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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, NameAttrList, NodeDef, OpDef.AttrDef, OpInfo

• 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

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

Used in: AttrValue, AttrValue.ListValue, CostGraphDef.Node.OutputInfo, OpDef.ArgDef, OpInfo.TensorProperties, ResourceHandleProto.DtypeAndShape, 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

Used in: NamedDevice, OpInfo

• string type = 1

  Device type (CPU, GPU, ...)

• string vendor = 2

  Vendor (Intel, nvidia, ...)

• string model = 3

  Model (Haswell, K40, ...)

• int64 frequency = 4

  Core Frequency in Mhz

• int64 num_cores = 5

  Number of cores

• map<string, string> environment = 6

  Version of the tools and libraries used with this device (e.g. gcc 4.9, cudnn 5.1)

• int64 num_registers = 7

  Number of registers per core.

• int64 l1_cache_size = 8

  L1 cache size in bytes

• int64 l2_cache_size = 9

  L2 cache size in bytes

• int64 l3_cache_size = 10

  L3 cache size in bytes

• int64 shared_memory_size_per_multiprocessor = 11

  Shared memory size per multiprocessor in bytes. This field is applicable to GPUs only.

• int64 memory_size = 12

  Memory size in bytes

• int64 bandwidth = 13

  Memory bandwidth in KB/s

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

• 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

  "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: OpPerformance

• double mu = 1

• double sigma = 2

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

• string name = 1

• optional DeviceProperties properties = 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.

Used in: OpPerformance

• double mu = 1

• double sigma = 2

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.

• repeated ResourceHandleProto.DtypeAndShape handle_data = 7

  The handle data for resource inputs.

• 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.

Description of an operation as well as the parameters expected to impact its performance.

Used in: OpPerformance

• string op = 1

  The operation name. There may be custom parameters in attrs.

• map<string, AttrValue> attr = 2

  Custom parameters impacting the behavior of the op.

• repeated OpInfo.TensorProperties inputs = 3

• repeated OpInfo.TensorProperties outputs = 5

  Optional description of the op outputs

• optional DeviceProperties device = 4

  Device on which the operation is run.

• optional SessionInfo session_info = 6

  Information about the session configs.

Input data types, shapes and values if known.

Used in: OpInfo

• DataType dtype = 1

• optional TensorShapeProto shape = 2

• optional TensorProto value = 3

A collection of OpDefs

• repeated OpDef op = 1

Performance data for tensorflow operations

Used in: OpPerformanceList

• optional OpInfo op = 1

  The op

• optional SessionInfo session_info = 12

  Information about the session configs.

• string node = 5

  The node name (optional). Makes it easier to associate the performance data with a specific graph node.

• int64 temporary_memory_size = 2

  Temporary memory used by this node (in bytes).

• int64 compute_cost = 3

  Time it takes to run the op (in nanoseconds).

• int64 compute_time = 6

  Analytical compute cost (in nanoseconds).

• int64 memory_time = 7

  Analytical memory access cost (in nanoseconds).

• double compute_efficiency = 4

  Percentage of theoretical compute performance.

• double memory_efficiency = 8

  Percentage of theoretical memory performance.

• oneof execution_time

  Expected execution time, modeled using one of 2 possible distributions.

  • NormalDistribution execution_time_normal = 10

  • LogNormalDistribution execution_time_log_normal = 11

• optional OpPerformance.OpMemory op_memory = 9

Memory usage data for a tensorflow operation.

Used in: OpPerformance

• repeated int64 output_memory = 1

  The output information may have memory usage and output shapes.

• int64 temp_memory = 2

  Temp and persistent memory allocated by this node.

• int64 persistent_memory = 4

• int64 device_temp_memory = 3

• int64 device_persistent_memory = 5

A collection of OpPerformance data points.

• repeated OpPerformance op_performance = 1

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: OpDef.ArgDef, ResourceHandleProto

• DataType dtype = 1

• optional TensorShapeProto shape = 2

Description of the session when an op is run.

Used in: OpInfo, OpPerformance

• int64 intra_op_parallelism = 1

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.

• ST_OPTIONAL = 2

  "tensorflow::data::Optional" in the variant type registry.

Protocol buffer representing a tensor.

Used in: AttrValue, AttrValue.ListValue, OpInfo.TensorProperties, 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, OpInfo.TensorProperties, ResourceHandleProto.DtypeAndShape, 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.

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).