package tensorflow

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Used in: NodeExecStats, TensorDescription

• int64 requested_bytes = 1

  Total number of bytes requested

• int64 allocated_bytes = 2

  Total number of bytes allocated if known

• string allocator_name = 3

  Name of the allocator used

• int64 allocation_id = 4

  Identifier of the allocated buffer if known

• bool has_single_reference = 5

  Set if this tensor only has one remaining reference

• uint64 ptr = 6

  Address of the allocation.

An allocation/de-allocation operation performed by the allocator.

Used in: AllocatorMemoryUsed

• int64 alloc_micros = 1

  The timestamp of the operation.

• int64 alloc_bytes = 2

  Number of bytes allocated, or de-allocated if negative.

Used in: NodeExecStats

• string allocator_name = 1

• int64 total_bytes = 2

  These are per-node allocator memory stats.

• int64 peak_bytes = 3

• int64 live_bytes = 4

  The bytes that are not deallocated.

• repeated AllocationRecord allocation_records = 6

  The allocation and deallocation timeline.

• int64 allocator_bytes_in_use = 5

  These are snapshots of the overall allocator memory stats. The number of live bytes currently allocated by the allocator.

Used to specify and override the default API & behavior in the generated code for client languages, from what you would get from the OpDef alone. There will be a set of ApiDefs that are common to all client languages, and another set per client language. The per-client-language ApiDefs will inherit values from the common ApiDefs which it can either replace or modify. We separate the API definition from the OpDef so we can evolve the API while remaining backwards compatible when interpretting old graphs. Overrides go in an "api_def.pbtxt" file with a text-format ApiDefs message. WARNING: Be *very* careful changing the API for any existing op -- you can change the semantics of existing code. These changes may need to wait until a major release of TensorFlow to avoid breaking our compatibility promises.

Used in: ApiDefs

• string graph_op_name = 1

  Name of the op (in the OpDef) to specify the API for.

• string deprecation_message = 12

  If this op is deprecated, set deprecation message to the message that should be logged when this op is used. The message should indicate alternative op to use, if any.

• int32 deprecation_version = 13

  Major version when the op will be deleted. For e.g. set this value to 2 if op API should be removed in TensorFlow 2.0 and deprecated in versions before that.

• ApiDef.Visibility visibility = 2

• repeated ApiDef.Endpoint endpoint = 3

• repeated ApiDef.Arg in_arg = 4

• repeated ApiDef.Arg out_arg = 5

• repeated string arg_order = 11

  List of original in_arg names to specify new argument order. Length of arg_order should be either empty to keep current order or match size of in_arg.

• repeated ApiDef.Attr attr = 6

• string summary = 7

  One-line human-readable description of what the Op does.

• string description = 8

  Additional, longer human-readable description of what the Op does.

• string description_prefix = 9

  Modify an existing/inherited description by adding text to the beginning or end.

• string description_suffix = 10

Used in: ApiDef

• string name = 1

• string rename_to = 2

  Change the name used to access this arg in the API from what is used in the GraphDef. Note that these names in `backticks` will also be replaced in the summary & description fields.

• string description = 3

  Note: this will replace any inherited arg doc. There is no current way of modifying arg descriptions (other than replacing them entirely) as can be done with op descriptions.

Description of the graph-construction-time configuration of this Op. That is to say, this describes the attr fields that will be specified in the NodeDef.

Used in: ApiDef

• string name = 1

• string rename_to = 2

  Change the name used to access this attr in the API from what is used in the GraphDef. Note that these names in `backticks` will also be replaced in the summary & description fields.

• optional AttrValue default_value = 3

  Specify a new default value to use for this attr. This default will be used when creating new graphs, as opposed to the default in the OpDef, which will be used when interpreting old GraphDefs.

• string description = 4

  Note: this will replace any inherited attr doc, there is no current way of modifying attr descriptions as can be done with op descriptions.

If you specify any endpoint, this will replace all of the inherited endpoints. The first endpoint should be the "canonical" endpoint, and should not be deprecated (unless all endpoints are deprecated).

Used in: ApiDef

• string name = 1

  Name should be either like "CamelCaseName" or "Package.CamelCaseName". Client-language-specific ApiDefs may use a snake_case convention instead of CamelCase.

• bool deprecated = 3

  Set if this endpoint is deprecated. If set to true, a message suggesting to use a non-deprecated endpoint instead will be printed. If all endpoints are deprecated, set deprecation_message in ApiDef instead.

• int32 deprecation_version = 4

  Major version when an endpoint will be deleted. For e.g. set this value to 2 if endpoint should be removed in TensorFlow 2.0 and deprecated in versions before that.

Used in: ApiDef

• DEFAULT_VISIBILITY = 0

  Normally this is "VISIBLE" unless you are inheriting a different value from another ApiDef.

• VISIBLE = 1

  Publicly visible in the API.

• SKIP = 2

  Do not include this op in the generated API. If visibility is set to 'SKIP', other fields are ignored for this op.

• HIDDEN = 3

  Hide this op by putting it into an internal namespace (or whatever is appropriate in the target language).

• repeated ApiDef op = 1

Protocol buffer representing the value for an attr used to configure an Op. Comment indicates the corresponding attr type. Only the field matching the attr type may be filled.

Used in: ApiDef.Attr, FunctionDef, FunctionDef.ArgAttrs, KernelDef.AttrConstraint, NameAttrList, NodeDef, OpDef.AttrDef

• oneof value

  • bytes s = 2

    "string"

  • int64 i = 3

    "int"

  • float f = 4

    "float"

  • bool b = 5

    "bool"

  • DataType type = 6

    "type"

  • TensorShapeProto shape = 7

    "shape"

  • TensorProto tensor = 8

    "tensor"

  • AttrValue.ListValue list = 1

    any "list(...)"

  • NameAttrList func = 10

    "func" represents a function. func.name is a function's name or a primitive op's name. func.attr.first is the name of an attr defined for that function. func.attr.second is the value for that attr in the instantiation.

  • string placeholder = 9

    This is a placeholder only used in nodes defined inside a function. It indicates the attr value will be supplied when the function is instantiated. For example, let us suppose a node "N" in function "FN". "N" has an attr "A" with value placeholder = "foo". When FN is instantiated with attr "foo" set to "bar", the instantiated node N's attr A will have been given the value "bar".

LINT.IfChange

Used in: AttrValue

• repeated bytes s = 2

  "list(string)"

• repeated int64 i = 3

  "list(int)"

• repeated float f = 4

  "list(float)"

• repeated bool b = 5

  "list(bool)"

• repeated DataType type = 6

  "list(type)"

• repeated TensorShapeProto shape = 7

  "list(shape)"

• repeated TensorProto tensor = 8

  "list(tensor)"

• repeated NameAttrList func = 9

  "list(attr)"

• repeated CostGraphDef.Node node = 1

• repeated CostGraphDef.AggregatedCost cost = 2

Total cost of this graph, typically used for balancing decisions.

Used in: CostGraphDef

• float cost = 1

  Aggregated cost value.

• string dimension = 2

  Aggregated cost dimension (e.g. 'memory', 'compute', 'network').

Used in: CostGraphDef

• string name = 1

  The name of the node. Names are globally unique.

• string device = 2

  The device of the node. Can be empty if the node is mapped to the default partition or partitioning hasn't been run yet.

• int32 id = 3

  The id of the node. Node ids are only unique inside a partition.

• repeated Node.InputInfo input_info = 4

• repeated Node.OutputInfo output_info = 5

• int64 temporary_memory_size = 6

  Temporary memory used by this node.

• int64 persistent_memory_size = 12

  Persistent memory used by this node.

• int64 host_temp_memory_size = 10

• int64 device_temp_memory_size = 11

• int64 device_persistent_memory_size = 16

• int64 compute_cost = 9

  Estimate of the computational cost of this node, in microseconds.

• int64 compute_time = 14

  Analytical estimate of the computational cost of this node, in microseconds.

• int64 memory_time = 15

  Analytical estimate of the memory access cost of this node, in microseconds.

• bool is_final = 7

  If true, the output is permanent: it can't be discarded, because this node is part of the "final output". Nodes may depend on final nodes.

• repeated int32 control_input = 8

  Ids of the control inputs for this node.

• bool inaccurate = 17

  Are the costs inaccurate?

Inputs of this node. They must be executed before this node can be executed. An input is a particular output of another node, specified by the node id and the output index.

Used in: Node

• int32 preceding_node = 1

• int32 preceding_port = 2

Outputs of this node.

Used in: Node

• int64 size = 1

• int64 alias_input_port = 2

  If >= 0, the output is an alias of an input. Note that an alias input may itself be an alias. The algorithm will therefore need to follow those pointers.

• optional TensorShapeProto shape = 3

• DataType dtype = 4

Used in: SummaryMetadata

• DATA_CLASS_UNKNOWN = 0

  Unknown data class, used (implicitly) for legacy data. Will not be processed by data ingestion pipelines.

• DATA_CLASS_SCALAR = 1

  Scalar time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-0 tensor of floating-point dtype, which will be converted to float64.

• DATA_CLASS_TENSOR = 2

  Tensor time series. Each `Value` for the corresponding tag must have `tensor` set. The tensor value is arbitrary, but should be small to accommodate direct storage in database backends: an upper bound of a few kilobytes is a reasonable rule of thumb.

• DATA_CLASS_BLOB_SEQUENCE = 3

  Blob sequence time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-1 tensor of bytestring dtype.

(== suppress_warning documentation-presence ==) LINT.IfChange

Used in: AttrValue, AttrValue.ListValue, CostGraphDef.Node.OutputInfo, GraphTransferConstNodeInfo, GraphTransferGraphInputNodeInfo, GraphTransferGraphOutputNodeInfo, OpDef.ArgDef, RemoteFusedGraphExecuteInfo.TensorShapeTypeProto, ResourceHandleProto.DtypeAndShape, TensorDescription, TensorProto

• DT_INVALID = 0

  Not a legal value for DataType. Used to indicate a DataType field has not been set.

• DT_FLOAT = 1

  Data types that all computation devices are expected to be capable to support.

• DT_DOUBLE = 2

• DT_INT32 = 3

• DT_UINT8 = 4

• DT_INT16 = 5

• DT_INT8 = 6

• DT_STRING = 7

• DT_COMPLEX64 = 8

  Single-precision complex

• DT_INT64 = 9

• DT_BOOL = 10

• DT_QINT8 = 11

  Quantized int8

• DT_QUINT8 = 12

  Quantized uint8

• DT_QINT32 = 13

  Quantized int32

• DT_BFLOAT16 = 14

  Float32 truncated to 16 bits. Only for cast ops.

• DT_QINT16 = 15

  Quantized int16

• DT_QUINT16 = 16

  Quantized uint16

• DT_UINT16 = 17

• DT_COMPLEX128 = 18

  Double-precision complex

• DT_HALF = 19

• DT_RESOURCE = 20

• DT_VARIANT = 21

  Arbitrary C++ data types

• DT_UINT32 = 22

• DT_UINT64 = 23

• DT_FLOAT_REF = 101

  Do not use! These are only for parameters. Every enum above should have a corresponding value below (verified by types_test).

• DT_DOUBLE_REF = 102

• DT_INT32_REF = 103

• DT_UINT8_REF = 104

• DT_INT16_REF = 105

• DT_INT8_REF = 106

• DT_STRING_REF = 107

• DT_COMPLEX64_REF = 108

• DT_INT64_REF = 109

• DT_BOOL_REF = 110

• DT_QINT8_REF = 111

• DT_QUINT8_REF = 112

• DT_QINT32_REF = 113

• DT_BFLOAT16_REF = 114

• DT_QINT16_REF = 115

• DT_QUINT16_REF = 116

• DT_UINT16_REF = 117

• DT_COMPLEX128_REF = 118

• DT_HALF_REF = 119

• DT_RESOURCE_REF = 120

• DT_VARIANT_REF = 121

• DT_UINT32_REF = 122

• DT_UINT64_REF = 123

• string name = 1

  Fully specified name of the device within a cluster.

• string device_type = 2

  String representation of device_type.

• int64 memory_limit = 4

  Memory capacity of device in bytes.

• optional DeviceLocality locality = 5

  Platform-specific data about device that may be useful for supporting efficient data transfers.

• fixed64 incarnation = 6

  A device is assigned a global unique number each time it is initialized. "incarnation" should never be 0.

• string physical_device_desc = 7

  String representation of the physical device that this device maps to.

Used in: DeviceAttributes

• int32 bus_id = 1

  Optional bus locality of device. Default value of 0 means no specific locality. Specific localities are indexed from 1.

• int32 numa_node = 2

  Optional NUMA locality of device.

• optional LocalLinks links = 3

  Optional local interconnect links to other devices.

Used in: StepStats

• string device = 1

• repeated NodeExecStats node_stats = 2

• map<uint32, string> thread_names = 3

  Its key is thread id.

A function can be instantiated when the runtime can bind every attr with a value. When a GraphDef has a call to a function, it must have binding for every attr defined in the signature. TODO(zhifengc): * device spec, etc.

Used in: FunctionDefLibrary

• optional OpDef signature = 1

  The definition of the function's name, arguments, return values, attrs etc.

• map<string, AttrValue> attr = 5

  Attributes specific to this function definition.

• map<uint32, FunctionDef.ArgAttrs> arg_attr = 7

• map<uint32, uint32> resource_arg_unique_id = 8

  Unique IDs for each resource argument, used to track aliasing resources. If Argument A and Argument B alias each other, then resource_arg_unique_ids[A.index] == resource_arg_unique_ids[B.index]. If this field is empty, none of the arguments could alias; otherwise, every resource argument should have an entry in this field. When instantiated, the unique IDs will be attached to the _Arg nodes' "_resource_arg_unique_id" attribute.

• repeated NodeDef node_def = 3

  By convention, "op" in node_def is resolved by consulting with a user-defined library first. If not resolved, "func" is assumed to be a builtin op.

• map<string, string> ret = 4

  A mapping from the output arg names from `signature` to the outputs from `node_def` that should be returned by the function.

• map<string, string> control_ret = 6

  A mapping from control output names from `signature` to node names in `node_def` which should be control outputs of this function.

Attributes for function arguments. These attributes are the same set of valid attributes as to _Arg nodes.

Used in: FunctionDef

• map<string, AttrValue> attr = 1

A library is a set of named functions.

Used in: GraphDef

• repeated FunctionDef function = 1

• repeated GradientDef gradient = 2

GradientDef defines the gradient function of a function defined in a function library. A gradient function g (specified by gradient_func) for a function f (specified by function_name) must follow the following: The function 'f' must be a numerical function which takes N inputs and produces M outputs. Its gradient function 'g', which is a function taking N + M inputs and produces N outputs. I.e. if we have (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), then, g is (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, dL/dy1, dL/dy2, ..., dL/dy_M), where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the loss function). dL/dx_i is the partial derivative of L with respect to x_i.

Used in: FunctionDefLibrary

• string function_name = 1

  The function name.

• string gradient_func = 2

  The gradient function's name.

Represents the graph of operations

Used in: RemoteFusedGraphExecuteInfo

• repeated NodeDef node = 1

• optional VersionDef versions = 4

  Compatibility versions of the graph. See core/public/version.h for version history. The GraphDef version is distinct from the TensorFlow version, and each release of TensorFlow will support a range of GraphDef versions.

• int32 version = 3

  Deprecated single version field; use versions above instead. Since all GraphDef changes before "versions" was introduced were forward compatible, this field is entirely ignored.

• optional FunctionDefLibrary library = 2

  EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET. "library" provides user-defined functions. Naming: * library.function.name are in a flat namespace. NOTE: We may need to change it to be hierarchical to support different orgs. E.g., { "/google/nn", { ... }}, { "/google/vision", { ... }} { "/org_foo/module_bar", { ... }} map<string, FunctionDefLib> named_lib; * If node[i].op is the name of one function in "library", node[i] is deemed as a function call. Otherwise, node[i].op must be a primitive operation supported by the runtime. Function call semantics: * The callee may start execution as soon as some of its inputs are ready. The caller may want to use Tuple() mechanism to ensure all inputs are ready in the same time. * The consumer of return values may start executing as soon as the return values the consumer depends on are ready. The consumer may want to use Tuple() mechanism to ensure the consumer does not start until all return values of the callee function are ready.

Used in: GraphTransferInfo

• string name = 1

• int32 node_id = 2

• repeated int64 shape = 3

• bytes data = 4

• DataType dtype = 5

Used in: GraphTransferInfo

• string name = 1

• repeated int64 shape = 2

• DataType dtype = 3

Used in: GraphTransferInfo

• string name = 1

• repeated int64 shape = 2

• DataType dtype = 3

Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.

• repeated GraphTransferNodeInfo node_info = 1

• repeated GraphTransferConstNodeInfo const_node_info = 2

• repeated GraphTransferNodeInputInfo node_input_info = 3

• repeated GraphTransferNodeOutputInfo node_output_info = 4

• repeated GraphTransferGraphInputNodeInfo graph_input_node_info = 5

  Input Node parameters of transferred graph

• repeated GraphTransferGraphOutputNodeInfo graph_output_node_info = 6

• GraphTransferInfo.Destination destination = 7

  Destination of graph transfer

Used in: GraphTransferInfo

• NOP = 0

• HEXAGON = 1

Used in: GraphTransferInfo

• string name = 1

• int32 node_id = 2

• string type_name = 3

• int32 soc_op_id = 4

• int32 padding_id = 5

• int32 input_count = 6

• int32 output_count = 7

Used in: GraphTransferNodeInputInfo

• int32 node_id = 1

• int32 output_port = 2

Used in: GraphTransferInfo

• int32 node_id = 1

• repeated GraphTransferNodeInput node_input = 2

Used in: GraphTransferInfo

• int32 node_id = 1

• repeated int32 max_byte_size = 2

Serialization format for histogram module in core/lib/histogram/histogram.h

Used in: Summary.Value

• double min = 1

• double max = 2

• double num = 3

• double sum = 4

• double sum_squares = 5

• repeated double bucket_limit = 6

  Parallel arrays encoding the bucket boundaries and the bucket values. bucket(i) is the count for the bucket i. The range for a bucket is: i == 0: -DBL_MAX .. bucket_limit(0) i != 0: bucket_limit(i-1) .. bucket_limit(i)

• repeated double bucket = 7

Used in: LocalLinks

• int32 device_id = 1

• string type = 2

• int32 strength = 3

Used in: KernelList

• string op = 1

  Must match the name of an Op.

• string device_type = 2

  Type of device this kernel runs on.

• repeated KernelDef.AttrConstraint constraint = 3

• repeated string host_memory_arg = 4

  Names of the Op's input_/output_args that reside in host memory instead of device memory.

• string label = 5

  This allows experimental kernels to be registered for an op that won't be used unless the user specifies a "_kernel" attr with value matching this.

• int32 priority = 6

  Prioritization of kernel amongst different devices. By default we assume priority is 0. The higher the priority the better. By default (i.e. if this is not set), we prefer GPU kernels over CPU.

Used in: KernelDef

• string name = 1

  Name of an attr from the Op.

• optional AttrValue allowed_values = 2

  A list of values that this kernel supports for this attr. Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.

A collection of KernelDefs

• repeated KernelDef kernel = 1

Used in: DeviceLocality

• repeated InterconnectLink link = 1

• int64 step_id = 1

  Process-unique step id.

• string operation = 2

  Name of the operation making the allocation.

• int64 num_bytes = 3

  Number of bytes in the allocation.

• uint64 ptr = 4

  Address of the allocation.

• int64 allocation_id = 5

  Id of the tensor buffer being allocated, used to match to a corresponding deallocation.

• string allocator_name = 6

  Name of the allocator used.

• int64 step_id = 1

  Process-unique step id.

• string operation = 2

  Name of the operation making the deallocation.

• int64 allocation_id = 3

  Id of the tensor buffer being deallocated, used to match to a corresponding allocation.

• string allocator_name = 4

  Name of the allocator used.

• bool deferred = 5

  True if the deallocation is queued and will be performed later, e.g. for GPU lazy freeing of buffers.

• int64 step_id = 1

  Process-unique step id.

• string handle = 2

  Handle describing the feeds and fetches of the step.

• int64 step_id = 1

  Process-unique step id.

• string kernel_name = 2

  Name of the kernel making the allocation as set in GraphDef, e.g., "affine2/weights/Assign".

• optional TensorDescription tensor = 3

  Allocated tensor details.

• int64 allocation_id = 1

  Id of the tensor buffer being deallocated, used to match to a corresponding allocation.

• string allocator_name = 2

  Name of the allocator used.

• int64 step_id = 1

  Process-unique step id.

• string kernel_name = 2

  Name of the kernel producing an output as set in GraphDef, e.g., "affine2/weights/Assign".

• int32 index = 3

  Index of the output being set.

• optional TensorDescription tensor = 4

  Output tensor details.

For memory tracking.

Used in: NodeExecStats

• int64 temp_memory_size = 1

• int64 persistent_memory_size = 3

• repeated int64 persistent_tensor_alloc_ids = 5

• int64 device_temp_memory_size = 2

• int64 device_persistent_memory_size = 4

• repeated int64 device_persistent_tensor_alloc_ids = 6

A list of attr names and their values. The whole list is attached with a string name. E.g., MatMul[T=float].

Used in: AttrValue, AttrValue.ListValue

• string name = 1

• map<string, AttrValue> attr = 2

Used in: FunctionDef, GraphDef

• string name = 1

  The name given to this operator. Used for naming inputs, logging, visualization, etc. Unique within a single GraphDef. Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_>./]*".

• string op = 2

  The operation name. There may be custom parameters in attrs. Op names starting with an underscore are reserved for internal use.

• repeated string input = 3

  Each input is "node:src_output" with "node" being a string name and "src_output" indicating which output tensor to use from "node". If "src_output" is 0 the ":0" suffix can be omitted. Regular inputs may optionally be followed by control inputs that have the format "^node".

• string device = 4

  A (possibly partial) specification for the device on which this node should be placed. The expected syntax for this string is as follows: DEVICE_SPEC ::= PARTIAL_SPEC PARTIAL_SPEC ::= ("/" CONSTRAINT) * CONSTRAINT ::= ("job:" JOB_NAME) | ("replica:" [1-9][0-9]*) | ("task:" [1-9][0-9]*) | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") ) Valid values for this string include: * "/job:worker/replica:0/task:1/device:GPU:3" (full specification) * "/job:worker/device:GPU:3" (partial specification) * "" (no specification) If the constraints do not resolve to a single device (or if this field is empty or not present), the runtime will attempt to choose a device automatically.

• map<string, AttrValue> attr = 5

  Operation-specific graph-construction-time configuration. Note that this should include all attrs defined in the corresponding OpDef, including those with a value matching the default -- this allows the default to change and makes NodeDefs easier to interpret on their own. However, if an attr with a default is not specified in this list, the default will be used. The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and one of the names from the corresponding OpDef's attr field). The values must have a type matching the corresponding OpDef attr's type field. TODO(josh11b): Add some examples here showing best practices.

• optional NodeDef.ExperimentalDebugInfo experimental_debug_info = 6

  This stores debug information associated with the node.

Used in: NodeDef

• repeated string original_node_names = 1

  Opaque string inserted into error messages created by the runtime. This is intended to store the list of names of the nodes from the original graph that this node was derived. For example if this node, say C, was result of a fusion of 2 nodes A and B, then 'original_node' would be {A, B}. This information can be used to map errors originating at the current node to some top level source code.

• repeated string original_func_names = 2

  This is intended to store the list of names of the functions from the original graph that this node was derived. For example if this node, say C, was result of a fusion of node A in function FA and node B in function FB, then `original_funcs` would be {FA, FB}. If the node is in the top level graph, the `original_func` is empty. This information, with the `original_node_names` can be used to map errors originating at the current ndoe to some top level source code.

Time/size stats recorded for a single execution of a graph node.

Used in: DeviceStepStats

• string node_name = 1

  TODO(tucker): Use some more compact form of node identity than the full string name. Either all processes should agree on a global id (cost_id?) for each node, or we should use a hash of the name.

• int64 all_start_micros = 2

• int64 op_start_rel_micros = 3

• int64 op_end_rel_micros = 4

• int64 all_end_rel_micros = 5

• repeated AllocatorMemoryUsed memory = 6

• repeated NodeOutput output = 7

• string timeline_label = 8

• int64 scheduled_micros = 9

• uint32 thread_id = 10

• repeated AllocationDescription referenced_tensor = 11

• optional MemoryStats memory_stats = 12

• int64 all_start_nanos = 13

• int64 op_start_rel_nanos = 14

• int64 op_end_rel_nanos = 15

• int64 all_end_rel_nanos = 16

• int64 scheduled_nanos = 17

Output sizes recorded for a single execution of a graph node.

Used in: NodeExecStats

• int32 slot = 1

• optional TensorDescription tensor_description = 3

Defines an operation. A NodeDef in a GraphDef specifies an Op by using the "op" field which should match the name of a OpDef. LINT.IfChange

Used in: FunctionDef, OpList

• string name = 1

  Op names starting with an underscore are reserved for internal use. Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9>_]*".

• repeated OpDef.ArgDef input_arg = 2

  Description of the input(s).

• repeated OpDef.ArgDef output_arg = 3

  Description of the output(s).

• repeated string control_output = 20

  Named control outputs for this operation. Useful only for composite operations (i.e. functions) which want to name different control outputs.

• repeated OpDef.AttrDef attr = 4

• optional OpDeprecation deprecation = 8

  Optional deprecation based on GraphDef versions.

• string summary = 5

  One-line human-readable description of what the Op does.

• string description = 6

  Additional, longer human-readable description of what the Op does.

• bool is_commutative = 18

  True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)

• bool is_aggregate = 16

  If is_aggregate is true, then this operation accepts N >= 2 inputs and produces 1 output all of the same type. Should be associative and commutative, and produce output with the same shape as the input. The optimizer may replace an aggregate op taking input from multiple devices with a tree of aggregate ops that aggregate locally within each device (and possibly within groups of nearby devices) before communicating. TODO(josh11b): Implement that optimization.

  for things like add

• bool is_stateful = 17

  Ops are marked as stateful if their behavior depends on some state beyond their input tensors (e.g. variable reading op) or if they have a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops must always produce the same output for the same input and have no side-effects. By default Ops may be moved between devices. Stateful ops should either not be moved, or should only be moved if that state can also be moved (e.g. via some sort of save / restore). Stateful ops are guaranteed to never be optimized away by Common Subexpression Elimination (CSE).

  for things like variables, queue

• bool allows_uninitialized_input = 19

  By default, all inputs to an Op must be initialized Tensors. Ops that may initialize tensors for the first time should set this field to true, to allow the Op to take an uninitialized Tensor as input.

  for Assign, etc.

For describing inputs and outputs.

Used in: OpDef

• string name = 1

  Name for the input/output. Should match the regexp "[a-z][a-z0-9_]*".

• string description = 2

  Human readable description.

• DataType type = 3

  Describes the type of one or more tensors that are accepted/produced by this input/output arg. The only legal combinations are: * For a single tensor: either the "type" field is set or the "type_attr" field is set to the name of an attr with type "type". * For a sequence of tensors with the same type: the "number_attr" field will be set to the name of an attr with type "int", and either the "type" or "type_attr" field will be set as for single tensors. * For a sequence of tensors, the "type_list_attr" field will be set to the name of an attr with type "list(type)".

• string type_attr = 4

  if specified, attr must have type "type"

• string number_attr = 5

  if specified, attr must have type "int"

• string type_list_attr = 6

  If specified, attr must have type "list(type)", and none of type, type_attr, and number_attr may be specified.

• bool is_ref = 16

  For inputs: if true, the inputs are required to be refs. By default, inputs can be either refs or non-refs. For outputs: if true, outputs are refs, otherwise they are not.

Description of the graph-construction-time configuration of this Op. That is to say, this describes the attr fields that will be specified in the NodeDef.

Used in: OpDef

• string name = 1

  A descriptive name for the argument. May be used, e.g. by the Python client, as a keyword argument name, and so should match the regexp "[a-z][a-z0-9_]+".

• string type = 2

  One of the type names from attr_value.proto ("string", "list(string)", "int", etc.).

• optional AttrValue default_value = 3

  A reasonable default for this attribute if the user does not supply a value. If not specified, the user must supply a value.

• string description = 4

  Human-readable description.

• bool has_minimum = 5

  For type == "int", this is a minimum value. For "list(___)" types, this is the minimum length.

• int64 minimum = 6

• optional AttrValue allowed_values = 7

  The set of allowed values. Has type that is the "list" version of the "type" field above (uses the "list" field of AttrValue). If type == "type" or "list(type)" above, then the "type" field of "allowed_values.list" has the set of allowed DataTypes. If type == "string" or "list(string)", then the "s" field of "allowed_values.list" has the set of allowed strings.

Information about version-dependent deprecation of an op

Used in: OpDef

• int32 version = 1

  First GraphDef version at which the op is disallowed.

• string explanation = 2

  Explanation of why it was deprecated and what to use instead.

A collection of OpDefs

• repeated OpDef op = 1

For serializing and restoring the state of ReaderBase, see reader_base.h for details.

• int64 work_started = 1

• int64 work_finished = 2

• int64 num_records_produced = 3

• bytes current_work = 4

Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.

• optional GraphDef remote_graph = 1

  Definition of remote graph

• repeated string graph_input_node_name = 2

  Remote fused graph input node name

• repeated string graph_output_node_name = 3

  Remote fused graph output node name

• string executor_name = 4

  Executor's name

• bytes serialized_executor_parameters = 5

  Optional: Parameters given to the executor

• repeated RemoteFusedGraphExecuteInfo.TensorShapeTypeProto default_graph_input_tensor_shape = 6

  Optional: Default graph input tensor shape used to allocate memory before executing op

• repeated RemoteFusedGraphExecuteInfo.TensorShapeTypeProto default_graph_output_tensor_shape = 7

  Optional: Default graph input tensor shape used to allocate memory before executing op TODO(satok): Remote output tensor shape once shape information is stored in NodeDef

Used in: RemoteFusedGraphExecuteInfo

• DataType dtype = 1

• optional TensorShapeProto shape = 2

Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.

Used in: TensorProto

• string device = 1

  Unique name for the device containing the resource.

• string container = 2

  Container in which this resource is placed.

• string name = 3

  Unique name of this resource.

• uint64 hash_code = 4

  Hash code for the type of the resource. Is only valid in the same device and in the same execution.

• string maybe_type_name = 5

  For debug-only, the name of the type pointed to by this handle, if available.

• repeated ResourceHandleProto.DtypeAndShape dtypes_and_shapes = 6

  Data types and shapes for the underlying resource.

Protocol buffer representing a pair of (data type, tensor shape).

Used in: ResourceHandleProto

• DataType dtype = 1

• optional TensorShapeProto shape = 2

Used in: VariableDef

• string full_name = 1

  Name of the full variable of which this is a slice.

• repeated int64 full_shape = 2

  Shape of the full variable.

• repeated int64 var_offset = 3

  Offset of this variable into the full variable.

• repeated int64 var_shape = 4

  Shape of this variable.

For identifying the underlying type of a variant. For variants, the types listed here are a subset of the types in the variant type registry, corresponding to commonly used variants which must occasionally be special-cased.

• ST_INVALID = 0

  Invalid/unknown specialized type.

• ST_TENSOR_LIST = 1

  "tensorflow::TensorList" in the variant type registry.

• repeated DeviceStepStats dev_stats = 1

A Summary is a set of named values to be displayed by the visualizer. Summaries are produced regularly during training, as controlled by the "summary_interval_secs" attribute of the training operation. Summaries are also produced at the end of an evaluation.

• repeated Summary.Value value = 1

  Set of values for the summary.

Used in: Value

• float sample_rate = 1

  Sample rate of the audio in Hz.

• int64 num_channels = 2

  Number of channels of audio.

• int64 length_frames = 3

  Length of the audio in frames (samples per channel).

• bytes encoded_audio_string = 4

  Encoded audio data and its associated RFC 2045 content type (e.g. "audio/wav").

• string content_type = 5

Used in: Value

• int32 height = 1

  Dimensions of the image.

• int32 width = 2

• int32 colorspace = 3

  Valid colorspace values are 1 - grayscale 2 - grayscale + alpha 3 - RGB 4 - RGBA 5 - DIGITAL_YUV 6 - BGRA

• bytes encoded_image_string = 4

  Image data in encoded format. All image formats supported by image_codec::CoderUtil can be stored here.

Used in: Summary

• string node_name = 7

  This field is deprecated and will not be set.

• string tag = 1

  Tag name for the data. Used by TensorBoard plugins to organize data. Tags are often organized by scope (which contains slashes to convey hierarchy). For example: foo/bar/0

• optional SummaryMetadata metadata = 9

  Contains metadata on the summary value such as which plugins may use it. Take note that many summary values may lack a metadata field. This is because the FileWriter only keeps a metadata object on the first summary value with a certain tag for each tag. TensorBoard then remembers which tags are associated with which plugins. This saves space.

• oneof value

  Value associated with the tag.

  • float simple_value = 2

  • bytes obsolete_old_style_histogram = 3

  • Image image = 4

  • HistogramProto histo = 5

  • Audio audio = 6

  • TensorProto tensor = 8

Metadata associated with a series of Summary data

• string type_hint = 1

  Hint on how plugins should process the data in this series. Supported values include "scalar", "histogram", "image", "audio"

A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value.

Used in: Summary.Value

• optional SummaryMetadata.PluginData plugin_data = 1

  Data that associates a summary with a certain plugin.

• string display_name = 2

  Display name for viewing in TensorBoard.

• string summary_description = 3

  Longform readable description of the summary sequence. Markdown supported.

• DataClass data_class = 4

  Class of data stored in this time series. Required for compatibility with TensorBoard's generic data facilities (`DataProvider`, et al.). This value imposes constraints on the dtype and shape of the corresponding tensor values. See `DataClass` docs for details.

Used in: SummaryMetadata

• string plugin_name = 1

  The name of the plugin this data pertains to.

• bytes content = 2

  The content to store for the plugin. The best practice is for this to be a binary serialized protocol buffer.

Used in: MemoryLogTensorAllocation, MemoryLogTensorOutput, NodeOutput

• DataType dtype = 1

  Data type of tensor elements

• optional TensorShapeProto shape = 2

  Shape of the tensor.

• optional AllocationDescription allocation_description = 4

  Information about the size and allocator used for the data

Protocol buffer representing a tensor.

Used in: AttrValue, AttrValue.ListValue, Summary.Value, VariantTensorDataProto

• DataType dtype = 1

• optional TensorShapeProto tensor_shape = 2

  Shape of the tensor. TODO(touts): sort out the 0-rank issues.

• int32 version_number = 3

  Version number. In version 0, if the "repeated xxx" representations contain only one element, that element is repeated to fill the shape. This makes it easy to represent a constant Tensor with a single value.

• bytes tensor_content = 4

  Serialized raw tensor content from either Tensor::AsProtoTensorContent or memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation can be used for all tensor types. The purpose of this representation is to reduce serialization overhead during RPC call by avoiding serialization of many repeated small items.

• repeated int32 half_val = 13

  DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll have some pointless zero padding for each value here.

• repeated float float_val = 5

  DT_FLOAT.

• repeated double double_val = 6

  DT_DOUBLE.

• repeated int32 int_val = 7

  DT_INT32, DT_INT16, DT_INT8, DT_UINT8.

• repeated bytes string_val = 8

  DT_STRING

• repeated float scomplex_val = 9

  DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real and imaginary parts of i-th single precision complex.

• repeated int64 int64_val = 10

  DT_INT64

• repeated bool bool_val = 11

  DT_BOOL

• repeated double dcomplex_val = 12

  DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real and imaginary parts of i-th double precision complex.

• repeated ResourceHandleProto resource_handle_val = 14

  DT_RESOURCE

• repeated VariantTensorDataProto variant_val = 15

  DT_VARIANT

• repeated uint32 uint32_val = 16

  DT_UINT32

• repeated uint64 uint64_val = 17

  DT_UINT64

Dimensions of a tensor.

Used in: AttrValue, AttrValue.ListValue, CostGraphDef.Node.OutputInfo, RemoteFusedGraphExecuteInfo.TensorShapeTypeProto, ResourceHandleProto.DtypeAndShape, TensorDescription, TensorProto

• repeated TensorShapeProto.Dim dim = 2

  Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. If an entry has size -1, this corresponds to a dimension of unknown size. The names are optional. The order of entries in "dim" matters: It indicates the layout of the values in the tensor in-memory representation. The first entry in "dim" is the outermost dimension used to layout the values, the last entry is the innermost dimension. This matches the in-memory layout of RowMajor Eigen tensors. If "dim.size()" > 0, "unknown_rank" must be false.

• bool unknown_rank = 3

  If true, the number of dimensions in the shape is unknown. If true, "dim.size()" must be 0.

One dimension of the tensor.

Used in: TensorShapeProto

• int64 size = 1

  Size of the tensor in that dimension. This value must be >= -1, but values of -1 are reserved for "unknown" shapes (values of -1 mean "unknown" dimension). Certain wrappers that work with TensorShapeProto may fail at runtime when deserializing a TensorShapeProto containing a dim value of -1.

• string name = 2

  Optional name of the tensor dimension.

Can only be interpreted if you know the corresponding TensorShape.

• repeated TensorSliceProto.Extent extent = 1

  Extent of the slice in all tensor dimensions. Must have one entry for each of the dimension of the tensor that this slice belongs to. The order of sizes is the same as the order of dimensions in the TensorShape.

Extent of the slice in one dimension.

Either both or no attributes must be set. When no attribute is set means: All data in that dimension.

Used in: TensorSliceProto

• int64 start = 1

  Start index of the slice, starting at 0.

• oneof has_length

  Length of the slice: if the length is missing or -1 we will interpret this as "everything in this dimension". We use "oneof" to preserve information about whether the length is present without changing the serialization format from the prior proto2 version of this proto.

  • int64 length = 2

Indicates how a distributed variable will be aggregated.

Used in: VariableDef

• VARIABLE_AGGREGATION_NONE = 0

  `NONE`: This is the default, giving an error if you use a variable-update operation with multiple replicas.

• VARIABLE_AGGREGATION_SUM = 1

  `SUM`: Add the updates across replicas.

• VARIABLE_AGGREGATION_MEAN = 2

  `MEAN`: Take the arithmetic mean ("average") of the updates across replicas.

• VARIABLE_AGGREGATION_ONLY_FIRST_REPLICA = 3

  `ONLY_FIRST_REPLICA`: This is for when every replica is performing the same update, but we only want to perform the update once. Used, e.g., for the global step counter.

Protocol buffer representing a Variable.

• string variable_name = 1

  Name of the variable tensor.

• string initial_value_name = 6

  Name of the tensor holding the variable's initial value.

• string initializer_name = 2

  Name of the initializer op.

• string snapshot_name = 3

  Name of the snapshot tensor.

• optional SaveSliceInfoDef save_slice_info_def = 4

  Support for saving variables as slices of a larger variable.

• bool is_resource = 5

  Whether to represent this as a ResourceVariable.

• bool trainable = 7

  Whether this variable should be trained.

• VariableSynchronization synchronization = 8

  Indicates when a distributed variable will be synced.

• VariableAggregation aggregation = 9

  Indicates how a distributed variable will be aggregated.

Indicates when a distributed variable will be synced.

Used in: VariableDef

• VARIABLE_SYNCHRONIZATION_AUTO = 0

  `AUTO`: Indicates that the synchronization will be determined by the current `DistributionStrategy` (eg. With `MirroredStrategy` this would be `ON_WRITE`).

• VARIABLE_SYNCHRONIZATION_NONE = 1

  `NONE`: Indicates that there will only be one copy of the variable, so there is no need to sync.

• VARIABLE_SYNCHRONIZATION_ON_WRITE = 2

  `ON_WRITE`: Indicates that the variable will be updated across devices every time it is written.

• VARIABLE_SYNCHRONIZATION_ON_READ = 3

  `ON_READ`: Indicates that the variable will be aggregated across devices when it is read (eg. when checkpointing or when evaluating an op that uses the variable).

Protocol buffer representing the serialization format of DT_VARIANT tensors.

Used in: TensorProto

• string type_name = 1

  Name of the type of objects being serialized.

• bytes metadata = 2

  Portions of the object that are not Tensors.

• repeated TensorProto tensors = 3

  Tensors contained within objects being serialized.

Version information for a piece of serialized data There are different types of versions for each type of data (GraphDef, etc.), but they all have the same common shape described here. Each consumer has "consumer" and "min_producer" versions (specified elsewhere). A consumer is allowed to consume this data if producer >= min_producer consumer >= min_consumer consumer not in bad_consumers

Used in: GraphDef

• int32 producer = 1

  The version of the code that produced this data.

• int32 min_consumer = 2

  Any consumer below this version is not allowed to consume this data.

• repeated int32 bad_consumers = 3

  Specific consumer versions which are disallowed (e.g. due to bugs).