Python ops.device函数代码示例

  def testSizeAndClear(self):
    with ops.Graph().as_default() as G:
      with ops.device('/cpu:0'):
        x = array_ops.placeholder(dtypes.float32, name='x')
        pi = array_ops.placeholder(dtypes.int64)
        gi = array_ops.placeholder(dtypes.int64)
        v = 2. * (array_ops.zeros([128, 128]) + x)
      with ops.device(test.gpu_device_name()):
        stager = data_flow_ops.MapStagingArea(
            [dtypes.float32, dtypes.float32],
            shapes=[[], [128, 128]],
            names=['x', 'v'])
        stage = stager.put(pi, {'x': x, 'v': v})
        size = stager.size()
        clear = stager.clear()

    G.finalize()

    with self.session(use_gpu=True, graph=G) as sess:
      sess.run(stage, feed_dict={x: -1, pi: 3})
      self.assertEqual(sess.run(size), 1)
      sess.run(stage, feed_dict={x: -1, pi: 1})
      self.assertEqual(sess.run(size), 2)
      sess.run(clear)
      self.assertEqual(sess.run(size), 0)

  def testAnalysisAndAllocations(self):
    run_options = config_pb2.RunOptions(
        trace_level=config_pb2.RunOptions.FULL_TRACE)
    run_metadata = config_pb2.RunMetadata()
    config = config_pb2.ConfigProto(device_count={'CPU': 3})

    with session.Session(config=config) as sess:
      with ops.device('/cpu:0'):
        num1 = variables.Variable(1.0, name='num1')
      with ops.device('/cpu:1'):
        num2 = variables.Variable(2.0, name='num2')
      with ops.device('/cpu:2'):
        result = num1 + num2 + num1 * num2
      self.evaluate(variables.global_variables_initializer())
      sess.run(result, options=run_options, run_metadata=run_metadata)

    self.assertTrue(run_metadata.HasField('step_stats'))
    tl = timeline.Timeline(run_metadata.step_stats)
    step_analysis = tl.analyze_step_stats()
    ctf = step_analysis.chrome_trace.format_to_string()
    self._validateTrace(ctf)
    maximums = step_analysis.allocator_maximums
    cpuname = 'mklcpu' if test_util.IsMklEnabled() else 'cpu'
    self.assertTrue(cpuname in maximums)
    cpu_max = maximums[
        'cuda_host_bfc'] if 'cuda_host_bfc' in maximums else maximums[cpuname]
    # At least num1 + num2, both float32s (4 bytes each)
    self.assertGreaterEqual(cpu_max.num_bytes, 8)
    self.assertGreater(cpu_max.timestamp, 0)

  def testCopyToGPU(self):
    if not test_util.is_gpu_available():
      self.skipTest("No GPU available")

    with ops.device("/cpu:0"):
      optional_with_value = optional_ops.Optional.from_value(
          (constant_op.constant(37.0), constant_op.constant("Foo"),
           constant_op.constant(42)))
      optional_none = optional_ops.Optional.none_from_structure(
          structure.TensorStructure(dtypes.float32, []))

    with ops.device("/gpu:0"):
      gpu_optional_with_value = optional_ops._OptionalImpl(
          array_ops.identity(optional_with_value._variant_tensor),
          optional_with_value.value_structure)
      gpu_optional_none = optional_ops._OptionalImpl(
          array_ops.identity(optional_none._variant_tensor),
          optional_none.value_structure)

      gpu_optional_with_value_has_value = gpu_optional_with_value.has_value()
      gpu_optional_with_value_values = gpu_optional_with_value.get_value()

      gpu_optional_none_has_value = gpu_optional_none.has_value()

    self.assertTrue(self.evaluate(gpu_optional_with_value_has_value))
    self.assertEqual((37.0, b"Foo", 42),
                     self.evaluate(gpu_optional_with_value_values))
    self.assertFalse(self.evaluate(gpu_optional_none_has_value))

def _build_ring_gather(input_tensors, devices, num_subchunks,
                       pred_by_s_d, rank_by_s_d, red_op):
  """Construct a subgraph for the first (reduction) pass of ring all-reduce.

  Args:
    input_tensors: a list of T `tf.Tensor` 1D input tensors of same
      shape and type.
    devices: array of device name strings
    num_subchunks: number of subchunks each device should process in one tick.
    pred_by_s_d: as produced by _ring_permutations
    rank_by_s_d: as produced by _ring_permutations
    red_op: a binary operator for elementwise reduction

  Raises:
    ValueError: tensors must all be one dimensional.

  Returns:
    list of list of T `tf.Tensor` of (partially) reduced values where
    exactly num_subchunks chunks at each device are fully reduced.
  """
  num_devices = len(input_tensors)
  if num_devices == 0:
    return []
  if num_devices == 1:
    return input_tensors
  shape = input_tensors[0].shape
  if 1 != len(shape):
    raise ValueError("input tensors must be 1D")
  num_chunks = num_devices * num_subchunks
  num_ticks = num_devices - 1
  # Initialize chunks_by_dev with splits of the input tensors.
  chunks_by_dev = []
  split_pad_len = 0
  for d in range(0, num_devices):
    with ops.device(devices[d]):
      splits, split_pad_len = _padded_split(input_tensors[d], num_chunks)
      chunks_by_dev.append(splits)
  # Reduction phase
  for tick in range(0, num_ticks):
    # One new partial reduction for every chunk
    new_partial_reductions = [None for _ in range(0, num_chunks)]
    # Compute reductions with respect to last tick's values
    for d in range(0, num_devices):
      with ops.device(devices[d]):
        for s in range(0, num_subchunks):
          rank = rank_by_s_d[s][d]
          seg_index = (rank + num_devices - (2 + tick)) % num_devices
          pred_dev = pred_by_s_d[s][d]
          chunk_index = (seg_index * num_subchunks) + s
          new_partial_reductions[chunk_index] = red_op(
              chunks_by_dev[pred_dev][chunk_index],
              chunks_by_dev[d][chunk_index])
    # Update chunks_by_dev with the new values at the end of the tick.
    for d in range(0, num_devices):
      for s in range(0, num_subchunks):
        rank = rank_by_s_d[s][d]
        seg_index = (rank + num_devices - (2 + tick)) % num_devices
        chunk_index = (seg_index * num_subchunks) + s
        chunks_by_dev[d][chunk_index] = new_partial_reductions[chunk_index]
  return chunks_by_dev, split_pad_len

  def testSplitPipelineFailsWithPlacementError(self):
    with session.Session(
        target="",
        config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:

      dataset = dataset_ops.Dataset.from_tensors(0)

      # Define a pipeline that attempts to use variables on two
      # different devices.
      #
      # Initialize the variables before creating to iterator, to avoid the
      # placement algorithm overriding the DT_RESOURCE colocation constraints.
      with ops.device("/cpu:0"):
        var_0 = resource_variable_ops.ResourceVariable(initial_value=0)
        dataset = dataset.map(lambda x: x + var_0.read_value())
      sess.run(var_0.initializer)

      with ops.device("/cpu:1"):
        var_1 = resource_variable_ops.ResourceVariable(initial_value=0)
        dataset = dataset.map(lambda x: x + var_1.read_value())
      sess.run(var_1.initializer)

      iterator = dataset.make_initializable_iterator()
      sess.run(iterator.initializer)

      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          "Error while reading resource variable Variable"):
        sess.run(iterator.get_next())

def pack_range(key, packing, grad_vars, rng):
  """Form the concatenation of a specified range of gradient tensors.

  Args:
    key: Value under which to store meta-data in packing that will be used
      later to restore the grad_var list structure.
    packing: Dict holding data describing packed ranges of small tensors.
    grad_vars: List of (grad, var) pairs for one replica.
    rng: A pair of integers giving the first, last indices of a consecutive
      range of tensors to be packed.

  Returns:
    A tensor that is the concatenation of all the specified small tensors.
  """
  to_pack = grad_vars[rng[0]:rng[1] + 1]
  members = []
  variables = []
  restore_shapes = []
  with ops.name_scope('pack'):
    for g, v in to_pack:
      variables.append(v)
      restore_shapes.append(g.shape)
      with ops.device(g.device):
        members.append(array_ops.reshape(g, [-1]))
    packing[key] = GradPackTuple(
        indices=range(rng[0], rng[1] + 1),
        vars=variables,
        shapes=restore_shapes)
    with ops.device(members[0].device):
      return array_ops.concat(members, 0)

  def test_subscribe_tensors_on_different_devices(self):
    """Side effect ops are added with the same device of the subscribed op."""
    c1 = constant_op.constant(10)
    c2 = constant_op.constant(20)

    with ops.device('cpu:0'):
      add = math_ops.add(c1, c2)

    with ops.device('cpu:1'):
      mul = math_ops.multiply(c1, c2)

    def sub(t):
      return t

    add_sub = subscribe.subscribe(
        add, lambda t: script_ops.py_func(sub, [t], [t.dtype]))

    mul_sub = subscribe.subscribe(
        mul, lambda t: script_ops.py_func(sub, [t], [t.dtype]))

    # Expect the identity tensors injected by subscribe to have been created
    # on the same device as their original tensors.
    self.assertNotEqual(add_sub.device, mul_sub.device)
    self.assertEqual(add.device, add_sub.device)
    self.assertEqual(mul.device, mul_sub.device)

  def benchmarkMatrixDeterminantOp(self):
    for shape in self.shapes:
      with ops.Graph().as_default(), session.Session(
          config=benchmark.benchmark_config()) as sess, ops.device("/cpu:0"):
        matrix = self._GenerateMatrix(shape)
        d = linalg_ops.matrix_determinant(matrix)
        variables.global_variables_initializer().run()
        self.run_op_benchmark(
            sess,
            control_flow_ops.group(
                d,),
            min_iters=25,
            name="matrix_determinant_cpu_{shape}".format(shape=shape))

      if test.is_gpu_available(True):
        with ops.Graph().as_default(), session.Session(
            config=benchmark.benchmark_config()) as sess, ops.device("/gpu:0"):
          matrix = self._GenerateMatrix(shape)
          d = linalg_ops.matrix_determinant(matrix)
          variables.global_variables_initializer().run()
          self.run_op_benchmark(
              sess,
              control_flow_ops.group(
                  d,),
              min_iters=25,
              name="matrix_determinant_gpu_{shape}".format(shape=shape))

  def testServerDefChanged(self):
    """Update server def, and run ops on new cluster."""
    context.set_server_def(
        server_def=get_server_def(
            ALT_JOB_NAME,
            local_server_port=0,
            remote_server_addresses=[
                self._cached_server1_target, self._cached_server2_target
            ],
            task_index=0))

    with ops.device("job:%s/replica:0/task:1/device:CPU:0" % ALT_JOB_NAME):
      x1 = array_ops.ones([2, 2])
    y = math_ops.matmul(x1, x1)
    np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())

    # Set the server def back to JOB_NAME
    context.set_server_def(
        server_def=get_server_def(
            JOB_NAME,
            local_server_port=0,
            remote_server_addresses=[
                self._cached_server1_target, self._cached_server2_target
            ],
            task_index=0))

    with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
      x1 = array_ops.ones([2, 2])
    y = math_ops.matmul(x1, x1)
    np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())

  def __init__(self,
               input_dataset,
               device,
               buffer_size):
    with ops.device("/device:CPU:0"):
      super(_PrefetchToDeviceEagerIterator, self).__init__(input_dataset)
      input_iterator_handle = gen_dataset_ops.iterator_to_string_handle(
          self._resource)

    self._device = device

    @function.defun(input_signature=[tensor_spec.TensorSpec([], dtypes.string)])
    def _prefetch_fn(handle):
      """Prefetches one element from `input_iterator`."""
      remote_iterator = iterator_ops.Iterator.from_string_handle(
          handle, self.output_types, self.output_shapes, self.output_classes)
      ret = remote_iterator.get_next()
      return nest.flatten(sparse.serialize_sparse_tensors(ret))

    self._prefetch_fn = _prefetch_fn._get_concrete_function_internal()  # pylint: disable=protected-access

    with ops.device(device):
      self._buffering_resource = function_buffering_resource(
          f=self._prefetch_fn,
          output_types=self._flat_output_types,
          target_device=ged_ops.experimental_iterator_get_device(
              self._resource),
          string_arg=input_iterator_handle,
          buffer_size=buffer_size,
          shared_name=iterator_ops._generate_shared_name(
              "function_buffer_resource"))

 def _TestRandomGraphWithDevices(self,
                                 sess,
                                 seed,
                                 op_placement,
                                 devices,
                                 debug_mode=False):
   data = []
   shape = (self._dim, self._dim)
   feed_dict = {}
   # Initialize the matrices
   for i in range(len(devices)):
     with ops.device(devices[i]):
       var = array_ops.placeholder(dtypes.float32, shape=shape)
       np.random.seed(seed + i)
       feed_dict[var] = np.random.uniform(
           low=0, high=0.1, size=shape).astype(np.float32)
       data.append(var)
   # Run the 'add' operations on those matrices
   for op in op_placement:
     with ops.device(devices[op[2]]):
       data[op[2]] = math_ops.add(data[op[0]], data[op[1]])
   with ops.device('/cpu:0'):
     s = data[0]
     for i in range(1, len(data)):
       s = math_ops.add(s, data[i])
   if debug_mode:
     logging.info(ops.get_default_graph().as_graph_def())
   result = sess.run(s, feed_dict=feed_dict)
   self._LogMatrix(result, self._dim)
   return result

  def testReturnsSingleCheckpointIfOneShardedCheckpoint(self):
    checkpoint_dir = os.path.join(self.get_temp_dir(),
                                  'one_checkpoint_found_sharded')
    if not gfile.Exists(checkpoint_dir):
      gfile.MakeDirs(checkpoint_dir)

    global_step = variables.get_or_create_global_step()

    # This will result in 3 different checkpoint shard files.
    with ops.device('/cpu:0'):
      variables_lib.Variable(10, name='v0')
    with ops.device('/cpu:1'):
      variables_lib.Variable(20, name='v1')

    saver = saver_lib.Saver(sharded=True)

    with session_lib.Session(
        target='',
        config=config_pb2.ConfigProto(device_count={'CPU': 2})) as session:

      session.run(variables_lib.global_variables_initializer())
      save_path = os.path.join(checkpoint_dir, 'model.ckpt')
      saver.save(session, save_path, global_step=global_step)

    num_found = 0
    for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0):
      num_found += 1
    self.assertEqual(num_found, 1)

  def testDictionary(self):
    with ops.Graph().as_default() as G:
      with ops.device('/cpu:0'):
        x = array_ops.placeholder(dtypes.float32)
        pi = array_ops.placeholder(dtypes.int64)
        gi = array_ops.placeholder(dtypes.int64)
        v = 2. * (array_ops.zeros([128, 128]) + x)
      with ops.device(test.gpu_device_name()):
        stager = data_flow_ops.MapStagingArea(
            [dtypes.float32, dtypes.float32],
            shapes=[[], [128, 128]],
            names=['x', 'v'])
        stage = stager.put(pi, {'x': x, 'v': v})
        key, ret = stager.get(gi)
        z = ret['x']
        y = ret['v']
        y = math_ops.reduce_max(z * math_ops.matmul(y, y))

    G.finalize()

    with self.session(use_gpu=True, graph=G) as sess:
      sess.run(stage, feed_dict={x: -1, pi: 0})
      for i in range(10):
        _, yval = sess.run([stage, y], feed_dict={x: i, pi: i + 1, gi: i})
        self.assertAllClose(
            4 * (i - 1) * (i - 1) * (i - 1) * 128, yval, rtol=1e-4)

def _set_checkpoint_initializer(variable,
                                ckpt_file,
                                tensor_name,
                                slice_spec,
                                name="checkpoint_initializer"):
  """Overrides given variable's initialization op.

  Sets variable initializer to assign op that initializes variable from tensor's
  value in the checkpoint.

  Args:
    variable: `tf.Variable` object.
    ckpt_file: string, full path of the checkpoint.
    tensor_name: Name of the tensor to load from the checkpoint.
    slice_spec: Slice specification for loading partitioned tensors.
    name: Name of the operation.
  """
  base_type = variable.dtype.base_dtype
  # Do not colocate with variable since RestoreV2 op only runs on CPU and
  # colocation will force variable (and other ops that colocate with variable)
  # to be on CPU as well. It is okay to place the variable's initializer op on
  # CPU since it will only be run once at the start.
  with ops.device(variable.device), ops.device("/cpu:0"):
    restore_op = io_ops.restore_v2(
        ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
    if isinstance(variable, resource_variable_ops.ResourceVariable):
      init_op = variable.assign(restore_op, read_value=False)
    else:
      init_op = state_ops.assign(variable, restore_op)
    variable._initializer_op = init_op  # pylint:disable=protected-access
    restore_op.set_shape(variable.shape)
    variable._initial_value = restore_op  # pylint:disable=protected-access

  def benchmarkMatrixBandPartOp(self):
    for shape_ in self.shapes:
      for limits in (-1, -1), (-1, 0), (0, -1), (2, 2):
        with ops.Graph().as_default(), \
            session.Session() as sess, \
            ops.device("/cpu:0"):
          matrix = variables.Variable(array_ops.ones(shape_))
          band = array_ops.matrix_band_part(matrix, limits[0], limits[1])
          variables.global_variables_initializer().run()
          self.run_op_benchmark(
              sess,
              control_flow_ops.group(band),
              min_iters=10,
              name="matrix_band_part_cpu_{shape}_{limits}".format(
                  shape=shape_, limits=limits))

        if test_lib.is_gpu_available(True):
          with ops.Graph().as_default(), \
              session.Session() as sess, \
              ops.device("/gpu:0"):
            matrix = variables.Variable(array_ops.ones(shape_))
            band = array_ops.matrix_band_part(matrix, limits[0], limits[1])
            variables.global_variables_initializer().run()
            self.run_op_benchmark(
                sess,
                control_flow_ops.group(band),
                min_iters=10,
                name="matrix_band_part_gpu_{shape}_{limits}".format(
                    shape=shape_, limits=limits))