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syntax = "proto3";
package tensorflow.tfprof;
import "tensorflow/core/framework/attr_value.proto";
import "tensorflow/core/framework/step_stats.proto";
// It specifies the Python callstack that creates an op.
message CodeDef {
repeated Trace traces = 1;
message Trace {
string file = 1 [deprecated = true]; // deprecated by file_id.
int64 file_id = 6;
int32 lineno = 2;
string function = 3 [deprecated = true]; // deprecated by function_id.
int64 function_id = 7;
string line = 4 [deprecated = true]; // deprecated line_id.
int64 line_id = 8;
int32 func_start_line = 5;
}
}
message OpLogEntry {
// op name.
string name = 1;
// float_ops is filled by tfprof Python API when called. It requires the
// op has RegisterStatistics defined. Currently, Conv2D, MatMul, etc, are
// implemented.
int64 float_ops = 2;
// User can define extra op type information for an op. This allows the user
// to select a group of ops precisely using op_type as a key.
repeated string types = 3;
// Used to support tfprof "code" view.
CodeDef code_def = 4;
}
message OpLogProto {
repeated OpLogEntry log_entries = 1;
// Maps from id of CodeDef file,function,line to its string
// In the future can also map other id of other fields to string.
map<int64, string> id_to_string = 2;
}
// A proto representation of the profiler's profile.
// It allows serialization, shipping around and deserialization of the profiles.
//
// Please don't depend on the internals of the profile proto.
message ProfileProto {
map<int64, ProfileNode> nodes = 1;
// Whether or not has code traces.
bool has_trace = 2;
// Whether or not the TF device tracer fails to return accelerator
// information (which could lead to 0 accelerator execution time).
bool miss_accelerator_stream = 5;
// Traced steps.
repeated int64 steps = 3;
// Maps from id of CodeDef file,function,line to its string
// In the future can also map other id of other fields to string.
map<int64, string> id_to_string = 4;
}
message ProfileNode {
// graph node name.
string name = 1;
// graph operation type.
string op = 9;
// A unique id for the node.
int64 id = 13;
map<int32, int64> inputs = 2;
map<int32, Tuple> input_shapes = 16;
map<int32, int64> outputs = 3;
map<int32, Tuple> output_shapes = 15;
// A map from source node id to its output index to current node.
map<int64, int32> src_output_index = 14;
repeated int64 shape = 4;
repeated string op_types = 5;
string canonical_device = 6;
string host_device = 7;
int64 float_ops = 8;
CodeDef trace = 10;
map<string, AttrValue> attrs = 11;
map<int64, ExecProfile> execs = 12;
}
message ExecProfile {
// Can be larger than 1 if run multiple times in loop.
int64 run_count = 1;
// The earliest/latest time including scheduling and execution.
int64 all_start_micros = 2;
int64 latest_end_micros = 3;
// device -> vector of {op_start_micros, op_exec_micros} pairs.
// accelerator_execs: gpu:id/stream:all -> {op_start_micros, op_exec_micros}
// For accelerator, vector size can be larger than 1, multiple kernel fires
// or in tf.while_loop.
map<string, ExecTime> accelerator_execs = 4;
// cpu_execs: cpu/gpu:id -> {op_start_micros, op_exec_micros}
// For cpu, vector size can be larger than 1 if in tf.while_loop.
map<string, ExecTime> cpu_execs = 5;
// Each entry to memory information of a scheduling of the node.
// Normally, there will be multiple entries in while_loop.
repeated ExecMemory memory_execs = 7;
// The allocation and deallocation times and sizes throughout execution.
repeated AllocationRecord allocations = 11;
// The devices related to this execution.
repeated string devices = 6;
}
message ExecTime {
repeated Tuple times = 1;
}
message ExecMemory {
// This is the timestamp when the memory information was tracked.
int64 memory_micros = 1;
// NOTE: Please don't depend on the following 4 fields yet. Due to
// TensorFlow internal tracing issues, the numbers can be quite wrong.
// TODO(xpan): Fix the TensorFlow internal tracing.
int64 host_temp_bytes = 2;
int64 host_persistent_bytes = 3;
int64 accelerator_temp_bytes = 4;
int64 accelerator_persistent_bytes = 5;
// Total bytes requested by the op.
int64 requested_bytes = 6;
// Total bytes requested by the op and released before op end.
int64 peak_bytes = 7;
// Total bytes requested by the op and not released after op end.
int64 residual_bytes = 8;
// Total bytes output by the op (not necessarily requested by the op).
int64 output_bytes = 9;
// The total number of bytes currently allocated by the allocator if >0.
int64 allocator_bytes_in_use = 10;
// The memory of each output of the operation.
map<int32, Memory> output_memory = 11;
}
message Tuple {
repeated int64 int64_values = 1;
}
message Memory {
int64 bytes = 1;
uint64 ptr = 2;
}
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