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Adds eager microbenchmarks for the Keras .fit method

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PiperOrigin-RevId: 218269923
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A. Unique TensorFlower 2018-10-22 18:27:19 -07:00 committed by TensorFlower Gardener
parent e31663c010
commit 236725c044
1 changed files with 62 additions and 18 deletions
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80
tensorflow/python/eager/benchmarks_test.py
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@ -33,6 +33,7 @@ from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python import keras
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.eager import backprop # pylint: disable=unused-import
from tensorflow.python.eager import context
from tensorflow.python.eager import core
@ -48,6 +49,7 @@ from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.training import gradient_descent
CPU = "/device:CPU:0"
GPU = "/device:GPU:0"
@ -76,18 +78,18 @@ def c_tfe_py_fastpath_execute(a,
class SubclassedKerasModel(keras.Model):
def __init__(self):
def __init__(self, initializer="ones"):
super(SubclassedKerasModel, self).__init__()
self.layer_a = keras.layers.Dense(
64, kernel_initializer="ones", bias_initializer="zeros")
64, kernel_initializer=initializer, bias_initializer="zeros")
self.layer_b = keras.layers.Dense(
128, kernel_initializer="ones", bias_initializer="zeros")
128, kernel_initializer=initializer, bias_initializer="zeros")
self.layer_c = keras.layers.Dense(
256, kernel_initializer="ones", bias_initializer="zeros")
256, kernel_initializer=initializer, bias_initializer="zeros")
self.layer_d = keras.layers.Dense(
256, kernel_initializer="ones", bias_initializer="zeros")
256, kernel_initializer=initializer, bias_initializer="zeros")
self.layer_e = keras.layers.Dense(
10, kernel_initializer="ones", bias_initializer="zeros")
10, kernel_initializer=initializer, bias_initializer="zeros")
def call(self, x):
x = self.layer_a(x)
@ -97,34 +99,34 @@ class SubclassedKerasModel(keras.Model):
return self.layer_e(x)
def make_keras_model():
def make_keras_model(initializer="ones"):
model_input = keras.Input(shape=(10,))
x = keras.layers.Dense(
64, kernel_initializer="ones", bias_initializer="zeros")(model_input)
64, kernel_initializer=initializer, bias_initializer="zeros")(model_input)
x = keras.layers.Dense(
128, kernel_initializer="ones", bias_initializer="zeros")(x)
128, kernel_initializer=initializer, bias_initializer="zeros")(x)
x = keras.layers.Dense(
256, kernel_initializer="ones", bias_initializer="zeros")(x)
256, kernel_initializer=initializer, bias_initializer="zeros")(x)
x = keras.layers.Dense(
256, kernel_initializer="ones", bias_initializer="zeros")(x)
256, kernel_initializer=initializer, bias_initializer="zeros")(x)
x = keras.layers.Dense(
10, kernel_initializer="ones", bias_initializer="zeros")(x)
10, kernel_initializer=initializer, bias_initializer="zeros")(x)
return keras.Model(inputs=model_input, outputs=x)
def make_sequential_keras_model():
def make_sequential_keras_model(initializer="ones"):
model = keras.models.Sequential()
model.add(keras.layers.Dense(
64, kernel_initializer="ones", bias_initializer="zeros",
64, kernel_initializer=initializer, bias_initializer="zeros",
input_shape=(10,)))
model.add(keras.layers.Dense(
128, kernel_initializer="ones", bias_initializer="zeros"))
128, kernel_initializer=initializer, bias_initializer="zeros"))
model.add(keras.layers.Dense(
256, kernel_initializer="ones", bias_initializer="zeros"))
256, kernel_initializer=initializer, bias_initializer="zeros"))
model.add(keras.layers.Dense(
256, kernel_initializer="ones", bias_initializer="zeros"))
256, kernel_initializer=initializer, bias_initializer="zeros"))
model.add(keras.layers.Dense(
10, kernel_initializer="ones", bias_initializer="zeros"))
10, kernel_initializer=initializer, bias_initializer="zeros"))
return model
@ -718,6 +720,48 @@ class MicroBenchmarks(test.Benchmark):
assert np.equal(func(), make_keras_model()(data)).all()
self._run(func, 30000)
def benchmark_keras_model_subclassed_fit(self):
model = SubclassedKerasModel(initializer="glorot_uniform")
data = random_ops.random_uniform((10, 10), minval=-1, maxval=1)
labels = random_ops.random_uniform((10, 10), minval=-1, maxval=1)
dataset = dataset_ops.Dataset.from_tensors((data, labels)).repeat()
model.compile(
gradient_descent.GradientDescentOptimizer(learning_rate=0.001),
loss="mse")
func = lambda: model.fit(dataset, epochs=1, steps_per_epoch=1000, verbose=0)
# First call is more expensive (creates variables etc.), discount that.
model.fit(dataset, epochs=1, steps_per_epoch=1, verbose=0)
self._run(func, 1)
def benchmark_keras_model_functional_fit(self):
model = make_keras_model(initializer="glorot_uniform")
data = random_ops.random_uniform((10, 10), minval=-1, maxval=1)
labels = random_ops.random_uniform((10, 10), minval=-1, maxval=1)
dataset = dataset_ops.Dataset.from_tensors((data, labels)).repeat()
model.compile(
gradient_descent.GradientDescentOptimizer(learning_rate=0.001),
loss="mse")
func = lambda: model.fit(dataset, epochs=1, steps_per_epoch=1000, verbose=0)
# First call is more expensive (creates variables etc.), discount that.
model.fit(dataset, epochs=1, steps_per_epoch=1, verbose=0)
self._run(func, 1)
def benchmark_keras_model_sequential_fit(self):
model = make_sequential_keras_model(initializer="glorot_uniform")
data = random_ops.random_uniform((10, 10), minval=-1, maxval=1)
labels = random_ops.random_uniform((10, 10), minval=-1, maxval=1)
dataset = dataset_ops.Dataset.from_tensors((data, labels)).repeat()
model.compile(
gradient_descent.GradientDescentOptimizer(learning_rate=0.001),
loss="mse")
func = lambda: model.fit(dataset, epochs=1, steps_per_epoch=1000, verbose=0)
# First call is more expensive (creates variables etc.), discount that.
model.fit(dataset, epochs=1, steps_per_epoch=1, verbose=0)
self._run(func, 1)
def benchmarkScan(self):
elems = math_ops.range(1600)