0b8f0a5b84
- experimental_run_v2 -> run PiperOrigin-RevId: 300574367 Change-Id: I5d82ea5450a4d32aea6d05ed3db4f02b8edb2eea
309 lines
13 KiB
Python
309 lines
13 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for tfdbg op callbacks running with various `DistributionStrategy`s."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import re
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.debug.lib import check_numerics_callback
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from tensorflow.python.debug.lib import debug_events_reader
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from tensorflow.python.debug.lib import dumping_callback
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from tensorflow.python.debug.lib import dumping_callback_test_lib
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import errors
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import googletest
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.training import gradient_descent
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class MiniModel(keras.Model):
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"""Minimal subclassed Keras model."""
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def __init__(self, generate_infinity=False):
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super(MiniModel, self).__init__(name="")
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self._generate_infinity = generate_infinity
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self.fc = keras.layers.Dense(
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1, kernel_initializer="ones", bias_initializer="ones",
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activation="linear")
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@def_function.function
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def call(self, inputs, training=True):
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y = self.fc(inputs)
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if self._generate_infinity:
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y = math_ops.divide(y, array_ops.zeros_like(y))
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return y
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class DistributedDumpingCallbackTest(
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dumping_callback_test_lib.DumpingCallbackTestBase, parameterized.TestCase):
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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strategy_combinations.one_device_strategy_gpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.mirrored_strategy_with_two_gpus,
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],
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inside_scope=[False, True],
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# TODO(cais): Investigate that under V1 graph mode (mode="graph"),
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# occasionally (~1-2% of time) the test runs into the following error:
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# CancelledError: [_Derived_] Function was cancelled before it was
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# started.
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mode=["eager"],
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))
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def testCheckingInfinityInMiniModelOnOneOrTwoDevices(
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self, distribution, inside_scope):
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if not inside_scope:
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check_numerics_callback.enable_check_numerics()
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with distribution.scope():
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if inside_scope:
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check_numerics_callback.enable_check_numerics()
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mini_model = MiniModel(generate_infinity=True)
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def train_step():
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with backprop.GradientTape() as tape:
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loss = mini_model(array_ops.ones([1, 10]))
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return tape.gradient(loss, mini_model.weights)
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caught_error = None
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try:
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distribution.run(train_step)
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except errors.InvalidArgumentError as error:
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caught_error = error
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self.assertTrue(caught_error)
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self.assertTrue(re.search(
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r"Detected Infinity or NaN.*\"RealDiv\"", caught_error.message))
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self.assertIn(
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"-> | y = math_ops.divide(y, array_ops.zeros_like(y))",
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caught_error.message)
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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strategy_combinations.one_device_strategy_gpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.mirrored_strategy_with_two_gpus,
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],
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mode=["eager"],
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tensor_debug_mode=["NO_TENSOR", "FULL_TENSOR"],
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))
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def testDumpingMiniModel(self, distribution, tensor_debug_mode):
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with distribution.scope():
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writer = dumping_callback.enable_dump_debug_info(
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self.dump_root, tensor_debug_mode=tensor_debug_mode)
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mini_model = MiniModel()
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optimizer = gradient_descent.GradientDescentOptimizer(0.25)
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def train_step():
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with backprop.GradientTape() as tape:
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loss = mini_model(array_ops.ones([1, 10]))
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grads = tape.gradient(loss, mini_model.weights)
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grads_and_vars = zip(grads, mini_model.weights)
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optimizer.apply_gradients(grads_and_vars)
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distribution.run(train_step)
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updated_var_values = self.evaluate(mini_model.variables)
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num_devices = len(distribution.extended.worker_devices)
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assert num_devices in (1, 2)
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if num_devices == 1:
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self.assertAllEqual(0.75 * np.ones([10, 1]), updated_var_values[0])
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self.assertAllEqual([0.75], updated_var_values[1])
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else:
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self.assertAllEqual(0.5 * np.ones([10, 1]), updated_var_values[0])
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self.assertAllEqual([0.5], updated_var_values[1])
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writer.FlushNonExecutionFiles()
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writer.FlushExecutionFiles()
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device_name_0 = distribution.extended.worker_devices[0]
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logging.info("device_name_0 = %s", device_name_0)
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if num_devices > 1:
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device_name_1 = distribution.extended.worker_devices[1]
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logging.info("device_name_1 = %s", device_name_1)
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with debug_events_reader.DebugDataReader(self.dump_root) as reader:
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reader.update()
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traces = reader.graph_execution_traces()
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# Verify graph-execution traces are available for both devices.
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# We don't assert MatMul occurs exactly once because the gradient of
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# MatMul involves MatMul.
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device_0_executed_op_types = [
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trace.op_type for trace in traces
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if trace.device_name.endswith(device_name_0)]
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if num_devices > 1:
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device_1_executed_op_types = [
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trace.op_type for trace in traces
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if trace.device_name.endswith(device_name_1)]
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self.assertIn("MatMul", device_0_executed_op_types)
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self.assertEqual(device_0_executed_op_types.count("BiasAdd"), 1)
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if num_devices > 1:
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self.assertIn("MatMul", device_1_executed_op_types)
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self.assertEqual(device_1_executed_op_types.count("BiasAdd"), 1)
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if tensor_debug_mode == "NO_TENSOR":
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for trace in traces:
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self.assertIsNone(trace.debug_tensor_value)
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elif tensor_debug_mode == "FULL_TENSOR":
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device_0_matmul_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "MatMul" and
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trace.device_name.endswith(device_name_0)]
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device_0_bias_add_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "BiasAdd" and
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trace.device_name.endswith(device_name_0)]
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self.assertAllClose(device_0_matmul_values[0], [[10.0]])
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self.assertAllClose(device_0_bias_add_values[0], [[11.0]])
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if num_devices > 1:
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device_1_matmul_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "MatMul" and
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trace.device_name.endswith(device_name_1)]
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device_1_bias_add_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "BiasAdd" and
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trace.device_name.endswith(device_name_1)]
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self.assertAllClose(device_1_matmul_values[0], [[10.0]])
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self.assertAllClose(device_1_bias_add_values[0], [[11.0]])
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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strategy_combinations.one_device_strategy_gpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.mirrored_strategy_with_two_gpus,
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],
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mode=["eager"],
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tensor_debug_mode=["NO_TENSOR", "FULL_TENSOR"],
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))
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def testKerasModelFitOnOneOrTwoDevices(self, distribution, tensor_debug_mode):
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writer = dumping_callback.enable_dump_debug_info(
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self.dump_root, tensor_debug_mode=tensor_debug_mode)
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with distribution.scope():
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model = keras.Sequential()
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model.add(keras.layers.Dense(
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units=10, input_shape=[5], activation="relu"))
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model.add(keras.layers.Dense(units=1))
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model.compile(loss="mse", optimizer="sgd")
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batch_size = 20
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x = np.ones([batch_size, 5])
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y = np.ones([batch_size, 1])
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epochs = 1
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history = model.fit(x, y, epochs=epochs, verbose=0)
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self.assertLen(history.history["loss"], epochs)
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writer.FlushNonExecutionFiles()
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writer.FlushExecutionFiles()
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with debug_events_reader.DebugDataReader(self.dump_root) as reader:
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reader.update()
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executions = reader.executions()
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fit_executions = [
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execution.op_type
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for execution in executions
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if dumping_callback.is_op_type_function(execution.op_type)
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]
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self.assertLen(fit_executions, epochs)
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traces = reader.graph_execution_traces()
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num_devices = len(distribution.extended.worker_devices)
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device_name_0 = distribution.extended.worker_devices[0]
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if num_devices > 1:
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device_name_1 = distribution.extended.worker_devices[1]
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device_0_executed_op_types = [
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trace.op_type for trace in traces
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if trace.device_name.endswith(device_name_0)]
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if num_devices > 1:
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device_1_executed_op_types = [
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trace.op_type for trace in traces
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if trace.device_name.endswith(device_name_1)]
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self.assertIn("MatMul", device_0_executed_op_types)
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self.assertIn("BiasAdd", device_0_executed_op_types)
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self.assertIn("Relu", device_0_executed_op_types)
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self.assertIn("ReluGrad", device_0_executed_op_types)
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if num_devices > 1:
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# If there are two devices involved, assert the ops inside tf.functions
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# are executed and recorded for the equal numbers of times by the
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# dumping op-callback.
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self.assertEqual(
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device_0_executed_op_types.count("MatMul"),
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device_1_executed_op_types.count("MatMul"))
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self.assertEqual(
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device_0_executed_op_types.count("BiasAdd"),
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device_1_executed_op_types.count("BiasAdd"))
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self.assertEqual(
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device_0_executed_op_types.count("Relu"),
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device_1_executed_op_types.count("Relu"))
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self.assertEqual(
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device_0_executed_op_types.count("ReluGrad"),
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device_1_executed_op_types.count("ReluGrad"))
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if tensor_debug_mode == "NO_TENSOR":
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for trace in traces:
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self.assertIsNone(trace.debug_tensor_value)
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elif tensor_debug_mode == "FULL_TENSOR":
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gpu_0_relu_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "Relu" and
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trace.device_name.endswith(device_name_0)]
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self.assertTrue(gpu_0_relu_values)
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gpu_0_relu_grad_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "ReluGrad" and
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trace.device_name.endswith(device_name_0)]
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self.assertTrue(gpu_0_relu_grad_values)
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if num_devices > 1:
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gpu_1_relu_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "Relu" and
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trace.device_name.endswith(device_name_1)]
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self.assertTrue(gpu_1_relu_values)
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for i in range(len(gpu_0_relu_values)):
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self.assertEqual(gpu_0_relu_values[i].shape,
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gpu_1_relu_values[i].shape)
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gpu_1_relu_grad_values = [
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reader.graph_execution_trace_to_tensor_value(trace)
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for trace in traces if trace.op_type == "ReluGrad" and
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trace.device_name.endswith(device_name_1)]
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self.assertTrue(gpu_1_relu_grad_values)
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for i in range(len(gpu_0_relu_grad_values)):
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self.assertEqual(gpu_0_relu_grad_values[i].shape,
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gpu_1_relu_grad_values[i].shape)
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if __name__ == "__main__":
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googletest.main()
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