component_types', [dtypes.float64.as_datatype_enum], 'shapes',

[1, 2], 'capacity', -1, 'container', '', 'shared_name', ''))

def testExecuteListShapeAttrBadListValue(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Barrier',

num_outputs=1,

inputs=[],

attrs=('component_types', [dtypes.float64.as_datatype_enum], 'shapes',

[1], 'capacity', -1, 'container', '', 'shared_name', ''))

def testExecuteMultipleOutputs(self):

split_dim = 1

value = [[0, 1, 2], [3, 4, 5]]

x1, x2, x3 = execute(

b'Split',

num_outputs=3,

inputs=[constant_op.constant(split_dim),

constant_op.constant(value)],

attrs=('num_split', 3, 'T', dtypes.int32.as_datatype_enum))

self.assertAllEqual([[0], [3]], x1)

self.assertAllEqual([[1], [4]], x2)

self.assertAllEqual([[2], [5]], x3)

def testExecuteBadNumOutputsArgument(self):

with self.assertRaises(TypeError):

execute(

b'Relu', [],

inputs=[constant_op.constant(3.0)],

attrs=('T', dtypes.float32.as_datatype_enum))

def testExecuteUnknownOp(self):

with self.assertRaises(errors.NotFoundError):

execute(b'BlahBlahBlah', num_outputs=1, inputs=[], attrs=None)

def testExecuteUnknownAttr(self):

with self.assertRaises(errors.InvalidArgumentError):

execute(

b'Identity',

num_outputs=1,

inputs=[constant_op.constant(3)],

attrs=('T', dtypes.int32.as_datatype_enum, 'unknown_attr', 'blah'))

def testComposition(self):

def add(x, y):

return execute(

b'Add',

num_outputs=1,

inputs=[x, y],

attrs=('T', dtypes.int32.as_datatype_enum))[0]

x = constant_op.constant(1)

three_x = add(add(x, x), x)

self.assertEquals(dtypes.int32, three_x.dtype)

self.assertAllEqual(3, three_x)

def testOperationWithNoInputsRunsOnDevice(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

shape = constant_op.constant([], dtype=dtypes.int32)

# x: Run the "TruncatedNormal" op CPU and copy result to GPU.

x = truncated_normal(shape).gpu()

# y: Explicitly run the "TruncatedNormal" op on GPU.

with context.device('gpu:0'):

y = truncated_normal(shape)

# Add would fail if x and y were not on the same device.

execute(

b'Add', 1, inputs=[x, y], attrs=('T', x.dtype.as_datatype_enum))

def testInvalidDevice(self):

with self.assertRaises(ValueError):

with context.device('pu:0'):

_ = constant_op.constant(1)

def testConvertMixedEagerTensors(self):

array = np.zeros((), dtype=np.float32)

tensor = constant_op.constant(0., dtype=dtypes.float32)

types, tensors = execute_lib.convert_to_mixed_eager_tensors(

[array, tensor], context.context())

for typ, t in zip(types, tensors):

self.assertEquals(typ, dtypes.float32)

self.assertIsInstance(t, ops.EagerTensor)

class SendRecvTest(test_util.TensorFlowTestCase):

cpu_device = '/job:localhost/replica:0/task:0/device:CPU:0'

def _send(self, tensor, tensor_name, to_device):

return execute(

b'_Send', num_outputs=0, inputs=[tensor],

attrs=('T', tensor.dtype.as_datatype_enum,

'tensor_name', tensor_name,

'send_device', tensor.device,

'send_device_incarnation', 0,

'recv_device', to_device,

'client_terminated', True))

def _recv(self, dtype, tensor_name, from_device):

device_name = context.context().device_name

if not device_name:

device_name = self.cpu_device

return execute(

b'_Recv', num_outputs=1, inputs=[],

attrs=('tensor_type', dtype.as_datatype_enum,

'tensor_name', tensor_name,

'send_device', from_device,

'send_device_incarnation', 0,

'recv_device', device_name,

'client_terminated', False))[0]

def testBasic(self):

t0 = constant_op.constant(1.0)

t1 = constant_op.constant(2.0)

self._send(t0, 't0', self.cpu_device)

self._send(t1, 't1', self.cpu_device)

self.assertAllEqual(

self._recv(dtypes.float32, 't0', self.cpu_device),

1.0)

self.assertAllEqual(

self._recv(dtypes.float32, 't1', self.cpu_device),

2.0)

def testLocalCrossDevice(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

gpu_device_name = '/job:localhost/replica:0/task:0/device:GPU:0'

with ops.device('GPU:0'):

t0 = constant_op.constant(1.0)

self._send(t0, 't0', self.cpu_device)

with ops.device('cpu:0'):

self.assertAllEqual(

self._recv(dtypes.float32, 't0', gpu_device_name),

1.0)

self._send(constant_op.constant(2.0), 't1', gpu_device_name)

with ops.device('GPU:0'):

self.assertAllEqual(

self._recv(dtypes.float32, 't1', self.cpu_device),

2.0)

if __name__ == '__main__':

test.main()

tensorflow/tensorflow/python/eager/core_test.py at master · codeplaysoftware/tensorflow · GitHub

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