目前总共4篇:
1. Client根据用户编写的训练代码构建数据流图。
在构建数据流图时,会根据一定规则为每一个node分配worker(/job:worker或者/job:ps)。这个规则就是由tf.train.replica_device_setter来决定的。
代码device_setter.py文件中的def replica_device_setter()方法设置了为node分配worker的策略,简单来说就是:
“变量”按照轮询的方式放在/job:ps上,每个/job:worker产生的node放在自己的worker上。
device_setter.py文件中的def replica_device_setter()方法:
@tf_export("train.replica_device_setter") def replica_device_setter(ps_tasks=0, ps_device="/job:ps", worker_device="/job:worker", merge_devices=True, cluster=None, ps_ops=None, ps_strategy=None): """Return a `device function` to use when building a Graph for replicas. Device Functions are used in `with tf.device(device_function):` statement to automatically assign devices to `Operation` objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu). If `cluster` is `None`, and `ps_tasks` is 0, the returned function is a no-op. Otherwise, the value of `ps_tasks` is derived from `cluster`. By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom `ps_strategy` may be used to do more intelligent placement, such as `tf.contrib.training.GreedyLoadBalancingStrategy`. For example, ```python # To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker # jobs on hosts worker0, worker1 and worker2. cluster_spec = { "ps": ["ps0:2222", "ps1:2222"], "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]} with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)): # Build your graph v1 = tf.Variable(...) # assigned to /job:ps/task:0 v2 = tf.Variable(...) # assigned to /job:ps/task:1 v3 = tf.Variable(...) # assigned to /job:ps/task:0 # Run compute ``` Args: ps_tasks: Number of tasks in the `ps` job. Ignored if `cluster` is provided. ps_device: String. Device of the `ps` job. If empty no `ps` job is used. Defaults to `ps`. worker_device: String. Device of the `worker` job. If empty no `worker` job is used. merge_devices: `Boolean`. If `True`, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them. cluster: `ClusterDef` proto or `ClusterSpec`. ps_ops: List of strings representing `Operation` types that need to be placed on `ps` devices. If `None`, defaults to `STANDARD_PS_OPS`. ps_strategy: A callable invoked for every ps `Operation` (i.e. matched by `ps_ops`), that takes the `Operation` and returns the ps task index to use. If `None`, defaults to a round-robin strategy across all `ps` devices. Returns: A function to pass to `tf.device()`. Raises: TypeError if `cluster` is not a dictionary or `ClusterDef` protocol buffer, or if `ps_strategy` is provided but not a callable. """ # tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % FLAGS.task_index, cluster=cluster) if cluster is not None: if isinstance(cluster, server_lib.ClusterSpec): # True cluster_spec = cluster.as_dict() # {'ps': ['tfl2st:2221'], 'worker': ['tfl2st:2222']} else: cluster_spec = server_lib.ClusterSpec(cluster).as_dict() # Get ps_job_name from ps_device by stripping "/job:". ps_job_name = pydev.DeviceSpec.from_string(ps_device).job # 'ps' if ps_job_name not in cluster_spec or cluster_spec[ps_job_name] is None: return None ps_tasks = len(cluster_spec[ps_job_name]) # 1 if ps_tasks == 0: return None if ps_ops is None: # None # TODO(sherrym): Variables in the LOCAL_VARIABLES collection should not be # placed in the parameter server. ps_ops = list(STANDARD_PS_OPS) # STANDARD_PS_OPS = ("Variable", "VariableV2", "AutoReloadVariable",...) if not merge_devices: logging.warning( "DEPRECATION: It is recommended to set merge_devices=true in " "replica_device_setter") if ps_strategy is None: # None ps_strategy = _RoundRobinStrategy(ps_tasks) if not six.callable(ps_strategy): # false raise TypeError("ps_strategy must be callable") chooser = _ReplicaDeviceChooser( # (1, '/job:ps', '/job:worker/task:0', True, STANDARD_PS_OPS, _RoundRobinStrategy) ps_tasks, ps_device, worker_device, merge_devices, ps_ops, ps_strategy) return chooser.device_function |
调用代码是:
# cluster specification parameter_servers = ["tfl2st:2221", "tfl2st:2222"] workers = [ "tfl2st:2223", "tfl2st:2224"] cluster = tf.train.ClusterSpec({"ps":parameter_servers, "worker":workers}) # Between-graph replication with tf.device(tf.train.replica_device_setter( worker_device="/job:worker/task:%d" % FLAGS.task_index, cluster=cluster)): |
def replica_device_setter()方法设置了ps的轮询策略ps_strategy = _RoundRobinStrategy(ps_tasks);
设置设备选择的方法chooser = _ReplicaDeviceChooser();chooser.device_function;device_function的具体代码为:
class _ReplicaDeviceChooser(object): """Class to choose devices for Ops in a replicated training setup. This class is not to be used directly by users. See instead `replica_device_setter()` below. """ # (1, '/job:ps', '/job:worker/task:0', True, STANDARD_PS_OPS, _RoundRobinStrategy) def __init__(self, ps_tasks, ps_device, worker_device, merge_devices, ps_ops, ps_strategy): """Create a new `_ReplicaDeviceChooser`. Args: ps_tasks: Number of tasks in the `ps` job. ps_device: String. Name of the `ps` job. worker_device: String. Name of the `worker` job. merge_devices: Boolean. Set to True to allow merging of device specs. ps_ops: List of strings representing `Operation` types that need to be placed on `ps` devices. ps_strategy: A callable invoked for every ps `Operation` (i.e. matched by `ps_ops`), that takes the `Operation` and returns the ps task index to use. """ self._ps_tasks = ps_tasks # 1 self._ps_device = ps_device # '/job:ps' self._worker_device = worker_device # '/job:worker/task:0' self._merge_devices = merge_devices # True self._ps_ops = ps_ops # STANDARD_PS_OPS self._ps_strategy = ps_strategy # _RoundRobinStrategy def device_function(self, op): """Choose a device for `op`. Args: op: an `Operation`. Returns: The device to use for the `Operation`. """ # If we don't return early here, either merge_devices is True, or op.device # is empty (in which case merging is a no-op). So we can always merge below. if not self._merge_devices and op.device: return op.device current_device = pydev.DeviceSpec.from_string(op.device or "") # The ps_device will be used for specified ops (ps_ops) whenever it is # present and ps_tasks is non-zero. However, its task number will only be # set (using ps_strategy) if there is a job field in ps_device that won't be # changed by the job field (if present) in current_device. node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def if self._ps_tasks and self._ps_device and node_def.op in self._ps_ops: # 当前的op在_ps_ops集合中 ps_device = pydev.DeviceSpec.from_string(self._ps_device) current_job, ps_job = current_device.job, ps_device.job if ps_job and (not current_job or current_job == ps_job): ps_device.task = self._ps_strategy(op) ps_device.merge_from(current_device) return ps_device.to_string() worker_device = pydev.DeviceSpec.from_string(self._worker_device or "") worker_device.merge_from(current_device) return worker_device.to_string() |
2. 同步和异步训练是由optimizer来决定的。
2.1 同步训练
同步训练需要使用SyncReplicasOptimizer,参考https://www.tensorflow.org/api_docs/python/tf/train/SyncReplicasOptimizer 。其他optimizer都属于异步训练方式。
同步训练实现在sync_replicas_optimizer.py文件
中的def apply_gradient()方法中。假设有n个参数:
对于PS,需要创建n个参数收集器(每个参数对应一个收集器),每一个worker将自己计算得到的grad梯度推送到收集器上(推送是使用Send/Recv OP实现的)。每个参数收集器收集到所有的worker的推送值时,对所有的值求平均,然后更新参数的值。当所有的参数都更新完成之后,对global_step加1,并将global_step推送到每个worker的token_queue中,worker更新global_step,开始下一次训练。
对于Worker,从PS拉取需要的参数,计算grad梯度值,然后将grad推送到相应的参数收集器。推送之后从token_queue中拉取新的global_step(拉取不到新的global_step 就等待?),继续下一次训练。
2.2 异步训练
训练代码中使用的是GradientDescentOptimizer(继承了Optimizer),调用其minimize()方法,minimize()方法就是先调用compute_gradients()然后调用apply_gradient()方法。
异步训练的实现在optimizer.py文件中的def apply_gradient()方法中(GradientDescentOptimizer没有重写Optimizer的apply_gradient()方法)。参考https://stackoverflow.com/questions/43147435/how-does-asynchronous-training-work-in-distributed-tensorflow。
对于Worker,worker从PS拉取需要的参数,拉取过程是没有锁的,因此拉取的值可能包含了其他worker的修改,也可能没包含。计算gard梯度值,然后将grad梯度值发送给相应PS。
对于PS,ps收到grad值之后根据优化算法(如,SGD, SGD with Momentum, Adagrad, Adam, etc.)来更新参数。
注:在异步训练中,假设worker1读取参数w1,worker2再读取参数w1,然后worker1更新梯度,worker2再更新梯度,worker1更新的梯度就被worker2覆盖掉了。如果想对修改做同步,GradientDescentOptimizer的构造函数提供了use_locking参数。
代码逻辑如下:
def apply_gradients(self, grads_and_vars, global_step=None, name=None): """Apply gradients to variables. This is the second part of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs as returned by `compute_gradients()`. global_step: Optional `Variable` to increment by one after the variables have been updated. name: Optional name for the returned operation. Default to the name passed to the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If `global_step` was not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables have gradients. RuntimeError: If you should use `_distributed_apply()` instead. """ # This is a default implementation of apply_gradients() that can be shared # by most optimizers. It relies on the subclass implementing the following # methods: _create_slots(), _prepare(), _apply_dense(), and _apply_sparse(). # Handle DistributionStrategy case. if distribute_lib.get_cross_tower_context(): raise RuntimeError("Use `_distributed_apply()` instead of " "`apply_gradients()` in a cross-tower context.") # TODO(isaprykin): Get rid of `has_distribution_strategy()` check by # always calling _distributed_apply(), using the default distribution # as needed. if distribute_lib.has_distribution_strategy(): grads_and_vars = get_filtered_grad_fn(lambda _: grads_and_vars)() return distribute_lib.get_tower_context().merge_call( self._distributed_apply, grads_and_vars, global_step, name) # No DistributionStrategy case. grads_and_vars = tuple(grads_and_vars) # Make sure repeat iteration works. if not grads_and_vars: raise ValueError("No variables provided.") converted_grads_and_vars = [] for g, v in grads_and_vars: if g is not None: try: # Convert the grad to Tensor or IndexedSlices if necessary. g = ops.convert_to_tensor_or_indexed_slices(g) except TypeError: raise TypeError( "Gradient must be convertible to a Tensor" " or IndexedSlices, or None: %s" % g) if not isinstance(g, (ops.Tensor, ops.IndexedSlices)): raise TypeError( "Gradient must be a Tensor, IndexedSlices, or None: %s" % g) p = _get_processor(v) # _RefVariableProcessor converted_grads_and_vars.append((g, v, p)) # v._ref() = Tensor("weights/Variable:0", shape=(784, 10), dtype=float32_ref, device=/job:ps/task:0) # ((<tf.Tensor 'train/gradients/softmax/MatMul_grad/tuple/control_dependency_1:0' shape=(784, 10) dtype=float32>, <tf.Variable 'weights/Variable:0' shape=(784, 10) dtype=float32_ref>, <tensorflow.python.training.optimizer._RefVariableProcessor object at 0x7f6798012410>), (<tf.Tensor 'train/gradients/softmax/Add_grad/tuple/control_dependency_1:0' shape=(10,) dtype=float32>, <tf.Variable 'biases/Variable:0' shape=(10,) dtype=float32_ref>, <tensorflow.python.training.optimizer._RefVariableProcessor object at 0x7f67980124d0>)) converted_grads_and_vars = tuple(converted_grads_and_vars) var_list = [v for g, v, _ in converted_grads_and_vars if g is not None] if not var_list: raise ValueError("No gradients provided for any variable: %s." % ([str(v) for _, _, v in converted_grads_and_vars],)) with ops.init_scope(): self._create_slots(var_list) update_ops = [] with ops.name_scope(name, self._name) as name: self._prepare() for grad, var, processor in converted_grads_and_vars: if grad is None: continue # We colocate all ops created in _apply_dense or _apply_sparse # on the same device as the variable. # TODO(apassos): figure out how to get the variable name here. if context.executing_eagerly() or isinstance( var, resource_variable_ops.ResourceVariable) and not var._in_graph_mode: # pylint: disable=protected-access scope_name = "" else: scope_name = var.op.name # var.op = {name: "weights/Variable", op: "VariableV2", device: "/job:ps/task:0"} with ops.name_scope("update_" + scope_name), ops.colocate_with(var): update_ops.append(processor.update_op(self, grad)) # 111行 def update_op() 更新op,worker->ps if global_step is None: apply_updates = self._finish(update_ops, name) else: with ops.control_dependencies([self._finish(update_ops, "update")]): with ops.colocate_with(global_step): if isinstance(global_step, resource_variable_ops.ResourceVariable): # TODO(apassos): the implicit read in assign_add is slow; consider # making it less so. apply_updates = resource_variable_ops.assign_add_variable_op( global_step.handle, ops.convert_to_tensor(1, dtype=global_step.dtype), name=name) else: apply_updates = state_ops.assign_add(global_step, 1, name=name) if not context.executing_eagerly(): if isinstance(apply_updates, ops.Tensor): apply_updates = apply_updates.op train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) if apply_updates not in train_op: train_op.append(apply_updates) return apply_updates |
apply_gradients()方法中调用了update_ops.append(processor.update_op(self, grad))方法:
def update_op(self, optimizer, g): if isinstance(g, ops.Tensor): # update_op = {name: "train/GradientDescent/update_weights/Variable/ApplyGradientDescent",op: "ApplyGradientDescent", input: "weights/Variable", input: "train/GradientDescent/learning_rate", input: "train/gradients/softmax/MatMul_grad/tuple/control_dependency_1", device: "/job:ps/task:0"} update_op = optimizer._apply_dense(g, self._v) # pylint: disable=protected-access if self._v.constraint is not None: with ops.control_dependencies([update_op]): return self._v.assign(self._v.constraint(self._v)) else: return update_op # return else: assert isinstance(g, ops.IndexedSlices), ("Gradient ", g, " is neither a " "tensor nor IndexedSlices.") if self._v.constraint is not None: raise RuntimeError( "Cannot use a constraint function on a sparse variable.") # pylint: disable=protected-access return optimizer._apply_sparse_duplicate_indices(g, self._v) |
update_op(self, grad))方法调用了optimizer的_app_dense()方法,由于这里的optimizer是GradientDescentOptimizer,所以是调用GradientDescentOptimizer的_app_dense()方法:
def _apply_dense(self, grad, var): return training_ops.apply_gradient_descent( var, # <tf.Variable 'weights/Variable:0' shape=(784, 10) dtype=float32_ref> math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), # <tf.Tensor 'train/GradientDescent/learning_rate:0' shape=() dtype=float32> grad, # Tensor("train/gradients/softmax/MatMul_grad/tuple/control_dependency_1:0", shape=(784, 10), dtype=float32, device=/job:worker/task:0) use_locking=self._use_locking).op # false |
又调用了apply_gradient_descent()方法:
ef apply_gradient_descent(var, alpha, delta, use_locking=False, name=None): r"""Update '*var' by subtracting 'alpha' * 'delta' from it. Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). alpha: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. delta: A `Tensor`. Must have the same type as `var`. The change. use_locking: An optional `bool`. Defaults to `False`. If `True`, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyGradientDescent", var=var, alpha=alpha, delta=delta, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyGradientDescent", _inputs_flat, _attrs, _result, name) _result, = _result return _result else: raise RuntimeError("apply_gradient_descent op does not support eager execution. Arg 'out' is a ref.") |
又调用了_apply_op_helper()方法:
# keywords = {'var': <tf.Variable 'weights/Variable:0' shape=(784, 10) dtype=float32_ref>, 'alpha': <tf.Tensor 'train/GradientDescent/learning_rate:0' shape=() dtype=float32>, 'use_locking': False, 'delta': <tf.Tensor 'train/gradients/softmax/MatMul_grad/tuple/control_dependency_1:0' shape=(784, 10) dtype=float32>} def _apply_op_helper(self, op_type_name, name=None, **keywords): """Implementation of apply_op that returns output_structure, op.""" op_info = self._ops.get(op_type_name, None) if op_info is None: raise RuntimeError("Unrecognized Op name " + op_type_name) op_def = op_info.op_def # Fill in the list of default types for all "type" attrs. This # will be used to choose a preferred dtype to convert to in the # absence of input type information. # # TODO(b/31302892): Currently the defaults don't work in the right # way if you have two inputs, one of whose type resolution depends # on the other. Handling this will require restructuring this code # significantly. default_type_attr_map = {} for attr_def in op_def.attr: if attr_def.type != "type": continue key = attr_def.name if attr_def.HasField("default_value"): default_type_attr_map[key] = dtypes.as_dtype( attr_def.default_value.type) # Requires that op_def has passed validation (using the C++ # ValidateOpDef() from ../framework/op_def_util.h). attrs = {} inputs = [] input_types = [] with g.as_default(), ops.name_scope(name) as scope: # keywords = {'var': <tf.Variable 'weights/Variable:0' shape=(784, 10) dtype=float32_ref>, 'alpha': <tf.Tensor 'train/GradientDescent/learning_rate:0' shape=() dtype=float32>, 'use_locking': False, 'delta': <tf.Tensor 'train/gradients/softmax/MatMul_grad/tuple/control_dependency_1:0' shape=(784, 10) dtype=float32>} ... # NOTE(mrry): We add an explicit colocation constraint between # the newly created op and any of its reference-typed inputs. must_colocate_inputs = [val for arg, val in zip(op_def.input_arg, inputs) if arg.is_ref] with _MaybeColocateWith(must_colocate_inputs): # Add Op to graph op = g.create_op(op_type_name, inputs, output_types, name=scope, input_types=input_types, attrs=attr_protos, op_def=op_def) return output_structure, op_def.is_stateful, op |
该方法比较长,最后是调用了create_op():
def create_op( self, op_type, inputs, dtypes, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True): """Creates an `Operation` in this graph. This is a low-level interface for creating an `Operation`. Most programs will not call this method directly, and instead use the Python op constructors, such as `tf.constant()`, which add ops to the default graph. Args: op_type: The `Operation` type to create. This corresponds to the `OpDef.name` field for the proto that defines the operation. inputs: A list of `Tensor` objects that will be inputs to the `Operation`. dtypes: A list of `DType` objects that will be the types of the tensors that the operation produces. input_types: (Optional.) A list of `DType`s that will be the types of the tensors that the operation consumes. By default, uses the base `DType` of each input in `inputs`. Operations that expect reference-typed inputs must specify `input_types` explicitly. name: (Optional.) A string name for the operation. If not specified, a name is generated based on `op_type`. attrs: (Optional.) A dictionary where the key is the attribute name (a string) and the value is the respective `attr` attribute of the `NodeDef` proto that will represent the operation (an `AttrValue` proto). op_def: (Optional.) The `OpDef` proto that describes the `op_type` that the operation will have. compute_shapes: (Optional.) Deprecated. Has no effect (shapes are always computed). compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation. Raises: TypeError: if any of the inputs is not a `Tensor`. ValueError: if colocation conflicts with existing device assignment. Returns: An `Operation` object. """ del compute_shapes self._check_not_finalized() for idx, a in enumerate(inputs): if not isinstance(a, Tensor): raise TypeError("Input #%d is not a tensor: %s" % (idx, a)) if name is None: name = op_type # If a names ends with a '/' it is a "name scope" and we use it as-is, # after removing the trailing '/'. if name and name[-1] == "/": name = _name_from_scope_name(name) else: name = self.unique_name(name) node_def = _NodeDef(op_type, name, device=None, attrs=attrs) input_ops = set([t.op for t in inputs]) control_inputs = self._control_dependencies_for_inputs(input_ops) # _create_op_helper mutates the new Operation. `_mutation_lock` ensures a # Session.run call cannot occur between creating and mutating the op. with self._mutation_lock(): ret = Operation( node_def, self, inputs=inputs, output_types=dtypes, control_inputs=control_inputs, input_types=input_types, original_op=self._default_original_op, op_def=op_def) self._create_op_helper(ret, compute_device=compute_device) return ret |
又调用了_create_op_helper():
def _create_op_helper(self, op, compute_device=True): """Common logic for creating an op in this graph.""" # Apply any additional attributes requested. Do not overwrite any existing # attributes. for key, value in self._attr_scope_map.items(): try: op.get_attr(key) except ValueError: if callable(value): value = value(op.node_def) if not isinstance(value, (type(None), attr_value_pb2.AttrValue)): raise TypeError( "Callable for scope map key '%s' must return either None or " "an AttrValue protocol buffer; but it returned: %s" % (key, value)) if value: op._set_attr(key, value) # pylint: disable=protected-access # Apply a kernel label if one has been specified for this op type. try: kernel_label = self._op_to_kernel_label_map[op.type] op._set_attr("_kernel", # pylint: disable=protected-access attr_value_pb2.AttrValue(s=compat.as_bytes(kernel_label))) except KeyError: pass # Apply the overriding op type for gradients if one has been specified for # this op type. try: mapped_op_type = self._gradient_override_map[op.type] op._set_attr("_gradient_op_type", # pylint: disable=protected-access attr_value_pb2.AttrValue(s=compat.as_bytes(mapped_op_type))) except KeyError: pass self._record_op_seen_by_control_dependencies(op) if compute_device: self._apply_device_functions(op) if self._colocation_stack: all_colocation_groups = [] for colocation_op in self._colocation_stack: all_colocation_groups.extend(colocation_op.colocation_groups()) if colocation_op.device: # Make this device match the device of the colocated op, to provide # consistency between the device and the colocation property. if (op.device and pydev.canonical_name(op.device) != pydev.canonical_name(colocation_op.device)): logging.warning("Tried to colocate %s with an op %s that had " "a different device: %s vs %s. Postponing " "error-checking until all devices are assigned.", op.name, colocation_op.name, op.device, colocation_op.device) else: op._set_device(colocation_op.device) # pylint: disable=protected-access all_colocation_groups = sorted(set(all_colocation_groups)) # pylint: disable=protected-access op._set_attr("_class", attr_value_pb2.AttrValue( list=attr_value_pb2.AttrValue.ListValue(s=all_colocation_groups))) # pylint: enable=protected-access # Sets "container" attribute if # (1) self._container is not None # (2) "is_stateful" is set in OpDef # (3) "container" attribute is in OpDef # (4) "container" attribute is None if self._container and op.op_def.is_stateful: try: container_attr = op.get_attr("container") except ValueError: # "container" attribute is not in OpDef pass else: if not container_attr: op._set_attr("container", attr_value_pb2.AttrValue( # pylint: disable=protected-access s=compat.as_bytes(self._container))) |
其中有段逻辑
if compute_device: self._apply_device_functions(op) |
compute_device = True,会接着调用_apply_device_functions(op):
def _apply_device_functions(self, op): """Applies the current device function stack to the given operation.""" # Apply any device functions in reverse order, so that the most recently # pushed function has the first chance to apply a device to the op. # We apply here because the result can depend on the Operation's # signature, which is computed in the Operation constructor. for device_function in reversed(self._device_function_stack): if device_function is None: break op._set_device(device_function(op)) # pylint: disable=protected-access |
这个方法里为op分配的设备。分配策略为replica_device_setter()方法设置的策略。
参考:
[1] http://jcf94.com/2018/01/13/2018-01-13-tfunpacking/ (session.run())
[2] http://jcf94.com/2018/01/23/2018-01-23-tfunpacking2/ (tf数据流模型和自动求导)
[3] http://jcf94.com/2018/02/28/2018-02-28-tfunpacking3/ (graph和node)
[4] http://jcf94.com/2018/03/07/2018-03-07-tfunpacking4/ (device)
[5] http://jcf94.com/2018/03/09/2018-03-09-tfunpacking5/ (distributed)
[6] https://www.tensorflow.org/deploy/distributed (distributed tensorflow)
[7] https://stackoverflow.com/questions/43147435/how-does-asynchronous-training-work-in-distributed-tensorflow (asynchronous training in distributed tensorflow)