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syntax = "proto3";
import "tensorflow/core/framework/tensor_shape.proto";
import "tensorflow/core/framework/types.proto";
package tensorflow.tfprof;
message TFProfTensorProto {
DataType dtype = 1;
// Flatten tensor in row-major.
// Only one of the following array is set.
repeated double value_double = 2;
repeated int64 value_int64 = 3;
repeated string value_str = 4;
}
// A node in TensorFlow graph. Used by scope/graph view.
message TFGraphNodeProto {
// op name.
string name = 1;
// tensor value restored from checkpoint.
TFProfTensorProto tensor_value = 15;
// op execution time.
// A node can be defined once but run multiple times in tf.while_loop.
// the times sum up all different runs.
int64 run_count = 21;
int64 exec_micros = 2;
int64 accelerator_exec_micros = 17;
int64 cpu_exec_micros = 18;
// Total requested bytes by the op.
int64 requested_bytes = 3;
// Number of parameters if available.
int64 parameters = 4;
// Number of float operations.
int64 float_ops = 13;
// Device the op is assigned to.
// Since an op can fire multiple kernel calls, there can be multiple devices.
repeated string devices = 10;
// The following are the aggregated stats from all *accounted* children and
// the node itself. The actual children depend on the data structure used.
// In graph view, children are inputs recursively.
// In scope view, children are nodes under the name scope.
int64 total_definition_count = 23;
int64 total_run_count = 22;
int64 total_exec_micros = 6;
int64 total_accelerator_exec_micros = 19;
int64 total_cpu_exec_micros = 20;
int64 total_requested_bytes = 7;
int64 total_parameters = 8;
int64 total_float_ops = 14;
// shape information, if available.
// TODO(xpan): Why is this repeated?
repeated TensorShapeProto shapes = 11;
map<int32, TensorShapeProto> input_shapes = 16;
// Descendants of the graph. The actual descendants depend on the data
// structure used (scope, graph).
repeated TFGraphNodeProto children = 12;
}
// A node that groups multiple TFGraphNodeProto.
// Depending on the 'view', the semantics of the TFmultiGraphNodeProto
// is different:
// code view: A node groups all TensorFlow graph nodes created by the
// Python code.
// op view: A node groups all TensorFlow graph nodes that are of type
// of the op (e.g. MatMul, Conv2D).
message TFMultiGraphNodeProto {
// Name of the node.
string name = 1;
// code execution time.
int64 exec_micros = 2;
int64 accelerator_exec_micros = 12;
int64 cpu_exec_micros = 13;
// Total requested bytes by the code.
int64 requested_bytes = 3;
// Number of parameters if available.
int64 parameters = 4;
// Number of float operations.
int64 float_ops = 5;
// The following are the aggregated stats from descendants.
// The actual descendants depend on the data structure used.
int64 total_exec_micros = 6;
int64 total_accelerator_exec_micros = 14;
int64 total_cpu_exec_micros = 15;
int64 total_requested_bytes = 7;
int64 total_parameters = 8;
int64 total_float_ops = 9;
// TensorFlow graph nodes contained by the TFMultiGraphNodeProto.
repeated TFGraphNodeProto graph_nodes = 10;
// Descendants of the node. The actual descendants depend on the data
// structure used.
repeated TFMultiGraphNodeProto children = 11;
}
message AdviceProto {
// checker name -> a list of reports from the checker.
map<string, Checker> checkers = 1;
message Checker {
repeated string reports = 2;
}
}
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