def __init__(self, params):
super(SeqLayer, self).__init__(params)
p = self.params
assert p.name
num_cells = len(p.cell_tpl)
self._before_layers = []
self._cells = []
before_tpl_device = ''
cell_devices = [''] * num_cells
if py_utils.use_tpu():
cluster = self.cluster
before_tpl_device = cluster.WorkerDeviceInModelSplit(0)
cell_devices = [
cluster.WorkerDeviceInModelSplit(i) for i in range(num_cells)
]
for l in p.before_tpl:
with tf.device(before_tpl_device):
assert l.name
self.CreateChild(l.name, l)
self._before_layers.append((l.name, self.children[l.name]))
for i, l in enumerate(p.cell_tpl):
with tf.device(cell_devices[i]):
assert l.name
self.CreateChild(l.name, l)
self._cells.append((l.name, self.children[l.name]))
def FProp(self, theta, *args):
"""FProp through multiple devices in the split.
Args:
theta: A NestedMap object containing weights' values of this layer and its
children layers.
*args: A tuple of Tensors (one or more). Every tensor's first dimension is
the same (the batch dimension).
Returns:
The sub layer's output.
"""
p = self.params
with tf.name_scope(p.name):
assert all(isinstance(x, tf.Tensor) for x in args)
cluster = self.cluster
num = cluster.num_devices_per_split
if num == 1:
return self.sub.FProp(theta.sub, *args)
inps = py_utils.SplitRecursively(list(args), num, axis=0)
outs = []
for i, xs in enumerate(inps):
device = cluster.WorkerDeviceInModelSplit(i)
tf.logging.info('%d on device %s', i, device)
with tf.device(device):
ys = self.sub.FProp(theta.sub, *xs)
if isinstance(ys, tuple):
outs += [list(ys)]
else:
outs += [ys] # ys is a single tensor
ret = py_utils.ConcatRecursively(outs, axis=0)
if isinstance(ret, list):
return tuple(ret)
else:
return ret # ys is a single tensor
def FProp(self, theta, *args):
"""Round-robin every children cells in cell_tpl among worker devices.
Args:
theta: A NestedMap object containing weights' values of this layer and its
children layers.
*args: Input args
Returns:
A list contains one tensor of [batch_size, feature_height, feature_width,
channel].
"""
num_layers = len(self.params.cell_tpl)
cluster = self.cluster
for (name, l) in self._before_layers:
l_theta = theta[name]
args = _ToTuple(args)
args = l.FProp(l_theta, *args)
for i in range(num_layers):
with tf.device(cluster.WorkerDeviceInModelSplit(i)):
cell_name, cell = self._cells[i]
args = _ToTuple(args)
args = cell.FProp(theta[cell_name], *args)
return args
def CreateTpuEmbeddingEnqueueOps(self):
"""Creates the TpuEmbedding enqueue ops on the host.
Note that this must be called after the instantiation of the
monolithic TPUEmbeddingLayer.
"""
p = self.params
cluster = self.cluster
num_tpu_hosts = cluster.num_tpu_hosts
num_infeed_hosts = num_tpu_hosts if p.use_per_host_infeed else 1
tpu_embedding_collection = tf.get_collection(py_utils.TPU_EMBEDDING)
tpu_embedding = (tpu_embedding_collection[0]
if tpu_embedding_collection else None)
enqueue_ops = []
if num_tpu_hosts > 1 and tpu_embedding is not None:
if not p.use_per_host_infeed:
tf.logging.fatal(
'TPU Embedding must be used with per_host_infeed with multiple '
'TPU host topologies.')
tpu_emb_input_keys = (list(tpu_embedding.feature_to_config_dict.keys())
if tpu_embedding is not None else [])
tf.logging.info('tpu_emb_input_keys: %r', tpu_emb_input_keys)
if not tpu_embedding:
return
for task_id in range(num_infeed_hosts):
host_device = '/task:{}/device:CPU:0'.format(task_id)
with tf.device(host_device):
if isinstance(self._batch, py_utils.NestedMap):
# Hack: bucket_keys and xxx.bucket_keys are not needed on TPU.
# Note that when MultiTaskData is used, bucket_keys will be at the
# second level of the dictionary.
self._batch = self._batch.FilterKeyVal(
lambda k, _: not k.endswith('bucket_keys'))
tf.logging.info('host_device: %s, batch: %r', host_device,
self._batch)
enqueue_dict_per_core = [
{} for _ in range(tpu_embedding.num_cores_per_host)
]
num_cores_per_host = tpu_embedding.num_cores_per_host
for key in tpu_emb_input_keys:
feat = self._batch[key]
tpu_emb_feat_splitted = tf.split(feat, num_cores_per_host)
for core, split in enumerate(tpu_emb_feat_splitted):
# Dense to sparse. Note the assumption of a padding id.
sample_indices = tf.where(tf.not_equal(split, -1))
embedding_indices = tf.gather_nd(split, sample_indices)
enqueue_data = tpu_embedding_lib.EnqueueData(
embedding_indices, sample_indices)
enqueue_dict_per_core[core][key] = enqueue_data
enqueue_ops += tpu_embedding.generate_enqueue_ops(
enqueue_dict_per_core)
self._tpu_infeed_op.append(tf.group(*enqueue_ops))
def TpuDequeueBatch(self):
"""Create TPU dequeue ops.
This should only be called within a TPU context.
Returns:
- A NestedMap of the input batch.
"""
assert self._tpu_queues, 'CreateTpuEnqueueOps must be called first.'
with tf.device(tf.tpu.core(0)):
# Note that the dequeue_tuple op on the TPU core
# only cares about the shape/types being dequeued
# which is why this is hard-coded to the first Queue.
tensors = self._tpu_queues[0].generate_dequeue_op()
return self._batch.Pack(tensors)
def CollectVarHistogram(vs_gs):
"""Adds histogram summaries for variables and gradients."""
for name, (var, grad) in vs_gs.FlattenItems():
name = py_utils.SanitizeScopeKey(name)
with tf.device(var.device), tf.name_scope(name + '/summary'):
if isinstance(grad, tf.IndexedSlices):
var = tf.gather(var, grad.indices)
grad = grad.values
if var.dtype.is_complex:
var = tf.abs(var)
grad = tf.abs(grad)
histogram('var_hist/' + name, var)
histogram('grad_hist/' + name, grad)
def Recv(self):
"""Receives a tensor from the channel."""
if self._send_tpu_core == -1:
received = tf.raw_ops.Recv(tensor_type=self._dtype,
tensor_name=self._name,
send_device=self._send_device,
send_device_incarnation=0,
recv_device=self._recv_device)
received.set_shape(self._shape)
return received
else:
with tf.device(self._recv_device):
return xla.recv(self._dtype,
tensor_name=self._name,
shape=self._shape,
name="Recv_" + self._name)
def Send(self, tensor):
"""Sends a tensor through the channel."""
assert tensor.dtype == self._dtype
assert not self._send_called, ("Send called multiple times for %s" %
self._name)
self._send_called = True
if self._send_tpu_core == -1:
return tf.raw_ops.Send(tensor=tensor,
tensor_name=self._name,
send_device=self._send_device,
send_device_incarnation=0,
recv_device=self._recv_device)
else:
with tf.device(self._send_device):
return xla.send(tensor,
tensor_name=self._name,
name="Send_" + self._name)
def __init__(self, train_cfg, ps_params_dict, model_task_name, logdir,
tf_master, **kwargs):
"""Construct an ExecutorTpu BaseRunner.
Args:
train_cfg: SingleTaskModelParams or MultiTaskModelParams
ps_params_dict: A dict of top-level task name -> ProgramSchedule params,
if train_cfg is a SingleTaskModelParams, we expect only one entry.
model_task_name: An override for multi-task models, currently unused.
logdir: String path to the log directory to output to.
tf_master: String path to the master job, e.g. 'local'.
**kwargs: keyword args to pass through to BaseRunner.
"""
super(ExecutorTpu, self).__init__(train_cfg, model_task_name, logdir,
tf_master, **kwargs)
self._cluster_def = self._cluster.worker_cluster_def
# There is a single Executor task
assert self._cluster.num_replicas == 1
data_parallelism = self._cluster.num_splits_per_client
assert data_parallelism
num_devices_per_split = self._cluster.num_devices_per_split
tf.logging.info('data_parallelism: %d, num_devices_per_split: %d',
data_parallelism, num_devices_per_split)
self.task_scheduler = None
self._checkpoint_dir = os.path.join(logdir, 'train')
self._variable_renaming_rules = []
self._ml_perf = None
# If this is a multi-task model, grab the params for the TaskScheduler.
if issubclass(train_cfg.cls, base_model.SingleTaskModel):
tf.logging.info('single_task_model')
assert len(ps_params_dict) == 1
self._model_task_name = list(ps_params_dict.keys())[0]
self._single_task_mode = True
elif issubclass(train_cfg.cls, base_model.MultiTaskModel):
tf.logging.info('multi_task_model')
if issubclass(train_cfg.cls, multitask_model.RegExSharedVariableModel):
self._variable_renaming_rules = train_cfg.variable_renaming_rules
if train_cfg.task_schedule is None:
task_schedule_params = task_scheduler.ConstantScheduler.Params()
task_schedule_params.task_probs = sorted(
list(train_cfg.task_probs.IterParams()))
else:
task_schedule_params = train_cfg.task_schedule
self.task_scheduler = task_schedule_params.Instantiate()
self._single_task_mode = False
else:
tf.logging.fatal(
'Model %s is not a sub-class of SingleTaskModel or MultiTaskModel',
train_cfg.cls)
tf.logging.info('train_cfg.cls: %s', train_cfg.cls)
self._WriteToLog(train_cfg.ToText(), self._checkpoint_dir, 'params.txt')
self._program_schedule_dict = {}
self._programs = []
for task_string, program_schedule_params in ps_params_dict.items():
program_schedule_params.logdir = logdir
program_schedule_params.num_splits_per_client = data_parallelism
program_schedule_params.task_name = task_string
ps = program_schedule_params.Instantiate()
self._program_schedule_dict[task_string] = ps
tf.logging.info('program_schedule_params: %s',
program_schedule_params.ToText())
self._programs += ps.Programs()
if program_schedule_params.ml_perf.benchmark_name is not None:
self._ml_perf = program_schedule_params.ml_perf
tf.logging.info('num_programs: %d', len(self._programs))
if self._ml_perf is not None:
self._ml_perf_log = True
mlp_log.mlperf_print(key='benchmark', value=self._ml_perf.benchmark_name)
else:
self._ml_perf_log = False
# BaseRunner legacy
self.enqueue_ops = None
@py_utils.RetryOnTransientTfError()
def _WaitTillInit():
"""Wait until the model is ready."""
try:
with self._graph.as_default(), self._GetSession(
cluster_def=self._cluster_def,
disable_meta_optimizer=FLAGS.disable_meta_optimizer_in_executor
) as sess:
topology = sess.run(
tf.tpu.initialize_system(embedding_config=None, job=None))
device_assignment = device_assignment_lib.device_assignment(
topology,
computation_shape=py_utils.ComputationShape(
num_devices_per_split),
num_replicas=data_parallelism)
py_utils.SetTpuDeviceAssignment(device_assignment)
tf.logging.info('device_assignment.core_assignment: %s',
str(device_assignment.core_assignment))
tf.logging.info('device_assignment.topology.device_coordinates: %s',
str(device_assignment.topology.device_coordinates))
except py_utils.transient_tf_errors as e:
tf.logging.info('TPU initialization failed: %s', e)
raise
if self._ml_perf_log:
mlp_log.mlperf_print(key='cache_clear', value=True)
mlp_log.mlperf_print(key='init_start', value=None)
_WaitTillInit()
with self._graph.as_default(), tf.container(self._container_id):
tf.logging.info('self._cluster.job_spec.name: %s',
self._cluster.job_spec.name)
with self._cluster, tf.device(
self._cluster.job_spec.name if not FLAGS.cluster_placer_in_executor
else self._cluster.GetPlacer()):
with py_utils.VariableRenameScope(self._variable_renaming_rules):
_ = py_utils.GetOrCreateGlobalStepVar()
for program in self._programs:
program.BuildTpuSubgraph()
for program in self._programs:
program.SetStatusMessageFn(self._SetStatusMessage)
program.CreateCheckpointer()
self._initialize_tables = tf.tables_initializer()
self._initialize_local_vars = tf.local_variables_initializer()
self.save_only_checkpointer = checkpointer.Checkpointer(
self._checkpoint_dir,
model=None,
train_params=train_cfg.train,
save_only=True)
def _DecoderDevice(self):
"""Returns the device to run the decoder computation."""
return tf.device('')
def SplitInputBatch(self, num_splits):
with tf.device(self.cluster.input_device):
return super(_UseInputDevice,
self).SplitInputBatch(num_splits)
def __init__(self, params):
with tf.device(self.cluster.input_device):
super(_UseInputDevice, self).__init__(params)
def FProp(self, theta, *args):
"""Run multiple cells in different devices in a pipelining manner.
Args:
theta: A NestedMap object containing weights' values of this layer and its
children layers.
*args: Non-keyworded variable length argument list of input tensors.
Returns:
A list of output tensors
"""
# TODO(huangyp): handle optional None inputs.
p = self.params
if self.do_eval:
outputs = copy.copy(args)
for (name, l) in self._before_layers + self._cells:
outputs = _ToTuple(outputs)
outputs = l.FProp(theta[name], *outputs)
return outputs
num_cells = len(p.cell_tpl)
cluster = self.cluster
# Compute shapes of input and output tensors.
input_shapes = self._get_input_shapes(*args)
state_dtype = self._get_state_dtype(*args)
state_shapes = self._CalculateOutputShapes(input_shapes)
tf.logging.info('state_shapes={}'.format(state_shapes))
def GetCellFn(i):
"""Get the ith feature extraction layer."""
def CellFn(theta, state0, inputs):
"""A cell fn is exectued inside of StackedRecurrent."""
del state0
def _FPropInputSetShape(name, t_shape):
if t_shape is None:
return None
inputs[name].set_shape(t_shape.ToTensorShape().as_list())
return inputs[name]
if p.nested_map_fprop:
# pylint: disable=protected-access
fprop_inputs = state_shapes[i]._RecursiveMap(_FPropInputSetShape)
# pylint: enable=protected-access
else:
fprop_inputs = []
for input_idx, input_shape in enumerate(state_shapes[i]):
name = 's{}'.format(input_idx)
fprop_inputs.append(_FPropInputSetShape(name, input_shape))
with py_utils.RemoveAssertContext(remove=True):
with CellFnFPropOpReplacementWrapper():
tf.logging.info('cell {} input {}'.format(i, fprop_inputs))
mb_tensor = inputs[_MICRO_BATCH_STATE_NAME]
SetOverWriteGlobalStep(mb_tensor)
_, cell = self._cells[i]
fprop_inputs = _ToTuple(fprop_inputs)
outputs = cell.FProp(theta, *fprop_inputs)
if p.nested_map_fprop:
assert py_utils.IsCompatible(outputs, state_shapes[i + 1])
state1 = outputs.Filter(lambda x: x is not None)
else:
state1 = py_utils.NestedMap()
outputs = _ToTuple(outputs)
assert len(outputs) == len(state_shapes[i + 1])
for output_idx in range(len(outputs)):
if outputs[output_idx] is not None:
name = 's{}'.format(output_idx)
state1[name] = outputs[output_idx]
state1[_MICRO_BATCH_STATE_NAME] = mb_tensor
return state1, py_utils.NestedMap()
return CellFn
cell_fns = []
accumulator_layers = []
thetas = []
init_states = []
devices = []
for cell_idx in range(num_cells):
cell_name, cell = self._cells[cell_idx]
accumulator_layers.append(cell)
cell_fns.append(GetCellFn(cell_idx))
thetas.append(theta[cell_name])
def _TfZeros(t_shape):
if t_shape is None:
return None
return tf.zeros(t_shape.ToTensorShape().as_list(), dtype=state_dtype)
if p.nested_map_fprop:
init_state = py_utils.Transform(_TfZeros, state_shapes[cell_idx + 1])
init_state = init_state.Filter(lambda x: x is not None)
else:
init_state = py_utils.NestedMap()
for output_idx, state in enumerate(state_shapes[cell_idx + 1]):
state = _TfZeros(state)
if state is not None:
name = 's{}'.format(output_idx)
init_state[name] = state
init_state[_MICRO_BATCH_STATE_NAME] = tf.cast(0, dtype=state_dtype)
init_states.append(init_state)
devices.append(cluster.WorkerDeviceInModelSplit(cell_idx))
cell_grads = [None] * num_cells
cell_outs = [lambda x: x] * num_cells
cell_out_grads = [lambda x: x] * num_cells
with tf.device(devices[0]):
previous = _ToTuple(args)
for (name, l) in self._before_layers:
previous = l.FProp(theta[name], *previous)
previous = _ToTuple(previous)
def _StackAndSplit(x):
# Split tensors into microbatches.
if x is None:
return None
return tf.stack(tf.split(x, p.num_micro_batches, axis=p.batch_dim))
if p.nested_map_fprop:
inputs = py_utils.Transform(_StackAndSplit, previous[0])
inputs = inputs.Filter(lambda x: x is not None)
else:
inputs = py_utils.NestedMap()
for output_idx, output_tensor in enumerate(previous):
output_tensor = _StackAndSplit(output_tensor)
if output_tensor is not None:
name = 's{}'.format(output_idx)
inputs[name] = output_tensor
gs_tensor = py_utils.GetGlobalStep()
inputs[_MICRO_BATCH_STATE_NAME] = tf.stack([
tf.cast(gs_tensor * p.num_micro_batches + t, dtype=state_dtype)
for t in range(p.num_micro_batches)
])
tf.logging.info('pipeline input = {}'.format(inputs))
output_state, _ = recurrent.StackedRecurrent(
devices=devices,
cell_fns=cell_fns,
cell_grads=cell_grads,
cell_outs=cell_outs,
cell_out_grads=cell_out_grads,
thetas=thetas,
init_states=init_states,
inputs=inputs,
accumulator_layers=accumulator_layers,
unused_acc_state=True)
with tf.device(devices[-1]):
def _ReshapeRetVal(name, t_shape):
"""Restore shape for tensors in microbatches."""
if t_shape is None:
return None
output_tensor = output_state[name]
if p.batch_dim != 0:
perm = list(range(1, p.batch_dim + 1)) + [0]
perm += list(range(p.batch_dim + 1, t_shape.rank + 1))
output_tensor = tf.transpose(output_tensor, perm=perm)
output_shape = t_shape.ToTensorShape().as_list()
output_shape[p.batch_dim] *= p.num_micro_batches
output_tensor = tf.reshape(output_tensor, output_shape)
return output_tensor
# Construct the final return values from output_state.
if p.nested_map_fprop:
# pylint: disable=protected-access
output_tensors = state_shapes[-1]._RecursiveMap(_ReshapeRetVal)
# pylint: enable=protected-access
else:
output_tensors = []
for output_idx, state_shape in enumerate(state_shapes[-1]):
output_name = 's{}'.format(output_idx)
output_tensor = _ReshapeRetVal(output_name, state_shape)
output_tensors.append(output_tensor)
if len(output_tensors) == 1:
output_tensors = output_tensors[0]
else:
output_tensors = tuple(output_tensors)
tf.logging.info('pipeline output = {}'.format(output_tensors))
return output_tensors
def Export(cls,
model_cfg,
model_task_name=None,
device_options=InferenceDeviceOptions(
device='',
retain_device_placement=False,
var_options=None,
gen_init_op=True,
dtype_override=None),
freeze_checkpoint=None,
freeze_defaults=False,
export_path=None,
subgraph_filter=None,
random_seed=None,
disable_packed_input=True):
"""Exports a InferenceGraph proto with piecewise subgraphs.
Sets FLAGS.enable_asserts to False unless user explicitly sets it to True.
Args:
model_cfg: a Params instance as returned by
model_registry.GetParams(modelname, 'Test') or model_params.Model().
model_task_name: The task to generate an inference graph for. Should be
None for single-task models.
device_options: Device options for the accelerator used for serving.
freeze_checkpoint: The checkpoint to load. Loads and freezes the model if
given.
freeze_defaults: Default initializes the graph and freeze. Useful for
early testing of downstream tools without having a checkpoint.
export_path: If not None, write the inference graph in ASCII to this path.
subgraph_filter: A list of subgraph names. If not None or empty, export
only this list of inference subgraphs.
random_seed: Fixes the random seed in the exported inference graph.
disable_packed_input: Disable packed input for inference writing purposes.
Returns:
InferenceGraph proto.
Raises:
ValueError: if the model does not support the listed subgraphs.
"""
assert issubclass(model_cfg.cls, base_model.BaseModel)
# Disable assertions unless user explicitly enables it.
if FLAGS['enable_asserts'].using_default_value:
FLAGS.enable_asserts = False
# TODO(laurenzo): Work out how much we need to specify here in terms of
# cluster configuration.
cls._SetClusterParams(model_cfg.cluster, device_options)
# Configure the model.
model_cfg.random_seed = random_seed
model_cfg.is_inference = True
if disable_packed_input:
def _DisablePackedInput(task):
if (_ParamExists(task, 'encoder')
and _ParamExists(task.encoder, 'packed_input')):
task.encoder.packed_input = False
if (_ParamExists(task, 'decoder')
and _ParamExists(task.decoder, 'packed_input')):
task.decoder.packed_input = False
if issubclass(model_cfg.cls, base_model.MultiTaskModel):
for _, task_param in model_cfg.task_params.IterParams():
_DisablePackedInput(task_param)
else:
_DisablePackedInput(model_cfg.task)
tf.logging.info('Model %s params:', model_cfg.name)
for line in model_cfg.ToText().split('\n'):
tf.logging.info('%s', line)
# Instantiate the graph.
graph = tf.Graph()
with graph.as_default():
tf.random.set_seed(random_seed)
cluster = model_cfg.cluster.Instantiate()
device = cluster.GetPlacer()
tpu_const_scope = _DummyScope()
if (IsTpu(device_options)
and device_options.var_options == 'AS_CONSTANTS'):
# Do not specify devices for variables if we are marking them as
# constants.
device = ''
tpu_const_scope = ConstGuaranteeScope()
with cluster, tf.device(device), tpu_const_scope:
bfloat16_override = ShouldForceBfloat16ForWeightsAndActivations(
device_options)
if bfloat16_override:
py_utils.UpdateDtype(model_cfg, tf.bfloat16)
py_utils.UpdateFpropDtype(model_cfg, tf.bfloat16)
# Hard-code TPU-related flags prior to instantiating model.
old_enable_asserts = FLAGS.enable_asserts
old_xla_device = FLAGS.xla_device
if IsTpu(device_options):
FLAGS.enable_asserts = False
FLAGS.xla_device = 'tpu'
# Ensure the global_step variable is created.
global_step_var = py_utils.GetOrCreateGlobalStepVar()
global_step = tf.identity(global_step_var,
name='global_step_tensor')
with py_utils.GlobalStepContext(global_step):
try:
mdl = model_cfg.Instantiate()
variables_to_restore = (_MakeVariableDictionary(
tf.global_variables()) if not mdl.ema else
mdl.ema.variables_to_restore(
mdl.variables_for_ema))
if bfloat16_override:
saver_var_spec = (
bfloat16_variables.
get_saver_spec_for_variables_with_bf16_overrides(
variables_to_restore))
else:
saver_var_spec = variables_to_restore
saver = tf.train.Saver(saver_var_spec)
tf.variables_initializer(tf.global_variables(),
name='init_all_variables')
if IsTpu(
device_options) and device_options.gen_init_op:
tf.group(tf.tpu.initialize_system(),
name='tpu_init_op')
model_task = mdl.GetTask(model_task_name)
inference_graph_proto = inference_graph_pb2.InferenceGraph(
)
subgraphs_proto = model_task.Inference()
if isinstance(subgraphs_proto, dict):
subgraphs_proto = ConvertSubgraphDictToProto(
subgraphs_proto)
for name, subgraph in subgraphs_proto.subgraphs.items(
):
if not subgraph_filter or name in subgraph_filter:
inference_graph_proto.subgraphs[name].CopyFrom(
subgraph)
# Add a table init op and global variable init op to the graph.
# Tables can be declared anywhere in the graph, so this op has to be
# added last.
tf.tables_initializer(name='init_all_tables')
finally:
# Reset TPU-related flags after model instantiation.
FLAGS.enable_asserts = old_enable_asserts
FLAGS.xla_device = old_xla_device
tf.logging.info('Graph contains ops: %r',
[op.name for op in graph.get_operations()])
inference_graph_proto.saver_def.CopyFrom(saver.as_saver_def())
# Freezing.
if freeze_defaults or freeze_checkpoint:
output_op_names = GetOutputOpNames(graph,
inference_graph_proto,
preserve_colocation_nodes=False)
if cls._DeviceSupportsFreezing(device_options):
raise ValueError(
'freeze_checkpoint cannot be used with device ' +
device_options.device)
if freeze_checkpoint:
tf.logging.info('Freezing graph from checkpoint: %s',
freeze_checkpoint)
graph_def = _FreezeGraphFromCheckpoint(graph, saver,
freeze_checkpoint,
output_op_names)
elif freeze_defaults:
tf.logging.info('Default initializing graph and freezing.')
graph_def = _FreezeDefaults(graph, output_op_names)
else:
output_op_names = GetOutputOpNames(graph, inference_graph_proto)
# Prune the graph to just the parts we need.
# To support restoring, we have to not prune out the restore node.
output_op_names.append('init_all_tables')
output_op_names.append('init_all_variables')
output_op_names.append('save/control_dependency')
output_op_names.append('save/restore_all')
if IsTpu(device_options) and device_options.gen_init_op:
output_op_names.append('tpu_init_op')
graph_def = graph.as_graph_def()
tf.logging.info('Pruning graph to output ops: %r', output_op_names)
graph_def = tf.graph_util.extract_sub_graph(
graph_def, output_op_names)
if not device_options.retain_device_placement:
# Clear the device so that the runtime can choose.
tf.logging.info('Clearing device placement for: %s',
device_options.device)
for node in graph_def.node:
node.ClearField('device')
for function in graph_def.library.function:
for node_def in function.node_def:
node_def.ClearField('device')
inference_graph_proto.graph_def.CopyFrom(graph_def)
if export_path:
with tf.io.gfile.GFile(export_path, 'w') as f:
f.write(text_format.MessageToString(inference_graph_proto))
return inference_graph_proto
