在TPU上运行代码时，我尝试使用CrossShardOptimizer。我在这里定义优化器
optimizer = tf.contrib.tpu.CrossShardOptimizer(tf.train.AdamOptimizer(1.0)).minimize(loss)
这是我用来运行会话的代码
with tf.Session(tpu_address, graph=graph) as session:
for step in range(1, num_steps):
batch_data, batch_labels = generate_batch(
batch_size, num_inputs)
feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
_, l = session.run([optimizer, loss], feed_dict=feed_dict)
这是我得到的错误
Initialized
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1333 try:
-> 1334 return fn(*args)
1335 except errors.OpError as e:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata)
1320
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1406 self._session, options, feed_dict, fetch_list, target_list,
-> 1407 run_metadata)
1408
InvalidArgumentError: Input 1 to CrossReplicaSum operator must be a compile-time constant.
XLA compilation requires that operator arguments that represent shapes or dimensions be evaluated to concrete values at compile time. This error means that a shape or dimension argument could not be evaluated at compile time, usually because the value of the argument depends on a parameter to the computation, on a variable, or on a stateful operation such as a random number generator.
[[{{node CrossReplicaSum_1}} = CrossReplicaSum[T=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:TPU:0"](CrossReplicaSum_1/input, CrossReplicaSum_1/group_assignment)]]
[[{{node CrossReplicaSum_1}} = CrossReplicaSum[T=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:TPU:0"](CrossReplicaSum_1/input, CrossReplicaSum_1/group_assignment)]]
[[{{node CrossReplicaSum_G12}} = _Recv[client_terminated=false, recv_device="/job:tpu_worker/replica:0/task:0/device:CPU:0", send_device="/job:tpu_worker/replica:0/task:0/device:TPU:0", send_device_incarnation=2958871627099111662, tensor_name="edge_181_CrossReplicaSum", tensor_type=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-21-8c0ef10a68cf> in <module>()
24 #print(batch_data)
25 feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
---> 26 _, l = session.run([optimizer, loss], feed_dict=feed_dict)
27
28 if l == l:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and feed_dict_tensor):
1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata)
1153 else:
1154 results = []
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1326 if handle is None:
1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata)
1329 else:
1330 return self._do_call(_prun_fn, handle, feeds, fetches)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1346 pass
1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message)
1349
1350 def _extend_graph(self):
InvalidArgumentError: Input 1 to CrossReplicaSum operator must be a compile-time constant.
XLA compilation requires that operator arguments that represent shapes or dimensions be evaluated to concrete values at compile time. This error means that a shape or dimension argument could not be evaluated at compile time, usually because the value of the argument depends on a parameter to the computation, on a variable, or on a stateful operation such as a random number generator.
[[node CrossReplicaSum_1 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/ops/gen_tpu_ops.py:322) = CrossReplicaSum[T=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:TPU:0"](CrossReplicaSum_1/input, CrossReplicaSum_1/group_assignment)]]
[[node CrossReplicaSum_1 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/ops/gen_tpu_ops.py:322) = CrossReplicaSum[T=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:TPU:0"](CrossReplicaSum_1/input, CrossReplicaSum_1/group_assignment)]]
[[{{node CrossReplicaSum_G12}} = _Recv[client_terminated=false, recv_device="/job:tpu_worker/replica:0/task:0/device:CPU:0", send_device="/job:tpu_worker/replica:0/task:0/device:TPU:0", send_device_incarnation=2958871627099111662, tensor_name="edge_181_CrossReplicaSum", tensor_type=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'CrossReplicaSum_1', defined at:
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-20-ecaf96c21b22>", line 42, in <module>
optimizer = tf.contrib.tpu.CrossShardOptimizer(tf.train.AdamOptimizer(1.0)).minimize(loss)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/optimizer.py", line 410, in minimize
name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py", line 170, in apply_gradients
grad, self._group_assignment), var))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/python/ops/tpu_ops.py", line 113, in cross_replica_sum
return gen_tpu_ops.cross_replica_sum(x, group_assignment, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/ops/gen_tpu_ops.py", line 322, in cross_replica_sum
name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Input 1 to CrossReplicaSum operator must be a compile-time constant.
XLA compilation requires that operator arguments that represent shapes or dimensions be evaluated to concrete values at compile time. This error means that a shape or dimension argument could not be evaluated at compile time, usually because the value of the argument depends on a parameter to the computation, on a variable, or on a stateful operation such as a random number generator.
[[node CrossReplicaSum_1 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/ops/gen_tpu_ops.py:322) = CrossReplicaSum[T=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:TPU:0"](CrossReplicaSum_1/input, CrossReplicaSum_1/group_assignment)]]
[[node CrossReplicaSum_1 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/ops/gen_tpu_ops.py:322) = CrossReplicaSum[T=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:TPU:0"](CrossReplicaSum_1/input, CrossReplicaSum_1/group_assignment)]]
[[{{node CrossReplicaSum_G12}} = _Recv[client_terminated=false, recv_device="/job:tpu_worker/replica:0/task:0/device:CPU:0", send_device="/job:tpu_worker/replica:0/task:0/device:TPU:0", send_device_incarnation=2958871627099111662, tensor_name="edge_181_CrossReplicaSum", tensor_type=DT_FLOAT, _device="/job:tpu_worker/replica:0/task:0/device:CPU:0"]()]]
这里是我Colab笔记本中完整代码的链接。一切都已设置好，包括自动下载我的数据。
https://drive.google.com/open?id=1dgYblB6zg9PmIulaaVo9Lvhd5viuPtIp
在其余部分在TPU上进行评估时，它似乎可能试图在CPU上进行评估，但是我将整个图形的目标设置为TPU地址，所以我不确定出什么问题。