https://github.com/Microsoft/CNTK
Tip revision: 476a60cc2c353d657f61923e92c2806a680c412c authored by Bowen Bao on 02 July 2018, 17:47:37 UTC
small tweak in seq conv to avoid additional gpu memory allocation and increase performance.
small tweak in seq conv to avoid additional gpu memory allocation and increase performance.
Tip revision: 476a60c
tensorboard.proto
syntax = "proto3";
package tensorflow;
option cc_enable_arenas = true;
// LINT.IfChange
enum DataType {
// Not a legal value for DataType. Used to indicate a DataType field
// has not been set.
DT_INVALID = 0;
// Data types that all computation devices are expected to be
// capable to support.
DT_FLOAT = 1;
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;
// TODO(josh11b): DT_GENERIC_PROTO = ??;
// TODO(jeff,josh11b): DT_UINT64? DT_UINT32?
// Do not use! These are only for parameters. Every enum above
// should have a corresponding value below (verified by types_test).
DT_FLOAT_REF = 101;
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;
}
// LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.h,https://www.tensorflow.org/code/tensorflow/go/tensor.go)
// Protocol buffer representing the shape of tensors.
// Dimensions of a tensor.
message TensorShapeProto {
// One dimension of the tensor.
message Dim {
// 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.
int64 size = 1;
// Optional name of the tensor dimension.
string name = 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.
repeated Dim dim = 2;
// If true, the number of dimensions in the shape is unknown.
//
// If true, "dim.size()" must be 0.
bool unknown_rank = 3;
};
// Protocol buffer representing a tensor.
message TensorProto {
DataType dtype = 1;
// Shape of the tensor. TODO(touts): sort out the 0-rank issues.
TensorShapeProto tensor_shape = 2;
// Only one of the representations below is set, one of "tensor_contents" and
// the "xxx_val" attributes. We are not using oneof because as oneofs cannot
// contain repeated fields it would require another extra set of messages.
// 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.
int32 version_number = 3;
// Serialized content from Tensor::AsProtoTensorContent(). This representation
// can be used for all tensor types.
bytes tensor_content = 4;
// Type specific representations that make it easy to create tensor protos in
// all languages. Only the representation corresponding to "dtype" can
// be set. The values hold the flattened representation of the tensor in
// row major order.
// DT_HALF. Note that since protobuf has no int16 type, we'll have some
// pointless zero padding for each value here.
repeated int32 half_val = 13 [packed = true];
// DT_FLOAT.
repeated float float_val = 5 [packed = true];
// DT_DOUBLE.
repeated double double_val = 6 [packed = true];
// DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
repeated int32 int_val = 7 [packed = true];
// DT_STRING
repeated bytes string_val = 8;
// DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
// and imaginary parts of i-th single precision complex.
repeated float scomplex_val = 9 [packed = true];
// DT_INT64
repeated int64 int64_val = 10 [packed = true];
// DT_BOOL
repeated bool bool_val = 11 [packed = true];
// DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
// and imaginary parts of i-th double precision complex.
repeated double dcomplex_val = 12 [packed = true];
};
// 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
//
message VersionDef {
// The version of the code that produced this data.
int32 producer = 1;
// Any consumer below this version is not allowed to consume this data.
int32 min_consumer = 2;
// Specific consumer versions which are disallowed (e.g. due to bugs).
repeated int32 bad_consumers = 3;
};
// Metadata associated with a series of Summary data
message SummaryDescription {
// Hint on how plugins should process the data in this series.
// Supported values include "scalar", "histogram", "image", "audio"
string type_hint = 1;
}
// Serialization format for histogram module in
// core/lib/histogram/histogram.h
message HistogramProto {
double min = 1;
double max = 2;
double num = 3;
double sum = 4;
double sum_squares = 5;
// 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_limit = 6 [packed = true];
repeated double bucket = 7 [packed = true];
};
// 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.
message Summary {
message Image {
// Dimensions of the image.
int32 height = 1;
int32 width = 2;
// Valid colorspace values are
// 1 - grayscale
// 2 - grayscale + alpha
// 3 - RGB
// 4 - RGBA
// 5 - DIGITAL_YUV
// 6 - BGRA
int32 colorspace = 3;
// Image data in encoded format. All image formats supported by
// image_codec::CoderUtil can be stored here.
bytes encoded_image_string = 4;
}
message Audio {
// Sample rate of the audio in Hz.
float sample_rate = 1;
// Number of channels of audio.
int64 num_channels = 2;
// Length of the audio in frames (samples per channel).
int64 length_frames = 3;
// Encoded audio data and its associated RFC 2045 content type (e.g.
// "audio/wav").
bytes encoded_audio_string = 4;
string content_type = 5;
}
message Value {
// Name of the node that output this summary; in general, the name of a
// TensorSummary node. If the node in question has multiple outputs, then
// a ":\d+" suffix will be appended, like "some_op:13".
// Might not be set for legacy summaries (i.e. those not using the tensor
// value field)
string node_name = 7;
// Tag name for the data. Will only be used by legacy summaries
// (ie. those not using the tensor value field)
// For legacy summaries, will be used as the title of the graph
// in the visualizer.
//
// Tag is usually "op_name:value_name", where "op_name" itself can have
// structure to indicate grouping.
string tag = 1;
// Value associated with the tag.
oneof value {
float simple_value = 2;
bytes obsolete_old_style_histogram = 3;
Image image = 4;
HistogramProto histo = 5;
Audio audio = 6;
TensorProto tensor = 8;
}
}
// Set of values for the summary.
repeated Value value = 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.
message AttrValue {
// LINT.IfChange
message ListValue {
repeated bytes s = 2; // "list(string)"
repeated int64 i = 3 [packed = true]; // "list(int)"
repeated float f = 4 [packed = true]; // "list(float)"
repeated bool b = 5 [packed = true]; // "list(bool)"
repeated DataType type = 6 [packed = true]; // "list(type)"
repeated TensorShapeProto shape = 7; // "list(shape)"
repeated TensorProto tensor = 8; // "list(tensor)"
// TODO(zhifengc/josh11b): implements list(func) if needed.
}
// LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
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"
ListValue list = 1; // any "list(...)"
// "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.
NameAttrList func = 10;
// 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".
string placeholder = 9;
}
}
// A list of attr names and their values. The whole list is attached
// with a string name. E.g., MatMul[T=float].
message NameAttrList {
string name = 1;
map<string, AttrValue> attr = 2;
}
message NodeDef {
// 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 name = 1;
// The operation name. There may be custom parameters in attrs.
// Op names starting with an underscore are reserved for internal use.
string op = 2;
// 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".
repeated string input = 3;
// 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 ::= COLOCATED_NODE | PARTIAL_SPEC
//
// COLOCATED_NODE ::= "@" NODE_NAME // See NodeDef.name above.
// PARTIAL_SPEC ::= ("/" CONSTRAINT) *
// CONSTRAINT ::= ("job:" JOB_NAME)
// | ("replica:" [1-9][0-9]*)
// | ("task:" [1-9][0-9]*)
// | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
//
// Valid values for this string include:
// * "@other/node" (colocate with "other/node")
// * "/job:worker/replica:0/task:1/gpu:3" (full specification)
// * "/job:worker/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.
string device = 4;
// 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.
map<string, AttrValue> attr = 5;
};
// Represents the graph of operations
message GraphDef {
repeated NodeDef node = 1;
// 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.
VersionDef versions = 4;
// Deprecated single version field; use versions above instead. Since all
// GraphDef changes before "versions" was introduced were forward
// compatible, this field is entirely ignored.
int32 version = 3 [deprecated = true];
};
// Protocol buffer representing an event that happened during
// the execution of a Brain model.
message Event {
// Timestamp of the event.
double wall_time = 1;
// Global step of the event.
int64 step = 2;
oneof what {
// An event file was started, with the specified version.
// This is use to identify the contents of the record IO files
// easily. Current version is "brain.Event:2". All versions
// start with "brain.Event:".
string file_version = 3;
// An encoded version of a GraphDef.
bytes graph_def = 4;
// A summary was generated.
Summary summary = 5;
// The user output a log message. Not all messages are logged, only ones
// generated via the Python tensorboard_logging module.
LogMessage log_message = 6;
// The state of the session which can be used for restarting after crashes.
SessionLog session_log = 7;
// The metadata returned by running a session.run() call.
TaggedRunMetadata tagged_run_metadata = 8;
// An encoded version of a MetaGraphDef.
bytes meta_graph_def = 9;
}
}
// Protocol buffer used for logging messages to the events file.
message LogMessage {
enum Level {
UNKNOWN = 0;
DEBUG = 10;
INFO = 20;
WARN = 30;
ERROR = 40;
FATAL = 50;
}
Level level = 1;
string message = 2;
}
// Protocol buffer used for logging session state.
message SessionLog {
enum SessionStatus {
STATUS_UNSPECIFIED = 0;
START = 1;
STOP = 2;
CHECKPOINT = 3;
}
SessionStatus status = 1;
// This checkpoint_path contains both the path and filename.
string checkpoint_path = 2;
string msg = 3;
}
// For logging the metadata output for a single session.run() call.
message TaggedRunMetadata {
// Tag name associated with this metadata.
string tag = 1;
// Byte-encoded version of the `RunMetadata` proto in order to allow lazy
// deserialization.
bytes run_metadata = 2;
}