# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ====================================== """Library of TPU helper functions.""" import collections import enum from typing import Any, Callable, Iterable, List, Optional, Text, Tuple, Union from absl import logging import numpy as np from tensorflow.compiler.tf2xla.python import xla as tf2xla from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.protobuf.tpu import dynamic_padding_pb2 as dynamic_padding from tensorflow.core.protobuf.tpu import tpu_embedding_configuration_pb2 as embedding_pb2 from tensorflow.python import tf2 from tensorflow.python.compiler.xla import xla from tensorflow.python.framework import auto_control_deps from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import config from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import func_graph from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops_stack from tensorflow.python.ops import cond from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.tpu import device_assignment as device_assignment_lib from tensorflow.python.tpu import tensor_tracer from tensorflow.python.tpu import tpu_feed from tensorflow.python.tpu import tpu_function from tensorflow.python.tpu import tpu_name_util from tensorflow.python.tpu import tpu_replication from tensorflow.python.tpu.ops import tpu_ops from tensorflow.python.types import core as core_types from tensorflow.python.util import compat from tensorflow.python.util import nest from tensorflow.python.util import object_identity from tensorflow.python.util import traceback_utils from tensorflow.python.util import variable_utils from tensorflow.python.util.tf_export import tf_export # Ops which can be safely pruned from XLA compile if they have no consumers. # These ops should also have no inputs. _UNCONNECTED_OPS_TO_PRUNE = set(["Placeholder", "VarHandleOp"]) _POST_DEVICE_REWRITE_ATTR = "_post_device_rewrite" _TPU_COMPILATION_STATUS_ATTR = "_tpu_compilation_status" _PIVOT_FOR_CLUSTER = "_pivot_for_cluster" core = tpu_name_util.core def _tpu_system_device_name(job: Optional[Text]) -> Text: """Returns the device name for the TPU_SYSTEM device of `job`.""" if job is None: return "/device:TPU_SYSTEM:0" else: return "/job:%s/device:TPU_SYSTEM:0" % job @tf_export(v1=["tpu.initialize_system"]) def initialize_system( embedding_config: Optional[embedding_pb2.TPUEmbeddingConfiguration] = None, job: Optional[Text] = None, compilation_failure_closes_chips: bool = True, tpu_cancellation_closes_chips: Optional[bool] = None, ) -> core_types.Tensor: """Initializes a distributed TPU system for use with TensorFlow. Args: embedding_config: If not None, a `TPUEmbeddingConfiguration` proto describing the desired configuration of the hardware embedding lookup tables. If embedding_config is None, no hardware embeddings can be used. job: The job (the XXX in TensorFlow device specification /job:XXX) that contains the TPU devices that will be initialized. If job=None it is assumed there is only one job in the TensorFlow flock, and an error will be returned if this assumption does not hold. compilation_failure_closes_chips: Set the configuration whether we want to close TPU chips when there is a compilation failure. tpu_cancellation_closes_chips: Set the configuration whether we want to close TPU chips when a TPU execution is cancelled. If the value is None, the behavior will be determined by the command line flag `tpu_cancellation_closes_chips` for the TPU worker. WARNING: this argument only applies to TFRT TPU runtime. Returns: A serialized `TopologyProto` that describes the TPU system. Note: the topology must be evaluated using `Session.run` before it can be used. """ config_string = ("" if embedding_config is None else embedding_config.SerializeToString()) # The enum is defined in core/tpu/kernels/tpu_execute_op_options.h. tpu_cancellation_closes_chips_enum = 0 if tpu_cancellation_closes_chips is not None: if tpu_cancellation_closes_chips: tpu_cancellation_closes_chips_enum = 1 else: tpu_cancellation_closes_chips_enum = 2 with ops.device(_tpu_system_device_name(job)): topology = tpu_ops.configure_distributed_tpu( compilation_failure_closes_chips=compilation_failure_closes_chips, tpu_cancellation_closes_chips=tpu_cancellation_closes_chips_enum, ) if embedding_config is None: return topology # This set of control dependencies is needed as this function is expected to # return an op which will return the topology when executed, but we need to # call the embedding initialization op between initializing the TPU and # returning the topology. with ops.control_dependencies([topology]): embedding_init = tpu_ops.configure_tpu_embedding(config=config_string) with ops.control_dependencies([embedding_init]): return array_ops.identity(topology, name="tpu_init_identity") def initialize_system_for_tpu_embedding( embedding_config: embedding_pb2.TPUEmbeddingConfiguration, job: Optional[Text] = None, ) -> ops.Operation: """Initializes a distributed TPU Embedding system for use with TensorFlow. The following two are equivalent: 1. initialize_system() with embedding_config. 2. initialize_system() without embedding_config, then initialize_system_for_tpu_embedding(). initialize_system() should not be called with embedding_config if initialize_system_for_tpu_embedding() is meant to be called later. Args: embedding_config: a `TPUEmbeddingConfiguration` proto describing the desired configuration of the hardware embedding lookup tables. job: The job (the XXX in TensorFlow device specification /job:XXX) that contains the TPU devices that will be initialized. If job=None it is assumed there is only one job in the TensorFlow flock, and an error will be returned if this assumption does not hold. Returns: A no-op. """ config_string = embedding_config.SerializeToString() with ops.device(_tpu_system_device_name(job)): return tpu_ops.configure_tpu_embedding(config=config_string) @tf_export(v1=["tpu.shutdown_system"]) def shutdown_system(job: Optional[Text] = None) -> ops.Operation: """Shuts down a running a distributed TPU system. Args: job: The job (the XXX in TensorFlow device specification /job:XXX) that contains the TPU devices that will be shutdown. If job=None it is assumed there is only one job in the TensorFlow flock, and an error will be returned if this assumption does not hold. """ with ops.device(_tpu_system_device_name(job)): shutdown_distributed_tpu = tpu_ops.shutdown_distributed_tpu() return shutdown_distributed_tpu @auto_control_deps.register_acd_resource_resolver def tpu_replicated_input_resolver( op: ops.Operation, resource_reads: object_identity.ObjectIdentitySet, resource_writes: object_identity.ObjectIdentitySet) -> bool: """Replaces TPUReplicatedInput outputs with its inputs in resource_inputs.""" # Ignore TPUReplicatedInput for ACD purposes since we will be directly adding # control deps on the replicated inputs. if op.type == "TPUReplicatedInput": if resource_reads or resource_writes: resource_reads.clear() resource_writes.clear() return True else: return False # Replace tensors in `resource_inputs` which are outputs of TPUReplicatedInput # with the actual replicated inputs. This allows ACD to correct add control # deps when there are multiple calls to `run` in a # `tf.function`. def replace_with_unreplicated_resources(resource_inputs): """Replaces handles in `resource_inputs` with their unreplicated inputs.""" to_remove = [] to_add = [] for resource in resource_inputs: if resource.op.type == "TPUReplicatedInput": to_remove.append(resource) to_add.extend(resource.op.inputs) for t in to_remove: resource_inputs.discard(t) resource_inputs.update(to_add) return to_add or to_remove return bool(replace_with_unreplicated_resources(resource_reads) or replace_with_unreplicated_resources(resource_writes)) @tf_export(v1=["tpu.PaddingSpec"]) class PaddingSpec(enum.IntEnum): """Represents the type of padding policies for tpu.replicate.""" # By default the policy is set to AUTO, the dynamic input shape dimension will # be pad to maximum of all the replicas. AUTO = 0 # Bucketize the dynamic input shape dimension into a power of 2. POWER_OF_TWO = 1 @tf_export("tpu.XLAOptions") class XLAOptions( collections.namedtuple( "XLAOptions", [ "use_spmd_for_xla_partitioning", "enable_xla_dynamic_padder", "use_shardy_partitioner", ], ) ): """XLA compilation options. Attributes: use_spmd_for_xla_partitioning: Boolean. Whether to use XLA's SPMD partitioner instead of MPMD partitioner when compiler partitioning is requested. enable_xla_dynamic_padder: Boolean. Whether to enable XLA dynamic padder infrastructure to handle dynamic shapes inputs inside XLA. True by default. Disabling this may cause correctness issues with dynamic shapes inputs, as XLA will just assume the inputs are with padded shapes. However users can optionally set it to False to improve device time if masking is already handled in the user side. use_shardy_partitioner: Boolean. Whether to use Shardy partitioner. """ def __new__( cls, use_spmd_for_xla_partitioning=True, enable_xla_dynamic_padder=True, use_shardy_partitioner=False, ): return super(XLAOptions, cls).__new__( cls, use_spmd_for_xla_partitioning, enable_xla_dynamic_padder, use_shardy_partitioner, ) @tf_export(v1=["tpu.replicate"]) @traceback_utils.filter_traceback def replicate( computation: Callable[..., Any], inputs: Optional[List[List[core_types.Tensor]]] = None, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None, maximum_shapes: Optional[Any] = None, padding_spec: Optional[PaddingSpec] = None, xla_options: Optional[XLAOptions] = None) -> List[Any]: """Builds a graph operator that runs a replicated TPU computation. Example for the basic usage that `inputs` has static shape: ```python def computation(x): x = x + 1 return tf.math.reduce_mean(x) x = tf.convert_to_tensor([1., 2., 3.]) y = tf.convert_to_tensor([4., 5., 6.]) tf.compat.v1.tpu.replicate(computation, inputs=[[x], [y]]) ``` If the `inputs` has dynamic shapes and you would like to automatically bucketize the inputs to avoid XLA recompilation. See the advanced example below: ```python def computation(x): x = x + 1 return tf.math.reduce_mean(x) # Assume input tensors in two replicas `x` and `y` both have dynamic shape # ([None, 2]). tf.compat.v1.tpu.replicate( computation, inputs=[x, y], maximum_shapes=[tf.TensorShape([None, None])], padding_spec=tf.compat.v1.tpu.PaddingSpec.POWER_OF_TWO) ``` Args: computation: A Python function that builds the computation to replicate. inputs: A list of lists of input tensors or `None` (equivalent to `[[]]`), indexed by `[replica_num][input_num]`. All replicas must have the same number of inputs. Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with `tf.convert_to_tensor`. infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple of arguments as inputs to computation. device_assignment: If not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if `None`. The `DeviceAssignment` may be omitted if each replica of the computation uses only one core, and there is either only one replica, or the number of replicas is equal to the number of cores in the TPU system. name: (Deprecated) Does nothing. maximum_shapes: A nested structure of tf.TensorShape representing the shape to which the respective component of each input element in each replica should be padded. Any unknown dimensions (e.g. tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like object) will be padded to the maximum size of that dimension over all replicas. The structure of `maximum_shapes` needs to be the same as `inputs[0]`. padding_spec: An enum specified by `tpu.PaddingSpec`. This describes the padding policy when the `inputs` to `tpu.replicate` is dynamic. One usage is to enable automatic bucketizing on the inputs by setting the value to `tpu.PaddingSpec.POWER_OF_TWO`, which can help to reduce the recompilation in the XLA side. xla_options: An instance of `tpu.XLAOptions` which indicates the options passed to XLA compiler. Use `None` for default options. Returns: A list of outputs, indexed by `[replica_num]` each output can be a nested structure same as what computation() returns with a few exceptions. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. TODO(b/121383831): Investigate into removing these special cases. Raises: ValueError: If all replicas do not have equal numbers of input tensors. ValueError: If the number of inputs per replica does not match the number of formal parameters to `computation`. ValueError: If the static `inputs` dimensions don't match with the values given in `maximum_shapes`. ValueError: If the structure of inputs per replica does not match the structure of `maximum_shapes`. """ return split_compile_and_replicate( computation, inputs, infeed_queue, device_assignment, name, maximum_shapes=maximum_shapes, padding_spec=padding_spec, xla_options=xla_options)[1] def _ceil_to_pow_of_n(x, n): """Ceil input `x` to power of `n`.""" x = math_ops.cast(x, dtypes.float32) lognx = math_ops.log(x) / math_ops.log(n * 1.0) lognx = math_ops.ceil(lognx) result = math_ops.pow(n * 1.0, lognx) result = math_ops.cast(result, dtypes.int32) return result def _pad_all_input( inputs: Iterable[core_types.Tensor], padded_shapes: List[Optional[tensor_shape.TensorShape]], padding_spec: PaddingSpec ) -> Tuple[List[List[Any]], List[dynamic_padding.PaddingMap]]: """Pad all input tensors given padded_shapes. The real shape tensors will be concatenated with the padded original inputs. Args: inputs: The original inputs. padded_shapes: A list of padded shapes for each input. If an entry is None, no padding is performed. padding_spec: An enum specified by `tpu.PaddingSpec`. This describes the padding policy when the `inputs` to `tf.tpu.replicate` is dynamic. One usage is to enable automatic bucketizing on the inputs by setting the value to `tpu.PaddingSpec.POWER_OF_TWO`, which can help to reduce the recompilation in the XLA side. Returns: The padded inputs and a PaddingMap list which maps the padded input dimension to the real shape argument index. """ # maximum_static_shapes[idx][i] indicates the maximum static size of ith # dimension of the idx input among all the replicas. maximum_static_shapes = [] # need_padding[idx][i] indicates whether the ith dimension of the idx input # needs padding. need_padding = [] input_shape_tensors = [] for core_idx, inputs_per_core in enumerate(inputs): for idx, input_tensor in enumerate(inputs_per_core): input_shape = input_tensor.get_shape().as_list() if core_idx == 0: input_shape_tensors.append([]) maximum_static_shapes.append(input_shape) need_padding.append(np.full_like(input_shape, False, dtype=bool)) else: for i, s in enumerate(input_shape): if s is None or s != maximum_static_shapes[idx][i]: need_padding[idx][i] = True maximum_static_shapes[idx] = max(input_shape, maximum_static_shapes[idx]) # Append _POST_DEVICE_REWRITE_ATTR attributes to the real shape ops. real_input_shape = array_ops.shape(input_tensor) real_input_shape.op._set_attr( # pylint: disable=protected-access _POST_DEVICE_REWRITE_ATTR, attr_value_pb2.AttrValue(b=True)) input_shape_tensors[idx].append(real_input_shape) maximum_shapes = [] for shapes_per_input in input_shape_tensors: maximum_shapes.append( math_ops.reduce_max(array_ops_stack.stack(shapes_per_input), axis=0)) padded_inputs = [] real_shapes = [] padding_maps = [] for core_idx, inputs_per_core in enumerate(inputs): padded_inputs.append([]) real_shapes.append([]) real_shape_idx = len(inputs_per_core) - 1 for idx, input_tensor in enumerate(inputs_per_core): input_shape_tensor = input_shape_tensors[idx][core_idx] input_shape = input_tensor.get_shape().as_list() padded_shape = padded_shapes[idx] # If we have no padded_shape, then skip padding. if any(need_padding[idx]) and padded_shape is not None: for i, s in enumerate(input_shape): if need_padding[idx][i]: if core_idx == 0: real_shape_idx += 1 padding_map = dynamic_padding.PaddingMap() padding_map.arg_index = idx padding_map.shape_index = i padding_map.padding_arg_index = real_shape_idx padding_maps.append(padding_map) real_shapes[core_idx].append( math_ops.cast(input_shape_tensor[i], dtypes.int32)) paddings = [] for i, s in enumerate(padded_shape.dims): if need_padding[idx][i]: # The minimum padded dimension size is 2 as XLA doesn't support size # 1 dynamic size. minimum_dynamic_dim_size = 2 if s.value is not None: # Pad to the given maximum value. max_dim_size = max(s.value, minimum_dynamic_dim_size) else: # If maximum value is not given, then pad to the maximum dimension # among all the cores. max_dim_size = math_ops.maximum(maximum_shapes[idx][i], minimum_dynamic_dim_size) if padding_spec == PaddingSpec.POWER_OF_TWO: max_dim_size = _ceil_to_pow_of_n(max_dim_size, 2) # Pad to the given maximum value. padding = [0, max_dim_size - input_shape_tensor[i]] else: padding = [0, 0] paddings.append(padding) if input_tensor.get_shape().is_fully_defined(): # TODO(rxsang): This is a hack to make sure padded_input has dynamic # shapes, so any tf.size/tf.shape op performed on it won't be constant # folded. Do we have better ways to do it? padded_input = cond.cond( array_ops.constant(True), lambda: array_ops.pad(input_tensor, paddings), # pylint: disable=cell-var-from-loop lambda: input_tensor) else: padded_input = array_ops.pad(input_tensor, paddings) # Append _POST_DEVICE_REWRITE_ATTR attributes to all padded inputs. padded_input.op._set_attr( # pylint: disable=protected-access _POST_DEVICE_REWRITE_ATTR, attr_value_pb2.AttrValue(b=True)) padded_inputs[core_idx].append(padded_input) else: padded_inputs[core_idx].append(input_tensor) num_replicas = len(padded_inputs) for i in range(num_replicas): padded_inputs[i].extend(real_shapes[i]) return padded_inputs, padding_maps def _flatten_and_filter_composite(maybe_composite, non_composite_output, composite_output=None): """For an input, replaced the input by a tuple if the input is composite. If `maybe_composite` is not composite, return the parameter `non_composite_output` otherwise return a tuple which consists of the value of the parameter `composite_output` the same number of times as there are components of the composite tensor. This is useful for computing a mask when flattening nested data with `expand_composites=True`. For example ```python nest.flatten(data, expand_composites=True) ``` and ```python nest.flatten(nest.map( data, lambda x: _flatten_and_filter_composite(x, False, True))) ``` will have the same length and second will be True if the tensor in the first is derived from a expanding a composite tensor. Args: maybe_composite: A value to test for being a composite tensor. non_composite_output: The value to return when `maybe_composite` is not a composite. composite_output: the value to fill the output tuple with if `maybe_composite` is a composite. Returns: `non_composite_output` or a tuple with multiple copies of `composite_output`. """ if isinstance(maybe_composite, composite_tensor.CompositeTensor): num_components = len(nest.flatten(maybe_composite, expand_composites=True)) return (composite_output,) * num_components return non_composite_output def split_compile_and_replicate( computation: Callable[..., Any], inputs: Optional[List[List[core_types.Tensor]]] = None, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None, use_tpu: bool = True, maximum_shapes: Optional[Any] = None, padding_spec: Optional[PaddingSpec] = None, xla_options: Optional[XLAOptions] = None, ) -> List[List[core_types.Tensor]]: """Builds graph operators that runs compilation and replicated computation. This is a lower level interface than replicate that returns a separate compile and execute output tensor. In the generated graph the compile op feeds into the execute op and no additional compilation is incurred when running the compile op before the execute op. The compile op returns additional information about the compilation but does not return the compiled program. Args: computation: A Python function that builds the computation to replicate. inputs: A list of lists of input tensors or `None` (equivalent to `[[]]`), indexed by `[replica_num][input_num]`. All replicas must have the same number of inputs. Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with `tf.convert_to_tensor`. infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple of arguments as inputs to computation. device_assignment: If not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if `None`. The `DeviceAssignment` may be omitted if each replica of the computation uses only one core, and there is either only one replica, or the number of replicas is equal to the number of cores in the TPU system. name: (Deprecated) Does nothing. use_tpu: When false, the input `computation` is executed on the XLA CPU/GPU backends. Currently, only supports a default placement (computation is placed on GPU if one is available, and on CPU if not). maximum_shapes: A nested structure of tf.TensorShape representing the shape to which the respective component of each input element in each replica should be padded. Any unknown dimensions (e.g. tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like object) will be padded to the maximum size of that dimension over all replicas. The structure of `maximum_shapes` needs to be the same as `inputs[0]`. padding_spec: An enum specified by `tf.tpu.PaddingSpec`. This describes the padding policy when the `inputs` to `tf.tpu.replicate` is dynamic. One usage is to enable automatic bucketizing on the inputs by setting the value to `tpu.PaddingSpec.POWER_OF_TWO`, which can help to reduce the recompilation in the XLA side. xla_options: An instance of `tpu.XLAOptions` which indicates the options passed to XLA compiler. Use `None` for default options. Returns: A list of lists with the first list corresponding to the compile op and the second a list of output tensors, indexed by `[replica_num][output_num]`. Raises: ValueError: If all replicas do not have equal numbers of input tensors. ValueError: If the number of inputs per replica does not match the number of formal parameters to `computation`. ValueError: If the static `inputs` dimensions don't match with the values given in `maximum_shapes`. ValueError: If the structure of inputs per replica does not match the structure of `maximum_shapes`. """ del name inputs = [[]] if inputs is None else inputs xla_options = xla_options or XLAOptions() metadata_kwargs = {} if device_assignment is not None: # Turn the Numpy array into a flattened list so we can pass it as an # operator attribute. metadata_kwargs = { "topology": device_assignment.topology.serialized(), "device_assignment": device_assignment.core_assignment.flatten().tolist() } metadata_kwargs["num_cores_per_replica"] = ( device_assignment.num_cores_per_replica) # This entry is used for enabling automatic outside compilation. metadata_kwargs["allow_soft_placement"] = config.get_soft_device_placement() if config.get_soft_device_placement(): logging.info("Automatic outside compilation is enabled. " "Ops without XLA kernels will be automatically " "placed on CPU.") if not isinstance(inputs, list): raise TypeError("tpu.replicate() inputs must be a list of lists/tuples, " f"received {type(inputs)}") if any(not isinstance(inp, (list, tuple)) for inp in inputs): raise TypeError( "tpu.replicate() inputs must be a list of lists/tuples, " f"received types: {[type(inp) for inp in inputs]}") num_replicas = len(inputs) # No replicas? Nothing to do. if num_replicas == 0: return [] # Checks all replicas have the same structure. for i in range(1, num_replicas): nest.assert_same_structure(inputs[0], inputs[i]) # Explicitly read variables. inputs = variable_utils.convert_variables_to_tensors(inputs) # Flatten inputs. This structure may contain None values, which will be # handled later. flat_inputs_with_nones = [ nest.flatten(per_replica_input, expand_composites=True) for per_replica_input in inputs ] # Mask parallel to one replica's inputs with True for tensors coming from # composites. is_composite = nest.flatten(nest.map_structure( lambda x: _flatten_and_filter_composite(x, False, True), inputs[0])) # Converts inputs to Tensors, replacing Nones with a placeholder 0 since # tpu_ops.tpu_replicated_input() can't handle non-Tensor values. flat_inputs = [] for inp in flat_inputs_with_nones: flat_inputs.append([ constant_op.constant(0) if x is None else ops.convert_to_tensor(x) for x in inp ]) # Verifies that all replicas have matching numbers and types of inputs flat_input_types = [x.dtype for x in flat_inputs[0]] input_arity = len(inputs[0]) flat_input_arity = len(flat_input_types) for i in range(num_replicas): if len(inputs[i]) != input_arity: raise ValueError("Replicas must have the same number of inputs. " "Replica 0 had {} inputs, replica {} had {} " "inputs.".format(input_arity, i, len(inputs[i]))) types = [x.dtype for x in flat_inputs[i]] if types != flat_input_types: raise ValueError("Replicas must have matching input types. Replica 0 had " "input types {}, replica {} had input types {}".format( flat_input_types, i, types)) arg_error = xla.check_function_argument_count( computation, input_arity, infeed_queue) if arg_error is not None: if infeed_queue is None: raise TypeError( "Supplied computation cannot be called with the specified inputs. " f"You specified {input_arity} inputs: {[i.name for i in inputs[0]]}, " f"but the computation needs {arg_error}") else: raise TypeError( "Supplied computation cannot be called with the specified inputs. " f"You specified {input_arity} inputs: {[i.name for i in inputs[0]]} ", f"and {infeed_queue.number_of_tuple_elements} additional inputs " f"from infeed, but the computation needs {arg_error}") dynamic_shape_inputs = False if maximum_shapes: if infeed_queue: raise ValueError( "Dynamic input shapes are not supported with infeed queues") # Make sure maximum_shapes has the same structure as inputs. nest.assert_same_structure(inputs[0], maximum_shapes, check_types=False) # Flatten padded shapes: # For composite tensor components, we don't want to pad them. For each # entry of maximum_shapes that corresponds to a composite tensor, replace it # by a tuple of Nones of the same length as the number of components of the # composite tensor. When we flatten a second time, this makes # flat_maximum_shapes have the same length as flat_inputs[i]. We can then # avoid padding these tensors. The assumption is that they will be used by # outside compilation or that the components are statically shaped and will # be used by tpu compatible ops. flat_maximum_shapes = nest.flatten( [_flatten_and_filter_composite(x, y) for x, y in zip(nest.flatten(inputs[0]), nest.flatten(maximum_shapes))]) flat_maximum_shapes = [ tensor_shape.TensorShape(s) if s is not None else None for s in flat_maximum_shapes ] nest.assert_same_structure(flat_inputs[0], flat_maximum_shapes, check_types=False) unpadded_inputs = flat_inputs flat_inputs, padding_maps = _pad_all_input(unpadded_inputs, flat_maximum_shapes, padding_spec) if padding_maps: dynamic_shape_inputs = True logging.info("TPU has inputs with dynamic shapes: %s", inputs[0]) metadata_kwargs["step_marker_location"] = getattr( computation, "step_marker_location", "STEP_MARK_AT_ENTRY") metadata_kwargs["use_spmd_for_xla_partitioning"] = \ xla_options.use_spmd_for_xla_partitioning metadata_kwargs["use_shardy_partitioner"] = xla_options.use_shardy_partitioner graph = ops.get_default_graph() # Fan-in: Builds a TPUReplicatedInput node for each input. flat_replicated_inputs = [] for i in range(0, len(flat_inputs[0])): replicas = [flat_inputs[replica][i] for replica in range(num_replicas)] flat_replicated_inputs.append( tpu_ops.tpu_replicated_input( replicas, name="input{}".format(i))) if isinstance(graph, func_graph.FuncGraph): # When we are in Tensorflow 2.0 function, 'graph' will be a FuncGraph # object. If both outside graph and this function have a TPU cluster, # they will have the same cluster name and it will cause problems (because # we lower functional ops in Tensorflow 2.0). Append function name to # 'cluster_name' to avoid cluster name collision. cluster_name = graph.unique_name("cluster_" + graph.name) else: cluster_name = graph.unique_name("cluster") pivot = control_flow_ops.no_op(name=cluster_name + "/pivot") pivot._set_attr(_PIVOT_FOR_CLUSTER, # pylint: disable=protected-access attr_value_pb2.AttrValue(s=compat.as_bytes(cluster_name))) context = tpu_replication.TPUReplicateContext( name=cluster_name, num_replicas=num_replicas, pivot=pivot) try: context.Enter() metadata = tpu_ops.tpu_replicate_metadata( num_replicas=num_replicas, use_tpu=use_tpu, **metadata_kwargs) with tpu_function.tpu_shard_context( num_replicas), ops.control_dependencies([metadata]): if dynamic_shape_inputs and xla_options.enable_xla_dynamic_padder: for padding_map in padding_maps: input_shape = flat_replicated_inputs[padding_map.arg_index].shape flat_replicated_inputs[ padding_map.arg_index] = tf2xla.set_dynamic_dimension_size( flat_replicated_inputs[padding_map.arg_index], padding_map.shape_index, flat_replicated_inputs[padding_map.padding_arg_index]) flat_replicated_inputs[padding_map.arg_index].set_shape(input_shape) # Add identity ops so even unused inputs are "consumed" by the # computation. This is to avoid orphaned TPUReplicatedInput nodes. # TODO(phawkins): consider instead pruning unused TPUReplicatedInput # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. flat_replicated_inputs = [ array_ops.identity(x, name="replicated_input_{}".format(i)) for i, x in enumerate(flat_replicated_inputs) ] for i, composite in zip(flat_replicated_inputs, is_composite): # pylint: disable=protected-access # Add an attribute to the identity node so that they could be removed in # encapsulate TPU computation pass if unused. However we don't remove # inputs when dynamic padding is enabled. # TODO(rxsang): Use other ways except argument index in padding_map so # outside compilation can work with dynamic padding correctly. if not dynamic_shape_inputs or composite: i.op._set_attr("_tpu_input_identity", attr_value_pb2.AttrValue(b=True)) # pylint: enable=protected-access # Clobber replicated placeholders with Nones. computation_inputs = [ None if inp is None else replicated for replicated, inp in zip( flat_replicated_inputs, flat_inputs_with_nones[0]) ] # Unflatten the computation inputs to match original input structure. computation_inputs = nest.pack_sequence_as( structure=inputs[0], flat_sequence=computation_inputs[:flat_input_arity], expand_composites=True) # If there is an infeed queue, adds the dequeued values to the # computation's inputs. if infeed_queue is not None: infeed_queue.set_number_of_shards(num_replicas) for t in infeed_queue.generate_dequeue_op(): computation_inputs.append(t) # Only resource variables work inside a TPU computation, so turn on # resource variables for the computation. # TODO(phawkins): consider removing this code. It will # be less confusing to clients if they knowingly choose to use resource # variables. # Partitioned variables is not supported (b/112311320). vscope = variable_scope.get_variable_scope() saved_use_resource = vscope.use_resource saved_custom_getter = vscope.custom_getter def custom_getter(getter, name, *args, **kwargs): """Variables on TPU have a few restrictions.""" partitioner = kwargs.get("partitioner", None) if partitioner is not None: kwargs["partitioner"] = None logging.warning( "Partitioned variables are not supported on TPU. Got " "`partitioner` that is %s for variable %s. " "Setting `partitioner` to `None`.", partitioner, name) if saved_custom_getter is None: return getter(name, *args, **kwargs) else: return saved_custom_getter(getter, name, *args, **kwargs) vscope.set_use_resource(True) vscope.set_custom_getter(custom_getter) outputs = computation(*computation_inputs) vscope.set_use_resource(saved_use_resource) vscope.set_custom_getter(saved_custom_getter) outputs = variable_utils.convert_variables_to_tensors(outputs) need_spmd_partitioning = ( xla_options.use_spmd_for_xla_partitioning and device_assignment is not None and device_assignment.num_cores_per_replica > 1) outputs_is_flat = xla.is_flat(outputs) if outputs_is_flat: output_tensors, control_deps, pack_template = _postprocess_flat_outputs( outputs, need_spmd_partitioning) else: output_tensors, control_deps, pack_template = ( _postprocess_non_flat_outputs(outputs, need_spmd_partitioning)) if tensor_tracer.TensorTracer.is_enabled(): if tf2.enabled(): logging.warn("TF API ver >= 2.0 detected. " "Tensor Tracer v1 is not enabled.") else: tt = tensor_tracer.TensorTracer() output_tensors = tt.trace_tpu(ops.get_default_graph(), output_tensors, control_deps, num_replicas) context.ExitResult(output_tensors) finally: context.report_unsupported_operations() context.Exit() host_compute_core = context.HostComputeCore() if host_compute_core: attr_value = attr_value_pb2.AttrValue() attr_value.list.s.extend(compat.as_bytes(x) for x in host_compute_core) metadata._set_attr("host_compute_core", attr_value) # pylint: disable=protected-access with ops.control_dependencies([metadata]): if use_tpu: compile_status = tpu_ops.tpu_compilation_result() op = compile_status.op attr_value = attr_value_pb2.AttrValue(s=compat.as_bytes(cluster_name)) op._set_attr(_TPU_COMPILATION_STATUS_ATTR, attr_value) # pylint: disable=protected-access else: compile_status = control_flow_ops.no_op(name="compilation_status") if not output_tensors: # Returns a list of NoOps dependent on the replication Op, indexed by # [replica_num]. return [ compile_status, [ control_flow_ops.group(control_deps, name="shard_%d" % i) for i in range(num_replicas) ] ] # Fan-out: Builds a TPUReplicatedOutput node for each output. replicated_outputs = [[] for i in range(num_replicas)] for i, t in enumerate(output_tensors): # None values returned by the computation can't be sent to # tpu_ops.tpu_replicated_output(), we handle them specially here. We can # avoid the placeholder 0 routine required on the inputs since outputs are # replicated per-tensor, not per-replica, so we can skip replication. if t is None: for replica in range(num_replicas): replicated_outputs[replica].append(None) continue # Fan-out: Builds a TPUReplicatedOutput node for each output. ys = tpu_ops.tpu_replicated_output( t, num_replicas, name="output{}".format(i)) # Wraps the outputs in identity operators so the names of any possible # `fetch` nodes are preserved by the replication rewrite. with ops.control_dependencies(control_deps): for replica in range(num_replicas): replicated_outputs[replica].append( array_ops.identity( ys[replica], name="output_%d_shard_%d" % (i, replica))) replicated_outputs = [ nest.pack_sequence_as(pack_template, replica_outs, expand_composites=True) for replica_outs in replicated_outputs ] return [compile_status, replicated_outputs] def _postprocess_flat_outputs( outputs: Any, need_spmd_partitioning: bool ) -> Tuple[List[Optional[core_types.Tensor]], List[ops.Operation], List[Any]]: """Validates non-flat outputs, add backs device assignments and other attrs. Args: outputs: Output from `computation` inside `tpu.rewrite`. need_spmd_partitioning: Whether XLA SPMD partitioning is needed. Returns: - Tensors extracted from outputs. - Operations extracted from outputs. - A pack template for use with nest.pack_sequence_as to pack the tensors. """ # Following code segment is to preserve legacy behavior. Previously we only # supported flat outputs and thus for consistency it was nice to convert even # single element into a tuple. But now that we support arbitrary output # structure, this is no longer necessary. # TODO(b/121383831): Migrate all legacy use cases and delete this special # case. # If the computation returns `None`, make it an empty tuple. if outputs is None: outputs = tuple() # For legacy / backwards compatibility reasons we return a list for "flat" # output values (even if the user's flat return value was a different type or # even just a scalar value) so use nest.flatten to compute a flat list pack # template. pack_template = nest.flatten(outputs, expand_composites=False) # Even though outputs is already "flat", we flatten any composites so their # component tensors can be tagged and replicated. The pack_template will be # used by the caller to repack the composite tensors. outputs = nest.flatten(outputs, expand_composites=True) # Append `no_op` here so that fetching any return value of this function # will trigger TPUExecute node. outputs += (control_flow_ops.no_op(),) maybe_convert = lambda x: None if x is None else ops.convert_to_tensor(x) try: if need_spmd_partitioning: outputs = [ o if isinstance(o, ops.Operation) else maybe_convert(o) for o in outputs ] else: with ops.device(core(0)): outputs = [ o if isinstance(o, ops.Operation) else maybe_convert(o) for o in outputs ] except Exception as e: raise ValueError( "TPU function return values must all either be Operations or " f"convertible to Tensors. Got error: {e}") # Separates the returned Operations and Tensors. output_operations = [o for o in outputs if isinstance(o, ops.Operation)] output_tensors = [o for o in outputs if not isinstance(o, ops.Operation)] if outputs != output_tensors + output_operations: raise ValueError( "TPU functions must return zero-or more Tensor values followed by " "zero or more Operations.") # Trim operations off the end of the pack template. output_operations has 1 # extra element due to the no-op that is added. if len(output_operations) > 1: pack_template = pack_template[:1 - len(output_operations)] # Wraps outputs in Identity ops. Otherwise a replicated input copied # straight to an output would bypass the replicate(). This would be bad # because the TPUReplicatedInput/TPUReplicatedOutput operator would not # be rewritten away, leading to a runtime error. # TODO(phawkins): extend the rewrite to elide these nodes instead. new_output_tensors = [] for t in output_tensors: if t is None: new_output_tensors.append(None) elif need_spmd_partitioning: o = array_ops.identity(t) # pylint: disable=protected-access o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True)) # pylint: enable=protected-access new_output_tensors.append(o) else: with ops.device(t.device if t.device else core(0)): o = array_ops.identity(t) # pylint: disable=protected-access o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True)) # pylint: enable=protected-access new_output_tensors.append(o) return new_output_tensors, output_operations, pack_template def _postprocess_non_flat_outputs( outputs: Any, need_spmd_partitioning: bool ) -> Tuple[List[Optional[core_types.Tensor]], List[ops.Operation], List[Any]]: """Validates non-flat outputs, add backs device assignments and other attrs. Args: outputs: Output from `computation` inside `tpu.rewrite`. need_spmd_partitioning: Whether XLA SPMD partitioning is needed. Returns: - Tensors extracted from outputs. - An empty Operations list because Operations are not allowed in non-flat outputs. - A pack template for use with nest.pack_sequence_as to pack the tensors. """ # Flatten output items. flat_outputs = nest.flatten(outputs, expand_composites=True) # Convert all non-None non-Operation outputs to Tensors. for i, o in enumerate(flat_outputs): if o is None: flat_outputs[i] = None continue if isinstance(o, ops.Operation): raise ValueError( "tpu.rewrite does not support Operation as return value in non-flat " "output structure. You can set returned Operations as control " "dependencies of returned Tensors so Operations are triggered when " f'Tensors are evaluated. Operation found: "{o.name}"') try: o = ops.convert_to_tensor(o) except Exception as e: raise ValueError( "TPU function return values must all either be Operations or " f'convertible to Tensors. Got error: "{e}"') # Wraps outputs in Identity ops. Otherwise a replicated input copied # straight to an output would bypass the replicate(). This would be bad # because the TPUReplicatedInput/TPUReplicatedOutput operator would not # be rewritten away, leading to a runtime error. # TODO(phawkins): extend the rewrite to elide these nodes instead. if need_spmd_partitioning: o = array_ops.identity(o) # pylint: disable=protected-access o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True)) # pylint: enable=protected-access flat_outputs[i] = array_ops.identity(o) else: with ops.device(o.device if o.device else core(0)): o = array_ops.identity(o) # pylint: disable=protected-access o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True)) # pylint: enable=protected-access flat_outputs[i] = array_ops.identity(o) # All flat_outputs are Tensors, and no Operations. return flat_outputs, [], outputs def split_compile_and_shard( computation: Callable[..., Any], inputs: Optional[List[List[Optional[core_types.Tensor]]]] = None, num_shards: int = 1, input_shard_axes: Optional[List[int]] = None, outputs_from_all_shards: Union[bool, List[bool]] = True, output_shard_axes: Optional[List[int]] = None, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None, xla_options: Optional[XLAOptions] = None, ) -> Tuple[ops.Operation, List[core_types.Tensor]]: """Shards `computation` for parallel execution. `inputs` must be a list of Tensors or None (equivalent to an empty list), each of which has a corresponding split axis (from `input_shard_axes`). Each input is split into `num_shards` pieces along the corresponding axis, and computation is applied to each shard in parallel. Tensors are broadcast to all shards if they are lexically captured by `computation`. e.g., x = tf.constant(7) def computation(): return x + 3 ... = shard(computation, ...) If `outputs_from_all_shards` is true, the outputs from all shards of `computation` are concatenated back together along their `output_shard_axes`. Otherwise, each output is taken from an arbitrary shard. Inputs and outputs of the computation must be at least rank-1 Tensors. Args: computation: A Python function that builds a computation to apply to each shard of the input. inputs: A list of input tensors or None (equivalent to an empty list). Each input tensor has a corresponding shard axes, given by `input_shard_axes`, which must have size divisible by `num_shards`. num_shards: The number of shards. input_shard_axes: A list of dimensions along which to shard `inputs`, or `None`. `None` means "shard all inputs along dimension 0". If not `None`, there must be one dimension per input. outputs_from_all_shards: Boolean or list of boolean. For each output, if `True`, outputs from all shards are concatenated along the corresponding `output_shard_axes` entry. Otherwise, each output is taken from an arbitrary shard. If the argument is a boolean, the argument's value is used for each output. output_shard_axes: A list of dimensions along which to concatenate the outputs of `computation`, or `None`. `None` means "concatenate all outputs along dimension 0". If not `None`, there must be one dimension per output. Ignored if `outputs_from_all_shards` is False. infeed_queue: If not `None`, the `InfeedQueue` to use to augment the inputs of `computation`. device_assignment: If not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if `None`. The `DeviceAssignment` may be omitted if each shard of the computation uses only one core, and there is either only one shard, or the number of shards is equal to the number of cores in the TPU system. name: (Deprecated) Does nothing. xla_options: An instance of `tpu.XLAOptions` which indicates the options passed to XLA compiler. Use `None` for default options. Returns: A tuple of (compile op, [output tensors]). Raises: ValueError: If num_shards <= 0 ValueError: If len(input_shard_axes) != len(inputs) ValueError: If len(output_shard_axes) != len(outputs from `computation`) """ # TODO(phawkins): consider adding support for broadcasting Tensors passed as # inputs. if num_shards <= 0: raise ValueError( f"num_shards must be a positive integer. Received {num_shards}") inputs = [] if inputs is None else inputs if not isinstance(inputs, list): raise TypeError("tpu.shard()'s inputs must be a list of Tensors or None. " f"Received {type(inputs)}") # Converts inputs to Tensors. inputs = [ops.convert_to_tensor(x) for x in inputs] if input_shard_axes is None: input_shard_axes = [0] * len(inputs) if len(inputs) != len(input_shard_axes): raise ValueError("Length of input_shard_axes must be equal to the number " f"of inputs. Received {len(inputs)} inputs and " f"{len(input_shard_axes)} input_shard_axes.") if inputs: # Splits the `inputs` along the corresponding `input_shard_axes`, giving # lists with layout [input][shard] split_inputs = [ array_ops.split(x, num_shards, axis=axis) for (axis, x) in zip(input_shard_axes, inputs)] # Transposes the input lists to have layout [shard][input] transposed_inputs = [list(i) for i in zip(*split_inputs)] else: transposed_inputs = [[]] * num_shards compile_op, outputs = split_compile_and_replicate( computation, transposed_inputs, infeed_queue=infeed_queue, device_assignment=device_assignment, name=name, xla_options=xla_options) # There must be at least one shard since num_shards > 0. # TODO(b/36647078) remove disable when pylint bug is fixed. # pylint: disable=indexing-exception if isinstance(outputs[0], ops.Operation): # pylint: enable=indexing-exception # There were no outputs from the computation and replicate returned a list # of NoOps with control dependencies on the computation. Return the first # one so it can be used as a control dependency or fetch node. # TODO(b/36647078) remove disable when pylint bug is fixed. # pylint: disable=indexing-exception return compile_op, [outputs[0]] # pylint: enable=indexing-exception # TODO(b/36647078) remove disable when pylint bug is fixed. # pylint: disable=indexing-exception num_outputs = len(outputs[0]) # pylint: enable=indexing-exception if output_shard_axes is None: output_shard_axes = [0] * num_outputs if num_outputs != len(output_shard_axes): raise ValueError("Length of output_shard_axes must be equal to the number " f"of outputs. Received {num_outputs} outputs " f"and {len(output_shard_axes)} output_shard_axes.") if isinstance(outputs_from_all_shards, bool): outputs_from_all_shards = [outputs_from_all_shards] * num_outputs if num_outputs != len(outputs_from_all_shards): raise ValueError( "Length of outputs_from_all_shards must be equal to the number of " f"outputs. Received {num_outputs} outputs and " f"{len(outputs_from_all_shards)} outputs_from_all_shards.") results = [] for (axis, all_shards, x) in zip(output_shard_axes, outputs_from_all_shards, zip(*outputs)): if all_shards: # Concatenate all of the outputs together (use stack for scalars). shape = x[0].shape is_scalar = shape is not None and (shape.ndims == 0) results.append((array_ops_stack.stack(list(x)) if is_scalar else array_ops.concat(list(x), axis=axis))) else: # TODO(phawkins): use a smarter policy, e.g., round-robin across shards. results.append(x[0]) return compile_op, results @tf_export(v1=["tpu.shard"]) @traceback_utils.filter_traceback def shard( computation: Callable[..., Any], inputs: Optional[List[core_types.Tensor]] = None, num_shards: int = 1, input_shard_axes: Optional[List[int]] = None, outputs_from_all_shards: Union[bool, List[bool]] = True, output_shard_axes: Optional[List[int]] = None, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None, xla_options: Optional[XLAOptions] = None) -> List[core_types.Tensor]: """Shards `computation` for parallel execution. `inputs` must be a list of Tensors or None (equivalent to an empty list), each of which has a corresponding split axis (from `input_shard_axes`). Each input is split into `num_shards` pieces along the corresponding axis, and computation is applied to each shard in parallel. Tensors are broadcast to all shards if they are lexically captured by `computation`. e.g., x = tf.constant(7) def computation(): return x + 3 ... = shard(computation, ...) TODO(phawkins): consider adding support for broadcasting Tensors passed as inputs. If `outputs_from_all_shards` is true, the outputs from all shards of `computation` are concatenated back together along their `output_shard_axes`. Otherwise, each output is taken from an arbitrary shard. Inputs and outputs of the computation must be at least rank-1 Tensors. Args: computation: A Python function that builds a computation to apply to each shard of the input. inputs: A list of input tensors or None (equivalent to an empty list). Each input tensor has a corresponding shard axes, given by `input_shard_axes`, which must have size divisible by `num_shards`. num_shards: The number of shards. input_shard_axes: A list of dimensions along which to shard `inputs`, or `None`. `None` means "shard all inputs along dimension 0". If not `None`, there must be one dimension per input. outputs_from_all_shards: Boolean or list of boolean. For each output, if `True`, outputs from all shards are concatenated along the corresponding `output_shard_axes` entry. Otherwise, each output is taken from an arbitrary shard. If the argument is a boolean, the argument's value is used for each output. output_shard_axes: A list of dimensions along which to concatenate the outputs of `computation`, or `None`. `None` means "concatenate all outputs along dimension 0". If not `None`, there must be one dimension per output. Ignored if `outputs_from_all_shards` is False. infeed_queue: If not `None`, the `InfeedQueue` to use to augment the inputs of `computation`. device_assignment: If not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if `None`. The `DeviceAssignment` may be omitted if each shard of the computation uses only one core, and there is either only one shard, or the number of shards is equal to the number of cores in the TPU system. name: (Deprecated) Does nothing. xla_options: An instance of `tpu.XLAOptions` which indicates the options passed to XLA compiler. Use `None` for default options. Returns: A list of output tensors. Raises: ValueError: If num_shards <= 0 ValueError: If len(input_shard_axes) != len(inputs) ValueError: If len(output_shard_axes) != len(outputs from `computation`) """ return split_compile_and_shard( computation, inputs=inputs, num_shards=num_shards, input_shard_axes=input_shard_axes, outputs_from_all_shards=outputs_from_all_shards, output_shard_axes=output_shard_axes, infeed_queue=infeed_queue, device_assignment=device_assignment, name=name, xla_options=xla_options)[1] @tf_export(v1=["tpu.batch_parallel"]) @traceback_utils.filter_traceback def batch_parallel( computation: Callable[..., Any], inputs: Optional[List[List[Optional[core_types.Tensor]]]] = None, num_shards: int = 1, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None, xla_options: Optional[XLAOptions] = None): """Shards `computation` along the batch dimension for parallel execution. Convenience wrapper around shard(). `inputs` must be a list of Tensors or None (equivalent to an empty list). Each input is split into `num_shards` pieces along the 0-th dimension, and computation is applied to each shard in parallel. Tensors are broadcast to all shards if they are lexically captured by `computation`. e.g., x = tf.constant(7) def computation(): return x + 3 ... = shard(computation, ...) The outputs from all shards are concatenated back together along their 0-th dimension. Inputs and outputs of the computation must be at least rank-1 Tensors. Args: computation: A Python function that builds a computation to apply to each shard of the input. inputs: A list of input tensors or None (equivalent to an empty list). The 0-th dimension of each Tensor must have size divisible by `num_shards`. num_shards: The number of shards. infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple of arguments as inputs to `computation`. device_assignment: If not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if `None`. The `DeviceAssignment` may be omitted if each shard of the computation uses only one core, and there is either only one shard, or the number of shards is equal to the number of cores in the TPU system. name: (Deprecated) Does nothing. xla_options: An instance of `tpu.XLAOptions` which indicates the options passed to XLA compiler. Use `None` for default options. Returns: A list of output tensors. Raises: ValueError: If `num_shards <= 0` """ return shard( computation, inputs, num_shards=num_shards, infeed_queue=infeed_queue, device_assignment=device_assignment, name=name, xla_options=xla_options) @tf_export(v1=["tpu.rewrite"]) @traceback_utils.filter_traceback def rewrite( computation: Callable[..., Any], inputs: Optional[List[List[Optional[core_types.Tensor]]]] = None, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None, xla_options: Optional[XLAOptions] = None) -> Any: """Rewrites `computation` for execution on a TPU system. Args: computation: A Python function that builds a computation to apply to the input. If the function takes n inputs, 'inputs' should be a list of n tensors. `computation` may return a list of operations and tensors. Tensors must come before operations in the returned list. The return value of `rewrite` is a list of tensors corresponding to the tensors from the output of `computation`. All `Operation`s constructed during `computation` will be executed when evaluating any of the returned output tensors, not just the ones returned. inputs: A list of input tensors or `None` (equivalent to an empty list). Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with `tf.convert_to_tensor`. infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple of arguments as inputs to `computation`. device_assignment: if not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. May be omitted for a single-core computation, in which case the core attached to task 0, TPU device 0 is used. name: (Deprecated) Does nothing. xla_options: An instance of `tpu.XLAOptions` which indicates the options passed to XLA compiler. Use `None` for default options. Returns: Same data structure as if computation(*inputs) is called directly with some exceptions for correctness. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. TODO(b/121383831): Investigate into removing these special cases. """ # TODO(b/36647078) remove disable when pylint bug is fixed. # pylint: disable=indexing-exception return replicate( computation, None if inputs is None else [inputs], infeed_queue=infeed_queue, device_assignment=device_assignment, name=name, xla_options=xla_options)[0] # pylint: enable=indexing-exception # Operations that indicate some error in the user's inference graph. _DENYLISTED_INFERENCE_OPS = set([ "ReadVariableOp", "AssignVariableOp", "AssignAddVariableOp", "AssignSubVariableOp", "VarHandleOp", "Variable", "VariableV2", ]) def under_tpu_inference_context() -> bool: """Check if it is currently under `_TPUInferenceContext`.""" graph = ops.get_default_graph() while graph: context = graph._get_control_flow_context() # pylint: disable=protected-access while context: if isinstance(context, _TPUInferenceContext): return True context = context.outer_context if isinstance(graph, function._FuncGraph): # pylint: disable=protected-access graph = graph._outer_graph # pylint: disable=protected-access elif isinstance(graph, func_graph.FuncGraph): graph = graph.outer_graph else: return False return False class _TPUInferenceContext(control_flow_ops.XLAControlFlowContext): """A `ControlFlowContext` for nodes inside a TPU inference computation. The primary role of `_TPUInferenceContext` is to indicate the mode of operation and possibly sanity check operators inside a tpu.rewrite_for_inference() computation. """ def __init__(self, name: Text, check_ops: bool = True): super(_TPUInferenceContext, self).__init__() self._name = name self._check_ops = check_ops def AddOp(self, op): self._AddOpInternal(op) def _AddOpInternal(self, op): # pylint: disable=protected-access if self._check_ops and op.type in _DENYLISTED_INFERENCE_OPS: raise NotImplementedError( f"Operation of type {op.type} ({op.name}) is not supported on the " "TPU for inference. Execution will fail if this op is used in the " "graph. Make sure your variables are using variable_scope.") if self._outer_context: self._outer_context.AddInnerOp(op) def AddValue(self, val): result = val if self._outer_context: result = self._outer_context.AddValue(val) return result def AddInnerOp(self, op): self._AddOpInternal(op) @property def grad_state(self): return None def validate_inference_rewrite_for_variables(graph: ops.Graph): """Validates whether rewrite_for_inference() 'worked' for variables. The rewrite_for_inference() method is supposed to append GuaranteeConstOps after ReadVariableOps, but this mechanism works only if you are using tf.compat.v1.get_variable() to create and access variables in your tpu computation. This validation method can be called immediately after calling tpu.rewrite_for_inference() to check whether GuaranteeConstOps where added to the graph. Typical usages: tpu.validate_inference_rewrite_for_variables( tf.compat.v1.get_default_graph()) tpu.validate_inference_rewrite_for_variables(sess.graph) Args: graph: The graph which needs to be validated. Raises: RuntimeError: if validation failed. """ if not any(x.type == "GuaranteeConst" for x in graph.get_operations()): raise RuntimeError( "No GuaranteeConst ops found in the graph after running " "tpu.rewrite_for_inference(...). Please check that you are using " "tf.get_variable() to create and access variables in your tpu " "computation.") def rewrite_for_inference( computation: Callable[..., Any], inputs: Optional[List[core_types.Tensor]] = None, infeed_queue: Optional[tpu_feed.InfeedQueue] = None, device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None, name: Optional[Text] = None) -> List[core_types.Tensor]: """Rewrites `computation` for inference on a TPU system. Other than 'rewriting' the computation to run on a TPU, if using variables in your computation, it moves the ReadVariableOps outside the TPU computation, and adds GuaranteeConst ops just after the ReadVariableOps. This mechanism works only if you are using tf.compat.v1.get_variable() to create and access variables in your tpu computation. You can validate whether this worked, by calling validate_inference_rewrite_for_variables() method immediately after this method to check whether GuaranteeConstOps where added to the graph. Args: computation: A Python function that builds a computation to apply to the input. If the function takes n inputs, 'inputs' should be a list of n tensors. If the function returns m outputs, rewrite will return a list of m tensors. inputs: A list of input tensors or `None` (equivalent to an empty list). infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple of arguments as inputs to `computation`. device_assignment: if not `None`, a `DeviceAssignment` describing the mapping between logical cores in the computation with physical cores in the TPU topology. May be omitted for a single-core computation, in which case the core attached to task 0, TPU device 0 is used. name: The name of the operator. Returns: A list of output tensors. """ def guarantee_const_getter(getter, name, *args, **kwargs): with ops.control_dependencies(None): return array_ops.guarantee_const( getter(name, *args, **kwargs), name=name + "/GuaranteeConst") def wrapped_computation(*args, **kwargs): """Execute computation under `_TPUInferenceContext`.""" context = _TPUInferenceContext( name=ops.get_default_graph().unique_name("rewrite_for_inference")) try: context.Enter() vscope = variable_scope.get_variable_scope() prev_custom_getter = vscope.custom_getter prev_caching_device = vscope.caching_device vscope.set_custom_getter(guarantee_const_getter) vscope.set_caching_device(lambda op: op.device) result = computation(*args, **kwargs) vscope.set_custom_getter(prev_custom_getter) vscope.set_caching_device(prev_caching_device) finally: context.Exit() return result # pylint: disable=undefined-variable return rewrite( wrapped_computation, inputs=inputs, infeed_queue=infeed_queue, device_assignment=device_assignment, name=name) # pylint: enable=undefined-variable def prune_unconnected_ops_from_xla(prune_graph: ops.Graph): """Prunes unconnected ops as listed in _UNCONNECTED_OPS_TO_PRUNE. Args: prune_graph: A tensorflow graph from which we wish to prune unconnected ops as listed in _UNCONNECTED_OPS_TO_PRUNE. In general, these ops should have no inputs and no consumers. These can often be left behind due to graph construction rewiring (for instance TF-Hub). While they never execute, they will cause XLA compile to fail so we strip them from XLA compile by removing the tpu_replicate attribute. """ # Scan over the top level graph and all function graphs. for graph in [prune_graph] + [ f for f in prune_graph._functions.values() # pylint: disable=protected-access ]: if not isinstance(graph, ops.Graph): continue for op in graph.get_operations(): if op.type not in _UNCONNECTED_OPS_TO_PRUNE: continue outputs_consumed = False for output in op.outputs: if output.consumers(): outputs_consumed = True break if not outputs_consumed: logging.info( "Pruning OP %s of type %s from XLA Compile due to " "it being disconnected.", op.name, op.type) op._clear_attr(tpu_replication._TPU_REPLICATE_ATTR) # pylint: disable=protected-access