输入张量 <name> 进入循环的形状为 ()，但在一次迭代后形状为 <unknown>
Input tensor <name> enters the loop with shape (), but has shape <unknown> after one iteration
我正在尝试使用贪婪解码方法 tf.function 保存模型。
代码已经过测试，按预期在急切模式（调试）下工作。但是，它在非急切执行中不起作用。
该方法得到一个名为 Hyp 的 namedtuple，如下所示：
Hyp = namedtuple(
'Hyp',
field_names='score, yseq, encoder_state, decoder_state, decoder_output'
)
while 循环被这样调用：
_, hyp = tf.while_loop(
cond=condition_,
body=body_,
loop_vars=(tf.constant(0, dtype=tf.int32), hyp),
shape_invariants=(
tf.TensorShape([]),
tf.nest.map_structure(get_shape_invariants, hyp),
)
)
这是body_的相关部分：
def body_(i_, hypothesis_: Hyp):
# [:] Collapsed some code ..
def update_from_next_id_():
return Hyp(
# Update values ..
)
# The only place where I generate a new hypothesis_ namedtuple
hypothesis_ = tf.cond(
tf.not_equal(next_id, blank),
true_fn=lambda: update_from_next_id_(),
false_fn=lambda: hypothesis_
)
return i_ + 1, hypothesis_
我得到的是 ValueError:
ValueError: Input tensor 'hypotheses:0' enters the loop with shape (), but has shape <unknown> after one iteration. To allow the shape to vary across iterations, use the shape_invariants argument of tf.while_loop to specify a less-specific shape.
这可能是什么问题？
下面是 input_signature 是如何定义的 tf.function 我想序列化。
在这里，self.greedy_decode_impl 是实际的实现 - 我知道这在这里有点难看，但 self.greedy_decode 是我所说的。
self.greedy_decode = tf.function(
self.greedy_decode_impl,
input_signature=(
tf.TensorSpec([1, None, self.config.encoder.lstm_units], dtype=tf.float32),
Hyp(
score=tf.TensorSpec([], dtype=tf.float32),
yseq=tf.TensorSpec([1, None], dtype=tf.int32),
encoder_state=tuple(
(tf.TensorSpec([1, lstm.units], dtype=tf.float32),
tf.TensorSpec([1, lstm.units], dtype=tf.float32))
for (lstm, _) in self.encoder_network.lstm_stack
),
decoder_state=tuple(
(tf.TensorSpec([1, lstm.units], dtype=tf.float32),
tf.TensorSpec([1, lstm.units], dtype=tf.float32))
for (lstm, _) in self.predict_network.lstm_stack
),
decoder_output=tf.TensorSpec([1, None, self.config.decoder.lstm_units], dtype=tf.float32)
),
)
)
执行greedy_decode_impl：
def greedy_decode_impl(self, encoder_outputs: tf.Tensor, hypotheses: Hyp, blank=0) -> Hyp:
hyp = hypotheses
encoder_outputs = encoder_outputs[0]
def condition_(i_, *_):
time_steps = tf.shape(encoder_outputs)[0]
return tf.less(i_, time_steps)
def body_(i_, hypothesis_: Hyp):
encoder_output_ = tf.reshape(encoder_outputs[i_], shape=(1, 1, -1))
join_out = self.join_network((encoder_output_, hypothesis_.decoder_output), training=False)
logits = tf.squeeze(tf.nn.log_softmax(tf.squeeze(join_out)))
next_id = tf.argmax(logits, output_type=tf.int32)
log_prob = logits[next_id]
next_id = tf.reshape(next_id, (1, 1))
def update_from_next_id_():
decoder_output_, decoder_state_ = self.predict_network(
next_id,
memory_states=hypothesis_.decoder_state,
training=False
)
return Hyp(
score=hypothesis_.score + log_prob,
yseq=tf.concat([hypothesis_.yseq, next_id], axis=0),
decoder_state=decoder_state_,
decoder_output=decoder_output_,
encoder_state=hypothesis_.encoder_state
)
hypothesis_ = tf.cond(
tf.not_equal(next_id, blank),
true_fn=lambda: update_from_next_id_(),
false_fn=lambda: hypothesis_
)
return i_ + 1, hypothesis_
_, hyp = tf.while_loop(
cond=condition_,
body=body_,
loop_vars=(tf.constant(0, dtype=tf.int32), hyp),
shape_invariants=(
tf.TensorShape([]),
tf.nest.map_structure(get_shape_invariants, hyp),
)
)
return hyp
为什么它在 eager-mode 下工作而不在 non-eager 模式下工作？
根据 tf.while_loop 的文档，namedtuple 应该可以使用。
斐波那契示例
为了检查这是否适用于 namedtuple，我使用类似的机制实现了斐波那契数列。为了包含条件，循环在到达步骤 n // 2:
时停止附加新数字
正如我们在下面看到的，该方法应该没有 Python 副作用。
from collections import namedtuple
import tensorflow as tf
FibonacciStep = namedtuple('FibonacciStep', field_names='seq, prev_value')
def shape_list(x):
static = x.shape.as_list()
dynamic = tf.shape(x)
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
def get_shape_invariants(tensor):
shapes = shape_list(tensor)
return tf.TensorShape([i if isinstance(i, int) else None for i in shapes])
def save_tflite(fp, concrete_fn):
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_fn])
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations = []
tflite_model = converter.convert()
with tf.io.gfile.GFile(fp, 'wb') as f:
f.write(tflite_model)
@tf.function(
input_signature=(
tf.TensorSpec([], dtype=tf.int32),
FibonacciStep(
seq=tf.TensorSpec([1, None], dtype=tf.int32),
prev_value=tf.TensorSpec([], dtype=tf.int32),
)
)
)
def fibonacci(n: tf.Tensor, fibo: FibonacciStep):
def cond_(i_, *args):
return tf.less(i_, n)
def body_(i_, fibo_: FibonacciStep):
prev_value = fibo_.seq[0, -1] + fibo_.prev_value
def append_value():
return FibonacciStep(
seq=tf.concat([fibo_.seq, tf.reshape(prev_value, shape=(1, 1))], axis=-1),
prev_value=fibo_.seq[0, -1]
)
fibo_ = tf.cond(
tf.less_equal(i_, n // 2),
true_fn=lambda: append_value(),
false_fn=lambda: fibo_
)
return i_ + 1, fibo_
_, fibo = tf.while_loop(
cond=cond_,
body=body_,
loop_vars=(0, fibo),
shape_invariants=(
tf.TensorShape([]),
tf.nest.map_structure(get_shape_invariants, fibo),
)
)
return fibo
def main():
n = tf.constant(10, dtype=tf.int32)
fibo = FibonacciStep(
seq=tf.constant([[0, 1]], dtype=tf.int32),
prev_value=tf.constant(0, dtype=tf.int32),
)
fibo = fibonacci(n, fibo=fibo)
fibo = fibonacci(n + 10, fibo=fibo)
fp = '/tmp/fibonacci.tflite'
concrete_fn = fibonacci.get_concrete_function()
save_tflite(fp, concrete_fn)
print(fibo.seq.numpy()[0].tolist())
print('All done.')
if __name__ == '__main__':
main()
输出：
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584]
All done.
好吧，结果是
tf.concat([hypothesis_.yseq, next_id], axis=0),
应该是
tf.concat([hypothesis_.yseq, next_id], axis=-1),
公平地说，错误消息有点给了你一个提示，但用“有用”来形容它太过分了。我通过连接错误的轴违反了 TensorSpec，仅此而已，但 Tensorflow 无法直接指向受影响的张量（目前）。
我正在尝试使用贪婪解码方法 tf.function 保存模型。
代码已经过测试，按预期在急切模式（调试）下工作。但是，它在非急切执行中不起作用。
该方法得到一个名为 Hyp 的 namedtuple，如下所示：
Hyp = namedtuple(
'Hyp',
field_names='score, yseq, encoder_state, decoder_state, decoder_output'
)
while 循环被这样调用：
_, hyp = tf.while_loop(
cond=condition_,
body=body_,
loop_vars=(tf.constant(0, dtype=tf.int32), hyp),
shape_invariants=(
tf.TensorShape([]),
tf.nest.map_structure(get_shape_invariants, hyp),
)
)
这是body_的相关部分：
def body_(i_, hypothesis_: Hyp):
# [:] Collapsed some code ..
def update_from_next_id_():
return Hyp(
# Update values ..
)
# The only place where I generate a new hypothesis_ namedtuple
hypothesis_ = tf.cond(
tf.not_equal(next_id, blank),
true_fn=lambda: update_from_next_id_(),
false_fn=lambda: hypothesis_
)
return i_ + 1, hypothesis_
我得到的是 ValueError:
ValueError: Input tensor 'hypotheses:0' enters the loop with shape (), but has shape <unknown> after one iteration. To allow the shape to vary across iterations, use theshape_invariantsargument of tf.while_loop to specify a less-specific shape.
这可能是什么问题？
下面是 input_signature 是如何定义的 tf.function 我想序列化。
在这里，self.greedy_decode_impl 是实际的实现 - 我知道这在这里有点难看，但 self.greedy_decode 是我所说的。
self.greedy_decode = tf.function(
self.greedy_decode_impl,
input_signature=(
tf.TensorSpec([1, None, self.config.encoder.lstm_units], dtype=tf.float32),
Hyp(
score=tf.TensorSpec([], dtype=tf.float32),
yseq=tf.TensorSpec([1, None], dtype=tf.int32),
encoder_state=tuple(
(tf.TensorSpec([1, lstm.units], dtype=tf.float32),
tf.TensorSpec([1, lstm.units], dtype=tf.float32))
for (lstm, _) in self.encoder_network.lstm_stack
),
decoder_state=tuple(
(tf.TensorSpec([1, lstm.units], dtype=tf.float32),
tf.TensorSpec([1, lstm.units], dtype=tf.float32))
for (lstm, _) in self.predict_network.lstm_stack
),
decoder_output=tf.TensorSpec([1, None, self.config.decoder.lstm_units], dtype=tf.float32)
),
)
)
执行greedy_decode_impl：
def greedy_decode_impl(self, encoder_outputs: tf.Tensor, hypotheses: Hyp, blank=0) -> Hyp:
hyp = hypotheses
encoder_outputs = encoder_outputs[0]
def condition_(i_, *_):
time_steps = tf.shape(encoder_outputs)[0]
return tf.less(i_, time_steps)
def body_(i_, hypothesis_: Hyp):
encoder_output_ = tf.reshape(encoder_outputs[i_], shape=(1, 1, -1))
join_out = self.join_network((encoder_output_, hypothesis_.decoder_output), training=False)
logits = tf.squeeze(tf.nn.log_softmax(tf.squeeze(join_out)))
next_id = tf.argmax(logits, output_type=tf.int32)
log_prob = logits[next_id]
next_id = tf.reshape(next_id, (1, 1))
def update_from_next_id_():
decoder_output_, decoder_state_ = self.predict_network(
next_id,
memory_states=hypothesis_.decoder_state,
training=False
)
return Hyp(
score=hypothesis_.score + log_prob,
yseq=tf.concat([hypothesis_.yseq, next_id], axis=0),
decoder_state=decoder_state_,
decoder_output=decoder_output_,
encoder_state=hypothesis_.encoder_state
)
hypothesis_ = tf.cond(
tf.not_equal(next_id, blank),
true_fn=lambda: update_from_next_id_(),
false_fn=lambda: hypothesis_
)
return i_ + 1, hypothesis_
_, hyp = tf.while_loop(
cond=condition_,
body=body_,
loop_vars=(tf.constant(0, dtype=tf.int32), hyp),
shape_invariants=(
tf.TensorShape([]),
tf.nest.map_structure(get_shape_invariants, hyp),
)
)
return hyp
为什么它在 eager-mode 下工作而不在 non-eager 模式下工作？
根据 tf.while_loop 的文档，namedtuple 应该可以使用。
斐波那契示例
为了检查这是否适用于 namedtuple，我使用类似的机制实现了斐波那契数列。为了包含条件，循环在到达步骤 n // 2:
正如我们在下面看到的，该方法应该没有 Python 副作用。
from collections import namedtuple
import tensorflow as tf
FibonacciStep = namedtuple('FibonacciStep', field_names='seq, prev_value')
def shape_list(x):
static = x.shape.as_list()
dynamic = tf.shape(x)
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
def get_shape_invariants(tensor):
shapes = shape_list(tensor)
return tf.TensorShape([i if isinstance(i, int) else None for i in shapes])
def save_tflite(fp, concrete_fn):
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_fn])
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations = []
tflite_model = converter.convert()
with tf.io.gfile.GFile(fp, 'wb') as f:
f.write(tflite_model)
@tf.function(
input_signature=(
tf.TensorSpec([], dtype=tf.int32),
FibonacciStep(
seq=tf.TensorSpec([1, None], dtype=tf.int32),
prev_value=tf.TensorSpec([], dtype=tf.int32),
)
)
)
def fibonacci(n: tf.Tensor, fibo: FibonacciStep):
def cond_(i_, *args):
return tf.less(i_, n)
def body_(i_, fibo_: FibonacciStep):
prev_value = fibo_.seq[0, -1] + fibo_.prev_value
def append_value():
return FibonacciStep(
seq=tf.concat([fibo_.seq, tf.reshape(prev_value, shape=(1, 1))], axis=-1),
prev_value=fibo_.seq[0, -1]
)
fibo_ = tf.cond(
tf.less_equal(i_, n // 2),
true_fn=lambda: append_value(),
false_fn=lambda: fibo_
)
return i_ + 1, fibo_
_, fibo = tf.while_loop(
cond=cond_,
body=body_,
loop_vars=(0, fibo),
shape_invariants=(
tf.TensorShape([]),
tf.nest.map_structure(get_shape_invariants, fibo),
)
)
return fibo
def main():
n = tf.constant(10, dtype=tf.int32)
fibo = FibonacciStep(
seq=tf.constant([[0, 1]], dtype=tf.int32),
prev_value=tf.constant(0, dtype=tf.int32),
)
fibo = fibonacci(n, fibo=fibo)
fibo = fibonacci(n + 10, fibo=fibo)
fp = '/tmp/fibonacci.tflite'
concrete_fn = fibonacci.get_concrete_function()
save_tflite(fp, concrete_fn)
print(fibo.seq.numpy()[0].tolist())
print('All done.')
if __name__ == '__main__':
main()
输出：
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584]
All done.
好吧，结果是
tf.concat([hypothesis_.yseq, next_id], axis=0),
应该是
tf.concat([hypothesis_.yseq, next_id], axis=-1),
公平地说，错误消息有点给了你一个提示，但用“有用”来形容它太过分了。我通过连接错误的轴违反了 TensorSpec，仅此而已，但 Tensorflow 无法直接指向受影响的张量（目前）。