[tfdbg2] Remove distributed_callbacks_test

PiperOrigin-RevId: 306554571
Change-Id: I11d7df5958b013572fd084c1b6db65001abce34d

tensorflow/python/debug/BUILD

@@ -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",

tensorflow/python/debug/lib/distributed_callbacks_test.py

-# 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()