Tensorflow （四） Graph和Function
Graph
tf.graph在TensorFlow中主要用于性能优化。我们用TensorFlow写代码时可以通过python的内部机制进行计算，这种操作称为Eagerly。而Graph操作会运用TensorFlow所内含的数据计算模块，相比之下更有效率。
Graph是用于tf.Operation操作的数据结构，而graph流中计算的基本数据单元是tf.Tensor，它们在graph上下文中所定义。这些graph还可以用于非python代码的互通。
详细来说，Graph作用于以下方面：
- 通过在计算中折叠常数节点来静态推断张量的值(“常数折叠”)。
- 独立的计算子部分，并在线程或设备之间分割它们。
- 通过消除公共子表达式简化算术运算。
简单来说，使用graph的好处主要有：速度快，支持并行和支持多设备优化几个方面
定义Graph
定义和使用graph的方式是使用tf.function，可以选择用tf.function()构建函数，或者用@tf.function()装饰器。
# Define a Python function.
def a_regular_function(x, y, b):
x = tf.matmul(x, y)
x = x + b
return x
# `a_function_that_uses_a_graph` is a TensorFlow `Function`.
a_function_that_uses_a_graph = tf.function(a_regular_function)
# Make some tensors.
x1 = tf.constant([[1.0, 2.0]])
y1 = tf.constant([[2.0], [3.0]])
b1 = tf.constant(4.0)
orig_value = a_regular_function(x1, y1, b1).numpy()
# Call a `Function` like a Python function.
tf_function_value = a_function_that_uses_a_graph(x1, y1, b1).numpy()
assert(orig_value == tf_function_value)
在使用上，tf.function好像和常规的function没什么区别，但是实际上，一个function可能封装了多个tf.graph，在计算效率和部署上两者会有很大的不同。
tf.function运用graph的特性适用于其所调用的其它函数。
def inner_function(x, y, b):
x = tf.matmul(x, y)
x = x + b
return x
# Use the decorator to make `outer_function` a `Function`.
@tf.function
def outer_function(x):
y = tf.constant([[2.0], [3.0]])
b = tf.constant(4.0)
return inner_function(x, y, b)
# Note that the callable will create a graph that
# includes `inner_function` as well as `outer_function`.
outer_function(tf.constant([[1.0, 2.0]])).numpy()
Autograph
我们在定义一个tf.function时，实际上在TensorFlow的内部会用AutoGraph (tf.autograph)将其转换成一个新的函数。
下面是一个示例写一个Relu函数然后可以看看转化的样子：
def simple_relu(x):
if tf.greater(x, 0):
return x
else:
return 0
# `tf_simple_relu` is a TensorFlow `Function` that wraps `simple_relu`.
tf_simple_relu = tf.function(simple_relu)
print("First branch, with graph:", tf_simple_relu(tf.constant(1)).numpy())
print("Second branch, with graph:", tf_simple_relu(tf.constant(-1)).numpy())
# This is the graph-generating output of AutoGraph.
print(tf.autograph.to_code(simple_relu))
First branch, with graph: 1
Second branch, with graph: 0
转换以后的函数：
def tf__simple_relu(x):
with ag__.FunctionScope('simple_relu', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:
do_return = False
retval_ = ag__.UndefinedReturnValue()
def get_state():
return (do_return, retval_)
def set_state(vars_):
nonlocal do_return, retval_
(do_return, retval_) = vars_
def if_body():
nonlocal do_return, retval_
try:
do_return = True
retval_ = ag__.ld(x)
except:
do_return = False
raise
def else_body():
nonlocal do_return, retval_
try:
do_return = True
retval_ = 0
except:
do_return = False
raise
ag__.if_stmt(ag__.converted_call(ag__.ld(tf).greater, (ag__.ld(x), 0), None, fscope), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
return fscope.ret(retval_, do_return)
还可以看看其graph的样子：
# This is the graph itself.
print(tf_simple_relu.get_concrete_function(tf.constant(1)).graph.as_graph_def())
输出：
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min_consumer: 12
}
Graph的多态性
Graph具有多态性，当为一个tf.function传递参数时，tf根据传递的参数类型来创建一个graph，并为这个graph赋予一个id。当之后传递相同类型的参数时，tf.function会根据参数类型来匹配之前创建过的graph
这里的参数类型必须是tensorflow的类型才能判定成同种参数！
@tf.function
def my_relu(x):
return tf.maximum(0., x)
# `my_relu` creates new graphs as it observes more signatures.
print(my_relu(tf.constant(5.5)))
print(my_relu([1, -1]))
print(my_relu(tf.constant([3., -3.])))
# These two calls do *not* create new graphs.
print(my_relu(tf.constant(-2.5))) # Signature matches `tf.constant(5.5)`.
print(my_relu(tf.constant([-1., 1.]))) # Signature matches `tf.constant([3., -3.])`.
tf.Tensor(5.5, shape=(), dtype=float32)
tf.Tensor([1. 0.], shape=(2,), dtype=float32)
tf.Tensor([3. 0.], shape=(2,), dtype=float32)
tf.Tensor(0.0, shape=(), dtype=float32)
tf.Tensor([0. 1.], shape=(2,), dtype=float32)
tf.graph会根据传递参数的不同选择其记忆的graph，也因此可以实现性能的优化
# There are three `ConcreteFunction`s (one for each graph) in `my_relu`.
# The `ConcreteFunction` also knows the return type and shape!
print(my_relu.pretty_printed_concrete_signatures())
my_relu(x)
Args:
x: float32 Tensor, shape=()
Returns:
float32 Tensor, shape=()
my_relu(x=[1, -1])
Returns:
float32 Tensor, shape=(2,)
my_relu(x)
Args:
x: float32 Tensor, shape=(2,)
Returns:
float32 Tensor, shape=(2,)
Tracing
Tracing是graph的一个特性，它会捕捉TensorFlow中其需要的部分。下面一个例子中，由于print()不在捕捉范围内，下面这段代码中的print不会多次执行。
@tf.function
def get_MSE(y_true, y_pred):
print("Calculating MSE!")
sq_diff = tf.pow(y_true - y_pred, 2)
return tf.reduce_mean(sq_diff)
tf.config.run_functions_eagerly(False)
y_true = tf.random.uniform([5], maxval=10, dtype=tf.int32)
y_pred = tf.random.uniform([5], maxval=10, dtype=tf.int32)
error = get_MSE(y_true, y_pred)
error = get_MSE(y_true, y_pred)
error = get_MSE(y_true, y_pred)
Calculating MSE!
会发现 Calculating MSE! 只输出了一次。
其中tf.config.run_functions_eagerly(False)可以控制在tf函数中是否采用eagerly的方式。
如果用tf.print()一定会多次输出，因为在tracing的范围内。
Non-strict execution
当我们定义一个tf.graph时，tf.graph只操作observable的内容，包括：
- 函数的返回值
- 一些特定的操作，如：
- 输入或输出算子，如
tf.operation - Debug操作：
tf.Debugging tf.Variable相关
- 输入或输出算子，如
这种行为称为Non-strict execution，与之无关的操作，哪怕存在error，执行时都不会管，如下所示：
def unused_return_eager(x):
# Get index 1 will fail when `len(x) == 1`
tf.gather(x, [1]) # unused
return x
try:
print(unused_return_eager(tf.constant([0.0])))
except tf.errors.InvalidArgumentError as e:
# All operations are run during eager execution so an error is raised.
print(f'{type(e).__name__}: {e}')
InvalidArgumentError: indices[0] = 1 is not in [0, 1) [Op:GatherV2]
这个例子中，如果定义为tf.function,即使存在索引问题，graph也不会处理：
@tf.function
def unused_return_graph(x):
tf.gather(x, [1]) # unused
return x
# Only needed operations are run during graph exection. The error is not raised.
print(unused_return_graph(tf.constant([0.0])))
tf.Tensor([0.], shape=(1,), dtype=float32)
