experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[tfdbg2] Remove distributed_callbacks_test

Browse Source 
PiperOrigin-RevId: 306554571
Change-Id: I11d7df5958b013572fd084c1b6db65001abce34d
This commit is contained in:
Shanqing Cai 2020-04-14 18:22:14 -07:00 committed by TensorFlower Gardener
parent baf2513d4d
commit 5297723009
2 changed files with 0 additions and 339 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

31
tensorflow/python/debug/BUILD
View File

@ -745,37 +745,6 @@ cuda_py_test(
],
)
cuda_py_test(
name = "distributed_callbacks_test",
size = "medium",
srcs = ["lib/distributed_callbacks_test.py"],
python_version = "PY3",
shard_count = 4,
tags = [
"guitar",
"multi_and_single_gpu",
"no_windows", # TODO(b/142475891): Enable this test on Windows.
"no_windows_gpu", # TODO(b/130551176)
],
xla_enable_strict_auto_jit = False, # Node names are different with autojit
deps = [
":check_numerics_callback",
":debug_events_reader",
":debug_events_writer",
":dumping_callback",
":dumping_callback_test_lib",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:math_ops",
"//tensorflow/python:platform_test",
"//tensorflow/python:variables",
"//tensorflow/python/distribute:combinations",
"//tensorflow/python/distribute:mirrored_strategy",
"//tensorflow/python/distribute:strategy_combinations",
"//tensorflow/python/keras",
"//third_party/py/numpy",
],
)
cuda_py_test(
name = "dumping_callback_test",
size = "medium",

308
tensorflow/python/debug/lib/distributed_callbacks_test.py
View File

@ -1,308 +0,0 @@
# Copyright 2019 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 tfdbg op callbacks running with various `DistributionStrategy`s."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from absl.testing import parameterized
import numpy as np
from tensorflow.python import keras
from tensorflow.python.debug.lib import check_numerics_callback
from tensorflow.python.debug.lib import debug_events_reader
from tensorflow.python.debug.lib import dumping_callback
from tensorflow.python.debug.lib import dumping_callback_test_lib
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.eager import backprop
from tensorflow.python.eager import def_function
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import gradient_descent
class MiniModel(keras.Model):
"""Minimal subclassed Keras model."""
def __init__(self, generate_infinity=False):
super(MiniModel, self).__init__(name="")
self._generate_infinity = generate_infinity
self.fc = keras.layers.Dense(
1, kernel_initializer="ones", bias_initializer="ones",
activation="linear")
@def_function.function
def call(self, inputs, training=True):
y = self.fc(inputs)
if self._generate_infinity:
y = math_ops.divide(y, array_ops.zeros_like(y))
return y
class DistributedDumpingCallbackTest(
dumping_callback_test_lib.DumpingCallbackTestBase, parameterized.TestCase):
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.one_device_strategy,
strategy_combinations.one_device_strategy_gpu,
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.mirrored_strategy_with_two_gpus,
],
inside_scope=[False, True],
# TODO(cais): Investigate that under V1 graph mode (mode="graph"),
# occasionally (~1-2% of time) the test runs into the following error:
# CancelledError: [_Derived_] Function was cancelled before it was
# started.
mode=["eager"],
))
def testCheckingInfinityInMiniModelOnOneOrTwoDevices(
self, distribution, inside_scope):
if not inside_scope:
check_numerics_callback.enable_check_numerics()
with distribution.scope():
if inside_scope:
check_numerics_callback.enable_check_numerics()
mini_model = MiniModel(generate_infinity=True)
def train_step():
with backprop.GradientTape() as tape:
loss = mini_model(array_ops.ones([1, 10]))
return tape.gradient(loss, mini_model.weights)
caught_error = None
try:
distribution.run(train_step)
except errors.InvalidArgumentError as error:
caught_error = error
self.assertTrue(caught_error)
self.assertTrue(re.search(
r"Detected Infinity or NaN.*\"RealDiv\"", caught_error.message))
self.assertIn(
"-> | y = math_ops.divide(y, array_ops.zeros_like(y))",
caught_error.message)
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.one_device_strategy,
strategy_combinations.one_device_strategy_gpu,
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.mirrored_strategy_with_two_gpus,
],
mode=["eager"],
tensor_debug_mode=["NO_TENSOR", "FULL_TENSOR"],
))
def testDumpingMiniModel(self, distribution, tensor_debug_mode):
with distribution.scope():
writer = dumping_callback.enable_dump_debug_info(
self.dump_root, tensor_debug_mode=tensor_debug_mode)
mini_model = MiniModel()
optimizer = gradient_descent.GradientDescentOptimizer(0.25)
def train_step():
with backprop.GradientTape() as tape:
loss = mini_model(array_ops.ones([1, 10]))
grads = tape.gradient(loss, mini_model.weights)
grads_and_vars = zip(grads, mini_model.weights)
optimizer.apply_gradients(grads_and_vars)
distribution.run(train_step)
updated_var_values = self.evaluate(mini_model.variables)
num_devices = len(distribution.extended.worker_devices)
assert num_devices in (1, 2)
if num_devices == 1:
self.assertAllEqual(0.75 * np.ones([10, 1]), updated_var_values[0])
self.assertAllEqual([0.75], updated_var_values[1])
else:
self.assertAllEqual(0.5 * np.ones([10, 1]), updated_var_values[0])
self.assertAllEqual([0.5], updated_var_values[1])
writer.FlushNonExecutionFiles()
writer.FlushExecutionFiles()
device_name_0 = distribution.extended.worker_devices[0]
logging.info("device_name_0 = %s", device_name_0)
if num_devices > 1:
device_name_1 = distribution.extended.worker_devices[1]
logging.info("device_name_1 = %s", device_name_1)
with debug_events_reader.DebugDataReader(self.dump_root) as reader:
reader.update()
traces = reader.graph_execution_traces()
# Verify graph-execution traces are available for both devices.
# We don't assert MatMul occurs exactly once because the gradient of
# MatMul involves MatMul.
device_0_executed_op_types = [
trace.op_type for trace in traces
if trace.device_name.endswith(device_name_0)]
if num_devices > 1:
device_1_executed_op_types = [
trace.op_type for trace in traces
if trace.device_name.endswith(device_name_1)]
self.assertIn("MatMul", device_0_executed_op_types)
self.assertEqual(device_0_executed_op_types.count("BiasAdd"), 1)
if num_devices > 1:
self.assertIn("MatMul", device_1_executed_op_types)
self.assertEqual(device_1_executed_op_types.count("BiasAdd"), 1)
if tensor_debug_mode == "NO_TENSOR":
for trace in traces:
self.assertIsNone(trace.debug_tensor_value)
elif tensor_debug_mode == "FULL_TENSOR":
device_0_matmul_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "MatMul" and
trace.device_name.endswith(device_name_0)]
device_0_bias_add_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "BiasAdd" and
trace.device_name.endswith(device_name_0)]
self.assertAllClose(device_0_matmul_values[0], [[10.0]])
self.assertAllClose(device_0_bias_add_values[0], [[11.0]])
if num_devices > 1:
device_1_matmul_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "MatMul" and
trace.device_name.endswith(device_name_1)]
device_1_bias_add_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "BiasAdd" and
trace.device_name.endswith(device_name_1)]
self.assertAllClose(device_1_matmul_values[0], [[10.0]])
self.assertAllClose(device_1_bias_add_values[0], [[11.0]])
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.one_device_strategy,
strategy_combinations.one_device_strategy_gpu,
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.mirrored_strategy_with_two_gpus,
],
mode=["eager"],
tensor_debug_mode=["NO_TENSOR", "FULL_TENSOR"],
))
def testKerasModelFitOnOneOrTwoDevices(self, distribution, tensor_debug_mode):
writer = dumping_callback.enable_dump_debug_info(
self.dump_root, tensor_debug_mode=tensor_debug_mode)
with distribution.scope():
model = keras.Sequential()
model.add(keras.layers.Dense(
units=10, input_shape=[5], activation="relu"))
model.add(keras.layers.Dense(units=1))
model.compile(loss="mse", optimizer="sgd")
batch_size = 20
x = np.ones([batch_size, 5])
y = np.ones([batch_size, 1])
epochs = 1
history = model.fit(x, y, epochs=epochs, verbose=0)
self.assertLen(history.history["loss"], epochs)
writer.FlushNonExecutionFiles()
writer.FlushExecutionFiles()
with debug_events_reader.DebugDataReader(self.dump_root) as reader:
reader.update()
executions = reader.executions()
fit_executions = [
execution.op_type
for execution in executions
if dumping_callback.is_op_type_function(execution.op_type)
]
self.assertLen(fit_executions, epochs)
traces = reader.graph_execution_traces()
num_devices = len(distribution.extended.worker_devices)
device_name_0 = distribution.extended.worker_devices[0]
if num_devices > 1:
device_name_1 = distribution.extended.worker_devices[1]
device_0_executed_op_types = [
trace.op_type for trace in traces
if trace.device_name.endswith(device_name_0)]
if num_devices > 1:
device_1_executed_op_types = [
trace.op_type for trace in traces
if trace.device_name.endswith(device_name_1)]
self.assertIn("MatMul", device_0_executed_op_types)
self.assertIn("BiasAdd", device_0_executed_op_types)
self.assertIn("Relu", device_0_executed_op_types)
self.assertIn("ReluGrad", device_0_executed_op_types)
if num_devices > 1:
# If there are two devices involved, assert the ops inside tf.functions
# are executed and recorded for the equal numbers of times by the
# dumping op-callback.
self.assertEqual(
device_0_executed_op_types.count("MatMul"),
device_1_executed_op_types.count("MatMul"))
self.assertEqual(
device_0_executed_op_types.count("BiasAdd"),
device_1_executed_op_types.count("BiasAdd"))
self.assertEqual(
device_0_executed_op_types.count("Relu"),
device_1_executed_op_types.count("Relu"))
self.assertEqual(
device_0_executed_op_types.count("ReluGrad"),
device_1_executed_op_types.count("ReluGrad"))
if tensor_debug_mode == "NO_TENSOR":
for trace in traces:
self.assertIsNone(trace.debug_tensor_value)
elif tensor_debug_mode == "FULL_TENSOR":
gpu_0_relu_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "Relu" and
trace.device_name.endswith(device_name_0)]
self.assertTrue(gpu_0_relu_values)
gpu_0_relu_grad_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "ReluGrad" and
trace.device_name.endswith(device_name_0)]
self.assertTrue(gpu_0_relu_grad_values)
if num_devices > 1:
gpu_1_relu_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "Relu" and
trace.device_name.endswith(device_name_1)]
self.assertTrue(gpu_1_relu_values)
for i in range(len(gpu_0_relu_values)):
self.assertEqual(gpu_0_relu_values[i].shape,
gpu_1_relu_values[i].shape)
gpu_1_relu_grad_values = [
reader.graph_execution_trace_to_tensor_value(trace)
for trace in traces if trace.op_type == "ReluGrad" and
trace.device_name.endswith(device_name_1)]
self.assertTrue(gpu_1_relu_grad_values)
for i in range(len(gpu_0_relu_grad_values)):
self.assertEqual(gpu_0_relu_grad_values[i].shape,
gpu_1_relu_grad_values[i].shape)
if __name__ == "__main__":
googletest.main()