根据该线程（NotFoundError on OpKernel when using tf.nn.embedding_lookup in tensorflow eager mode）的答案，某些操作尚未在GPU上实现。
我在操作上遇到了问题，我还收到一个 NotFoundError ，但错误消息使我感到困惑。这是我使用Tensorflow 1.10的示例代码。我知道我可以省略设备强制操作，并且tensorflow将在CPU上运行该操作，但是我想在GPU上做更多的事情。
import tensorflow as tf
tf.enable_eager_execution()
print("Eager execution: {}".format(tf.executing_eagerly()))
device = 'gpu:0'
with tf.device(device):
x = tf.constant([195330., 195075., 173910., 167535., 167535., 170340., 206040., 175185., 206040.,
118575., 214710., 171870., 204765., 202215., 0., 0., 0., 0.,
0., 0.], dtype=tf.float32)
print(tf.count_nonzero(x))
我收到以下错误：
python3 test.py
Eager execution: True
2018-09-28 14:41:51.186066: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: FMA
2018-09-28 14:41:51.370081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 5.93GiB freeMemory: 5.38GiB
2018-09-28 14:41:51.467475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 1 with properties:
name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015
pciBusID: 0000:02:00.0
totalMemory: 1.96GiB freeMemory: 1.93GiB
2018-09-28 14:41:51.467534: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1469] Ignoring visible gpu device (device: 1, name: GeForce GT 730, pci bus id: 0000:02:00.0, compute capability: 3.5) with Cuda multiprocessor count: 2. The minimum required count is 8. You can adjust this requirement with the env var TF_MIN_GPU_MULTIPROCESSOR_COUNT.
2018-09-28 14:41:51.467543: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-28 14:41:51.848119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-28 14:41:51.848172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0 1
2018-09-28 14:41:51.848195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N N
2018-09-28 14:41:51.848206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1: N N
2018-09-28 14:41:51.848446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5143 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
Traceback (most recent call last):
File "test.py", line 13, in <module>
print(tf.count_nonzero(x))
File "/home/joe/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 454, in new_func
return func(*args, **kwargs)
File "/home/joe/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 1384, in count_nonzero
reduction_indices=reduction_indices),
File "/home/joe/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 454, in new_func
return func(*args, **kwargs)
File "/home/joe/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 1307, in reduce_sum
name=name))
File "/home/joe/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 8283, in _sum
_six.raise_from(_core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.NotFoundError: No registered 'Sum' OpKernel for GPU devices compatible with node Sum = Sum[T=DT_INT64, Tidx=DT_INT32, keep_dims=false](dummy_input, dummy_input)
(OpKernel was found, but attributes didn't match)
. Registered: device='CPU'; T in [DT_COMPLEX128]; Tidx in [DT_INT64]
device='CPU'; T in [DT_COMPLEX128]; Tidx in [DT_INT32]
device='CPU'; T in [DT_COMPLEX64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_COMPLEX64]; Tidx in [DT_INT32]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT32]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='GPU'; T in [DT_COMPLEX128]; Tidx in [DT_INT64]
device='GPU'; T in [DT_COMPLEX128]; Tidx in [DT_INT32]
device='GPU'; T in [DT_COMPLEX64]; Tidx in [DT_INT64]
device='GPU'; T in [DT_COMPLEX64]; Tidx in [DT_INT32]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT32]
[Op:Sum]
据我了解的错误
No registered 'Sum' OpKernel for GPU devices compatible with node Sum = Sum[T=DT_INT64, Tidx=DT_INT32, keep_dims=false](dummy_input, dummy_input)
它寻找 T = DT_INT64，Tidx = DT_INT32 的实现，但是Tensor来自 float32 类型。我想念什么吗？
答案 0 :(得分：0)
查看实现here：
def count_nonzero(input_tensor,
axis=None,
keepdims=None,
dtype=dtypes.int64,
name=None,
reduction_indices=None,
keep_dims=None):
keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
"keep_dims", keep_dims)
if keepdims is None:
keepdims = False
with ops.name_scope(name, "count_nonzero", [input_tensor]):
input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
# A scalar of 'zero' is enough as `not_equal` will broadcast.
zero = array_ops.zeros([], dtype=input_tensor.dtype)
return cast(
reduce_sum(
# int64 reduction happens on GPU
to_int64(gen_math_ops.not_equal(input_tensor, zero)),
axis=axis,
keepdims=keepdims,
reduction_indices=reduction_indices),
dtype=dtype)
请注意在调用int64之前如何对reduce_sum进行强制转换。这就是为什么TF在int64而不是原始的float32上搜索操作的原因。
答案 1 :(得分：0)
我用更大和 reduce_sum 的组合代替了 count_nonzero （将布尔数组从Greater-op转换为float32） 。现在它可以在GPU上运行了：
print(tf.reduce_sum(tf.cast(tf.greater(x, 0), tf.float32)))