`tensorflow-metal` is not supported, errors when using tf with GPU on a mac - Some of the examples on doc does not work

[jackaraz]

Description

Hi, I'm trying to execute some of the examples that you have on your documentation. However, it's having some device-related issues. I don't have NVIDIA GPU (it's an M1 Mac), but it automatically sets the device to GPU even though the device seems to be set to False.

print(sampler.devices)
# {'/device:GPU:0': False}

Is it possible to change the use to CPU instead? I couldn't find a set device function in Vegasflow. Should it be done through tensorflow?

Thanks for your help.
Cheers

Code example

from vegasflow import VegasFlow, run_eager
import tensorflow as tf

run_eager(True)

def my_complicated_fun(xarr, **kwargs):
  return tf.reduce_sum(xarr, axis=1)

n_dim = 10
n_events = int(1e5)
sampler = VegasFlow(n_dim, n_events, verbose=False)
sampler.compile(my_complicated_fun)

# Now let's train the integrator for 10 iterations
_ = sampler.run_integration(10)

# Now we can use sampler to generate random numbers
rnds, _, px = sampler.generate_random_array(100)

Additional information

import tensorflow as tf
import vegasflow

print(f"Vegasflow: {vegasflow.__version__}")
# Vegasflow: 1.3.0
print(f"Tensorflow: {tf.__version__}")
# Tensorflow: 2.14.0
print(f"tf-mkl: {tf.python.framework.test_util.IsMklEnabled()}")
# AttributeError: module 'tensorflow' has no attribute 'python'
print(f"tf-cuda: {tf.test.is_built_with_cuda()}")
# tf-cuda: False
print(f"GPU available: {tf.test.is_gpu_available()}")
# GPU available: True

Traceback

[INFO] (vegasflow.monte_carlo) Checking whether the integrand outputs the correct shape (note, this will run a very small number of events and potentially trigger a retrace)
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
Cell In[103], line 15
     12 sampler.compile(my_complicated_fun)
     14 # Now let's train the integrator for 10 iterations
---> 15 _ = sampler.run_integration(10)
     17 # Now we can use sampler to generate random numbers
     18 rnds, _, px = sampler.generate_random_array(100)

File ~/packages/miniconda3/lib/python3.9/site-packages/vegasflow/monte_carlo.py:682, in MonteCarloFlow.run_integration(self, n_iter, log_time, histograms)
    679     start = time.time()
    681 # Run one single iteration and append results
--> 682 res, error = self._run_iteration()
    683 all_results.append((res, error))
    685 # If there is a histogram variable, store it and empty it

File ~/packages/miniconda3/lib/python3.9/site-packages/vegasflow/vflow.py:447, in VegasFlow._run_iteration(self)
    445 def _run_iteration(self):
    446     """Runs one iteration of the Vegas integrator"""
--> 447     return self._iteration_content()

File ~/packages/miniconda3/lib/python3.9/site-packages/vegasflow/vflow.py:442, in VegasFlow._iteration_content(self)
    440 # If training is active, act post integration
    441 if self.train:
--> 442     self.refine_grid(arr_res2)
    443 return res, sigma

File ~/packages/miniconda3/lib/python3.9/site-packages/vegasflow/vflow.py:363, in VegasFlow.refine_grid(self, arr_res2)
    361 for j in range(self.n_dim):
    362     new_divisions = refine_grid_per_dimension(arr_res2[j, :], self.divisions[j, :])
--> 363     self.divisions[j, :].assign(new_divisions)

File ~/packages/miniconda3/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py:1359, in strided_slice.<locals>.assign(val, name)
   1356 if name is None:
   1357   name = parent_name + "_assign"
-> 1359 return var._strided_slice_assign(
   1360     begin=begin,
   1361     end=end,
   1362     strides=strides,
   1363     value=val,
   1364     name=name,
   1365     begin_mask=begin_mask,
   1366     end_mask=end_mask,
   1367     ellipsis_mask=ellipsis_mask,
   1368     new_axis_mask=new_axis_mask,
   1369     shrink_axis_mask=shrink_axis_mask)

File ~/packages/miniconda3/lib/python3.9/site-packages/tensorflow/python/ops/resource_variable_ops.py:1523, in BaseResourceVariable._strided_slice_assign(self, begin, end, strides, value, name, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask)
   1518 def _strided_slice_assign(self, begin, end, strides, value, name, begin_mask,
   1519                           end_mask, ellipsis_mask, new_axis_mask,
   1520                           shrink_axis_mask):
   1521   with _handle_graph(self.handle), self._assign_dependencies():
   1522     return self._lazy_read(
-> 1523         gen_array_ops.resource_strided_slice_assign(
   1524             ref=self.handle,
   1525             begin=begin,
   1526             end=end,
   1527             strides=strides,
   1528             value=ops.convert_to_tensor(value, dtype=self.dtype),
   1529             name=name,
   1530             begin_mask=begin_mask,
   1531             end_mask=end_mask,
   1532             ellipsis_mask=ellipsis_mask,
   1533             new_axis_mask=new_axis_mask,
   1534             shrink_axis_mask=shrink_axis_mask))

File ~/packages/miniconda3/lib/python3.9/site-packages/tensorflow/python/ops/gen_array_ops.py:8826, in resource_strided_slice_assign(ref, begin, end, strides, value, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask, name)
   8824   return _result
   8825 except _core._NotOkStatusException as e:
-> 8826   _ops.raise_from_not_ok_status(e, name)
   8827 except _core._FallbackException:
   8828   pass

File ~/packages/miniconda3/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:5888, in raise_from_not_ok_status(e, name)
   5886 def raise_from_not_ok_status(e, name) -> NoReturn:
   5887   e.message += (" name: " + str(name if name is not None else ""))
-> 5888   raise core._status_to_exception(e) from None

InvalidArgumentError: Cannot assign a device for operation ResourceStridedSliceAssign: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices: 
Root Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[]
ResourceStridedSliceAssign: CPU 
_Arg: GPU CPU 

Colocation members, user-requested devices, and framework assigned devices, if any:
  ref (_Arg)  framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0
  ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0

Op: ResourceStridedSliceAssign
Node attrs: new_axis_mask=0, Index=DT_INT32, shrink_axis_mask=1, end_mask=2, ellipsis_mask=0, begin_mask=2, T=DT_DOUBLE
Registered kernels:
  device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]
  device='DEFAULT'; T in [DT_INT32]
  device='CPU'; T in [DT_UINT64]
  device='CPU'; T in [DT_INT64]
  device='CPU'; T in [DT_UINT32]
  device='CPU'; T in [DT_UINT16]
  device='CPU'; T in [DT_INT16]
  device='CPU'; T in [DT_UINT8]
  device='CPU'; T in [DT_INT8]
  device='CPU'; T in [DT_INT32]
  device='CPU'; T in [DT_HALF]
  device='CPU'; T in [DT_BFLOAT16]
  device='CPU'; T in [DT_FLOAT]
  device='CPU'; T in [DT_DOUBLE]
  device='CPU'; T in [DT_COMPLEX64]
  device='CPU'; T in [DT_COMPLEX128]
  device='CPU'; T in [DT_BOOL]
  device='CPU'; T in [DT_STRING]
  device='CPU'; T in [DT_RESOURCE]
  device='CPU'; T in [DT_VARIANT]
  device='CPU'; T in [DT_QINT8]
  device='CPU'; T in [DT_QUINT8]
  device='CPU'; T in [DT_QINT32]

	 [[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign

[scarlehoff]

How did you install tensorflow? It should not say that the GPU is available if it is not true.

I'm also using a M1 mac and, indeed,print(f"GPU available: {tf.test.is_gpu_available()}") returns False. I cannot reproduce the error, however, maybe you can control whether tensorflow sees or not a GPU with CUDA_VISIBLE_DEVICES=""

What version of python are you using? (I need to update that issue template!)

Also, since it is probably important in a mac, are you using python from the system, brew or conda?

[jackaraz]

Hi @scarlehoff

How did you install tensorflow? It should not say that the GPU is available if it is not true.

By following this link.

What version of python are you using?

im using python 3.9.18

I cannot reproduce the error, however, maybe you can control whether tensorflow sees or not a GPU with CUDA_VISIBLE_DEVICES=""

I tried to set os.environ["CUDA_VISIBLE_DEVICES"] = "" but I'm still getting the same error. I also tried setting CPU via

 physical_devices = tf.config.list_physical_devices('CPU')
 tf.config.set_visible_devices(physical_devices[0], 'CPU')

as well as

from vegasflow import configflow
configflow.DEFAULT_ACTIVE_DEVICES = ["CPU"]
...
vegas_instance = VegasFlow(dims, n_calls, list_devices=["CPU"])
...

But non of them helped.

[scarlehoff]

Ah... I see, the problem is tensorflow-metal.

A possible solution is to do at the beginning of the code:

import tensorflow as tf
tf.configl.set_visible_devices([], 'GPU')

Basically the opposite strategy of what you did, telling tf "your GPUs are... None" :P

However, I'd like to understand why M1 doesn't work. As long as you are not using complex types it should be fine. Maybe we are doing something that tensorflow-metal doesn't support yet. I'll investigate. Thanks for the report (I'll change the title to make the bug evident!)

btw, the example itself in the docs is now wrong, it should be rnds, px = sampler.generate_random_array(100), I removed the extra output some time ago but it seems I forgot to update the docs, sorry about that.

[jackaraz]

Aha! Thanks, Juan; that indeed fixes the issue!