improvements from external prs

-fix corner case where dr converter failed when initializing

Closes twitter/the-algorithm#550

navi/dr_transform/src/all_config.rs

@@ -44,5 +44,6 @@ pub struct RenamedFeatures {
 }
 
 pub fn parse(json_str: &str) -> Result<AllConfig, Error> {
-    serde_json::from_str(json_str)
+    let all_config: AllConfig = serde_json::from_str(json_str)?;
+    Ok(all_config)
 }

navi/dr_transform/src/converter.rs

@@ -2,6 +2,9 @@ use std::collections::BTreeSet;
 use std::fmt::{self, Debug, Display};
 use std::fs;
 
+use crate::all_config;
+use crate::all_config::AllConfig;
+use anyhow::{bail, Context};
 use bpr_thrift::data::DataRecord;
 use bpr_thrift::prediction_service::BatchPredictionRequest;
 use bpr_thrift::tensor::GeneralTensor;
@@ -16,8 +19,6 @@ use segdense::util;
 use thrift::protocol::{TBinaryInputProtocol, TSerializable};
 use thrift::transport::TBufferChannel;
 
-use crate::{all_config, all_config::AllConfig};
-
 pub fn log_feature_match(
     dr: &DataRecord,
     seg_dense_config: &DensificationTransformSpec,
@@ -28,20 +29,24 @@ pub fn log_feature_match(
 
     for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap() {
         debug!(
-            "{dr_type} - Continuous Datarecord => Feature ID: {feature_id}, Feature value: {feature_value}"
+            "{} - Continous Datarecord => Feature ID: {}, Feature value: {}",
+            dr_type, feature_id, feature_value
         );
         for input_feature in &seg_dense_config.cont.input_features {
             if input_feature.feature_id == *feature_id {
-                debug!("Matching input feature: {input_feature:?}")
+                debug!("Matching input feature: {:?}", input_feature)
             }
         }
     }
 
     for feature_id in dr.binary_features.as_ref().unwrap() {
-        debug!("{dr_type} - Binary Datarecord => Feature ID: {feature_id}");
+        debug!(
+            "{} - Binary Datarecord => Feature ID: {}",
+            dr_type, feature_id
+        );
         for input_feature in &seg_dense_config.binary.input_features {
             if input_feature.feature_id == *feature_id {
-                debug!("Found input feature: {input_feature:?}")
+                debug!("Found input feature: {:?}", input_feature)
             }
         }
     }
@@ -90,18 +95,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
         model_version: &str,
         reporting_feature_ids: Vec<(i64, &str)>,
         register_metric_fn: Option<impl Fn(&HistogramVec)>,
-    ) -> BatchPredictionRequestToTorchTensorConverter {
+    ) -> anyhow::Result<BatchPredictionRequestToTorchTensorConverter> {
-        let all_config_path = format!("{model_dir}/{model_version}/all_config.json");
+        let all_config_path = format!("{}/{}/all_config.json", model_dir, model_version);
-        let seg_dense_config_path =
+        let seg_dense_config_path = format!(
-            format!("{model_dir}/{model_version}/segdense_transform_spec_home_recap_2022.json");
+            "{}/{}/segdense_transform_spec_home_recap_2022.json",
-        let seg_dense_config = util::load_config(&seg_dense_config_path);
+            model_dir, model_version
+        );
+        let seg_dense_config = util::load_config(&seg_dense_config_path)?;
         let all_config = all_config::parse(
             &fs::read_to_string(&all_config_path)
-                .unwrap_or_else(|error| panic!("error loading all_config.json - {error}")),
+                .with_context(|| "error loading all_config.json - ")?,
-        )
+        )?;
-        .unwrap();
 
-        let feature_mapper = util::load_from_parsed_config_ref(&seg_dense_config);
+        let feature_mapper = util::load_from_parsed_config(seg_dense_config.clone())?;
 
         let user_embedding_feature_id = Self::get_feature_id(
             &all_config
@@ -131,11 +137,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
         let (discrete_feature_metrics, continuous_feature_metrics) = METRICS.get_or_init(|| {
             let discrete = HistogramVec::new(
                 HistogramOpts::new(":navi:feature_id:discrete", "Discrete Feature ID values")
-                    .buckets(Vec::from([
+                    .buckets(Vec::from(&[
-                        0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
+                        0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
                         120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0,
                         300.0, 500.0, 1000.0, 10000.0, 100000.0,
-                    ])),
+                    ] as &'static [f64])),
                 &["feature_id"],
             )
             .expect("metric cannot be created");
@@ -144,18 +150,18 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     ":navi:feature_id:continuous",
                     "continuous Feature ID values",
                 )
-                .buckets(Vec::from([
+                .buckets(Vec::from(&[
-                    0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
+                    0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0,
-                    120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0,
+                    130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0, 500.0,
-                    500.0, 1000.0, 10000.0, 100000.0,
+                    1000.0, 10000.0, 100000.0,
-                ])),
+                ] as &'static [f64])),
                 &["feature_id"],
             )
             .expect("metric cannot be created");
-            if let Some(r) = register_metric_fn {
+            register_metric_fn.map(|r| {
                 r(&discrete);
                 r(&continuous);
-            }
+            });
             (discrete, continuous)
         });
 
@@ -164,13 +170,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
         for (feature_id, feature_type) in reporting_feature_ids.iter() {
             match *feature_type {
-                "discrete" => discrete_features_to_report.insert(*feature_id),
+                "discrete" => discrete_features_to_report.insert(feature_id.clone()),
-                "continuous" => continuous_features_to_report.insert(*feature_id),
+                "continuous" => continuous_features_to_report.insert(feature_id.clone()),
-                _ => panic!("Invalid feature type {feature_type} for reporting metrics!"),
+                _ => bail!(
+                    "Invalid feature type {} for reporting metrics!",
+                    feature_type
+                ),
             };
         }
 
-        BatchPredictionRequestToTorchTensorConverter {
+        Ok(BatchPredictionRequestToTorchTensorConverter {
             all_config,
             seg_dense_config,
             all_config_path,
@@ -183,7 +192,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
             continuous_features_to_report,
             discrete_feature_metrics,
             continuous_feature_metrics,
-        }
+        })
     }
 
     fn get_feature_id(feature_name: &str, seg_dense_config: &Root) -> i64 {
@@ -218,9 +227,9 @@ impl BatchPredictionRequestToTorchTensorConverter {
         let mut working_set = vec![0 as f32; total_size];
         let mut bpr_start = 0;
         for (bpr, &bpr_end) in bprs.iter().zip(batch_size) {
-            if bpr.common_features.is_some()
+            if bpr.common_features.is_some() {
-                && bpr.common_features.as_ref().unwrap().tensors.is_some()
+                if bpr.common_features.as_ref().unwrap().tensors.is_some() {
-                && bpr
+                    if bpr
                         .common_features
                         .as_ref()
                         .unwrap()
@@ -258,6 +267,8 @@ impl BatchPredictionRequestToTorchTensorConverter {
                             }
                         }
                     }
+                }
+            }
             // find the feature in individual feature list and add to corresponding batch.
             for (index, datarecord) in bpr.individual_features_list.iter().enumerate() {
                 if datarecord.tensors.is_some()
@@ -298,9 +309,9 @@ impl BatchPredictionRequestToTorchTensorConverter {
     //           (INT64 --> INT64, DataRecord.discrete_feature)
     fn get_continuous(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
         // These need to be part of model schema
-        let rows = batch_ends[batch_ends.len() - 1];
+        let rows: usize = batch_ends[batch_ends.len() - 1];
-        let cols = 5293;
+        let cols: usize = 5293;
-        let full_size = rows * cols;
+        let full_size: usize = rows * cols;
         let default_val = f32::NAN;
 
         let mut tensor = vec![default_val; full_size];
@@ -325,16 +336,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     .unwrap();
 
                 for feature in common_features {
-                    if let Some(f_info) = self.feature_mapper.get(feature.0) {
+                    match self.feature_mapper.get(feature.0) {
+                        Some(f_info) => {
                             let idx = f_info.index_within_tensor as usize;
                             if idx < cols {
                                 // Set value in each row
                                 for r in bpr_start..bpr_end {
-                                let flat_index = r * cols + idx;
+                                    let flat_index: usize = r * cols + idx;
                                     tensor[flat_index] = feature.1.into_inner() as f32;
                                 }
                             }
                         }
+                        None => (),
+                    }
                     if self.continuous_features_to_report.contains(feature.0) {
                         self.continuous_feature_metrics
                             .with_label_values(&[feature.0.to_string().as_str()])
@@ -349,24 +363,28 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
             // Process the batch of datarecords
             for r in bpr_start..bpr_end {
-                let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
+                let dr: &DataRecord =
+                    &bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
                 if dr.continuous_features.is_some() {
                     for feature in dr.continuous_features.as_ref().unwrap() {
-                        if let Some(f_info) = self.feature_mapper.get(feature.0) {
+                        match self.feature_mapper.get(&feature.0) {
+                            Some(f_info) => {
                                 let idx = f_info.index_within_tensor as usize;
-                            let flat_index = r * cols + idx;
+                                let flat_index: usize = r * cols + idx;
                                 if flat_index < tensor.len() && idx < cols {
                                     tensor[flat_index] = feature.1.into_inner() as f32;
                                 }
                             }
+                            None => (),
+                        }
                         if self.continuous_features_to_report.contains(feature.0) {
                             self.continuous_feature_metrics
                                 .with_label_values(&[feature.0.to_string().as_str()])
-                                .observe(feature.1.into_inner())
+                                .observe(feature.1.into_inner() as f64)
                         } else if self.discrete_features_to_report.contains(feature.0) {
                             self.discrete_feature_metrics
                                 .with_label_values(&[feature.0.to_string().as_str()])
-                                .observe(feature.1.into_inner())
+                                .observe(feature.1.into_inner() as f64)
                         }
                     }
                 }
@@ -383,10 +401,10 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
     fn get_binary(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
         // These need to be part of model schema
-        let rows = batch_ends[batch_ends.len() - 1];
+        let rows: usize = batch_ends[batch_ends.len() - 1];
-        let cols = 149;
+        let cols: usize = 149;
-        let full_size = rows * cols;
+        let full_size: usize = rows * cols;
-        let default_val = 0;
+        let default_val: i64 = 0;
 
         let mut v = vec![default_val; full_size];
 
@@ -410,16 +428,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     .unwrap();
 
                 for feature in common_features {
-                    if let Some(f_info) = self.feature_mapper.get(feature) {
+                    match self.feature_mapper.get(feature) {
+                        Some(f_info) => {
                             let idx = f_info.index_within_tensor as usize;
                             if idx < cols {
                                 // Set value in each row
                                 for r in bpr_start..bpr_end {
-                                let flat_index = r * cols + idx;
+                                    let flat_index: usize = r * cols + idx;
                                     v[flat_index] = 1;
                                 }
                             }
                         }
+                        None => (),
+                    }
                 }
             }
 
@@ -428,11 +449,14 @@ impl BatchPredictionRequestToTorchTensorConverter {
                 let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
                 if dr.binary_features.is_some() {
                     for feature in dr.binary_features.as_ref().unwrap() {
-                        if let Some(f_info) = self.feature_mapper.get(feature) {
+                        match self.feature_mapper.get(&feature) {
+                            Some(f_info) => {
                                 let idx = f_info.index_within_tensor as usize;
-                            let flat_index = r * cols + idx;
+                                let flat_index: usize = r * cols + idx;
                                 v[flat_index] = 1;
                             }
+                            None => (),
+                        }
                     }
                 }
             }
@@ -448,10 +472,10 @@ impl BatchPredictionRequestToTorchTensorConverter {
     #[allow(dead_code)]
     fn get_discrete(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
         // These need to be part of model schema
-        let rows = batch_ends[batch_ends.len() - 1];
+        let rows: usize = batch_ends[batch_ends.len() - 1];
-        let cols = 320;
+        let cols: usize = 320;
-        let full_size = rows * cols;
+        let full_size: usize = rows * cols;
-        let default_val = 0;
+        let default_val: i64 = 0;
 
         let mut v = vec![default_val; full_size];
 
@@ -475,16 +499,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     .unwrap();
 
                 for feature in common_features {
-                    if let Some(f_info) = self.feature_mapper.get(feature.0) {
+                    match self.feature_mapper.get(feature.0) {
+                        Some(f_info) => {
                             let idx = f_info.index_within_tensor as usize;
                             if idx < cols {
                                 // Set value in each row
                                 for r in bpr_start..bpr_end {
-                                let flat_index = r * cols + idx;
+                                    let flat_index: usize = r * cols + idx;
                                     v[flat_index] = *feature.1;
                                 }
                             }
                         }
+                        None => (),
+                    }
                     if self.discrete_features_to_report.contains(feature.0) {
                         self.discrete_feature_metrics
                             .with_label_values(&[feature.0.to_string().as_str()])
@@ -495,16 +522,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
             // Process the batch of datarecords
             for r in bpr_start..bpr_end {
-                let dr: &DataRecord = &bpr.individual_features_list[r];
+                let dr: &DataRecord = &bpr.individual_features_list[usize::try_from(r).unwrap()];
                 if dr.discrete_features.is_some() {
                     for feature in dr.discrete_features.as_ref().unwrap() {
-                        if let Some(f_info) = self.feature_mapper.get(feature.0) {
+                        match self.feature_mapper.get(&feature.0) {
+                            Some(f_info) => {
                                 let idx = f_info.index_within_tensor as usize;
-                            let flat_index = r * cols + idx;
+                                let flat_index: usize = r * cols + idx;
                                 if flat_index < v.len() && idx < cols {
                                     v[flat_index] = *feature.1;
                                 }
                             }
+                            None => (),
+                        }
                         if self.discrete_features_to_report.contains(feature.0) {
                             self.discrete_feature_metrics
                                 .with_label_values(&[feature.0.to_string().as_str()])
@@ -569,7 +599,7 @@ impl Converter for BatchPredictionRequestToTorchTensorConverter {
             .map(|bpr| bpr.individual_features_list.len())
             .scan(0usize, |acc, e| {
                 //running total
-                *acc += e;
+                *acc = *acc + e;
                 Some(*acc)
             })
             .collect::<Vec<_>>();

navi/navi/proto/tensorflow/core/framework/full_type.proto

@@ -122,7 +122,7 @@ enum FullTypeId {
   //   TFT_TENSOR[TFT_INT32, TFT_UNKNOWN]
   //     is a Tensor of int32 element type and unknown shape.
   //
-  // TODO: Define TFT_SHAPE and add more examples.
+  // TODO(mdan): Define TFT_SHAPE and add more examples.
   TFT_TENSOR = 1000;
 
   // Array (or tensorflow::TensorList in the variant type registry).
@@ -178,7 +178,7 @@ enum FullTypeId {
   // object (for now).
 
   // The bool element type.
-  // TODO
+  // TODO(mdan): Quantized types, legacy representations (e.g. ref)
   TFT_BOOL = 200;
   // Integer element types.
   TFT_UINT8 = 201;
@@ -195,7 +195,7 @@ enum FullTypeId {
   TFT_DOUBLE = 211;
   TFT_BFLOAT16 = 215;
   // Complex element types.
-  // TODO: Represent as TFT_COMPLEX[TFT_DOUBLE] instead?
+  // TODO(mdan): Represent as TFT_COMPLEX[TFT_DOUBLE] instead?
   TFT_COMPLEX64 = 212;
   TFT_COMPLEX128 = 213;
   // The string element type.
@@ -240,7 +240,7 @@ enum FullTypeId {
   // ownership is in the true sense: "the op argument representing the lock is
   // available".
   // Mutex locks are the dynamic counterpart of control dependencies.
-  // TODO: Properly document this thing.
+  // TODO(mdan): Properly document this thing.
   //
   // Parametrization: TFT_MUTEX_LOCK[].
   TFT_MUTEX_LOCK = 10202;
@@ -271,6 +271,6 @@ message FullTypeDef {
   oneof attr {
     string s = 3;
     int64 i = 4;
-    // TODO: list/tensor, map? Need to reconcile with TFT_RECORD, etc.
+    // TODO(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc.
   }
 }

navi/navi/proto/tensorflow/core/framework/function.proto

@@ -23,7 +23,7 @@ message FunctionDefLibrary {
 // with a value. When a GraphDef has a call to a function, it must
 // have binding for every attr defined in the signature.
 //
-// TODO:
+// TODO(zhifengc):
 //   * device spec, etc.
 message FunctionDef {
   // The definition of the function's name, arguments, return values,

navi/navi/proto/tensorflow/core/framework/node_def.proto

@@ -61,7 +61,7 @@ message NodeDef {
   // 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: Add some examples here showing best practices.
+  // TODO(josh11b): Add some examples here showing best practices.
   map<string, AttrValue> attr = 5;
 
   message ExperimentalDebugInfo {

navi/navi/proto/tensorflow/core/framework/op_def.proto

@@ -96,7 +96,7 @@ message OpDef {
     // Human-readable description.
     string description = 4;
 
-    // TODO: bool is_optional?
+    // TODO(josh11b): bool is_optional?
 
     // --- Constraints ---
     // These constraints are only in effect if specified.  Default is no
@@ -139,7 +139,7 @@ message OpDef {
   // 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: Implement that optimization.
+  // TODO(josh11b): Implement that optimization.
   bool is_aggregate = 16;  // for things like add
 
   // Other optimizations go here, like

navi/navi/proto/tensorflow/core/framework/step_stats.proto

@@ -53,7 +53,7 @@ message MemoryStats {
 
 // Time/size stats recorded for a single execution of a graph node.
 message NodeExecStats {
-  // TODO: Use some more compact form of node identity than
+  // 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.

navi/navi/proto/tensorflow/core/framework/tensor.proto

@@ -16,7 +16,7 @@ option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framewo
 message TensorProto {
   DataType dtype = 1;
 
-  // Shape of the tensor.  TODO: sort out the 0-rank issues.
+  // 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

navi/navi/proto/tensorflow/core/protobuf/config.proto

@@ -532,7 +532,7 @@ message ConfigProto {
 
     // We removed the flag client_handles_error_formatting. Marking the tag
     // number as reserved.
-    // TODO: Should we just remove this tag so that it can be
+    // TODO(shikharagarwal): Should we just remove this tag so that it can be
     // used in future for other purpose?
     reserved 2;
 
@@ -576,7 +576,7 @@ message ConfigProto {
     // - If isolate_session_state is true, session states are isolated.
     // - If isolate_session_state is false, session states are shared.
     //
-    // TODO: Add a single API that consistently treats
+    // TODO(b/129330037): Add a single API that consistently treats
     // isolate_session_state and ClusterSpec propagation.
     bool share_session_state_in_clusterspec_propagation = 8;
 
@@ -704,7 +704,7 @@ message ConfigProto {
 
 // Options for a single Run() call.
 message RunOptions {
-  // TODO Turn this into a TraceOptions proto which allows
+  // TODO(pbar) Turn this into a TraceOptions proto which allows
   // tracing to be controlled in a more orthogonal manner?
   enum TraceLevel {
     NO_TRACE = 0;
@@ -781,7 +781,7 @@ message RunMetadata {
   repeated GraphDef partition_graphs = 3;
 
   message FunctionGraphs {
-    // TODO: Include some sort of function/cache-key identifier?
+    // TODO(nareshmodi): Include some sort of function/cache-key identifier?
     repeated GraphDef partition_graphs = 1;
 
     GraphDef pre_optimization_graph = 2;

navi/navi/proto/tensorflow/core/protobuf/coordination_service.proto

@@ -194,7 +194,7 @@ service CoordinationService {
 
   // Report error to the task. RPC sets the receiving instance of coordination
   // service agent to error state permanently.
-  // TODO: Consider splitting this into a different RPC service.
+  // TODO(b/195990880): Consider splitting this into a different RPC service.
   rpc ReportErrorToAgent(ReportErrorToAgentRequest)
       returns (ReportErrorToAgentResponse);
 

navi/navi/proto/tensorflow/core/protobuf/debug.proto

@@ -46,7 +46,7 @@ message DebugTensorWatch {
   // are to be debugged, the callers of Session::Run() must use distinct
   // debug_urls to make sure that the streamed or dumped events do not overlap
   // among the invocations.
-  // TODO: More visible documentation of this in g3docs.
+  // TODO(cais): More visible documentation of this in g3docs.
   repeated string debug_urls = 4;
 
   // Do not error out if debug op creation fails (e.g., due to dtype

navi/navi/proto/tensorflow/core/protobuf/debug_event.proto

@@ -12,7 +12,7 @@ option java_package = "org.tensorflow.util";
 option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";
 
 // Available modes for extracting debugging information from a Tensor.
-// TODO: Document the detailed column names and semantics in a separate
+// TODO(cais): Document the detailed column names and semantics in a separate
 // markdown file once the implementation settles.
 enum TensorDebugMode {
   UNSPECIFIED = 0;
@@ -223,7 +223,7 @@ message DebuggedDevice {
   // A debugger-generated ID for the device. Guaranteed to be unique within
   // the scope of the debugged TensorFlow program, including single-host and
   // multi-host settings.
-  // TODO: Test the uniqueness guarantee in multi-host settings.
+  // TODO(cais): Test the uniqueness guarantee in multi-host settings.
   int32 device_id = 2;
 }
 
@@ -264,7 +264,7 @@ message Execution {
   // field with the DebuggedDevice messages.
   repeated int32 output_tensor_device_ids = 9;
 
-  // TODO support, add more fields
+  // TODO(cais): When backporting to V1 Session.run() support, add more fields
   // such as fetches and feeds.
 }
 

navi/navi/proto/tensorflow/core/protobuf/distributed_runtime_payloads.proto

@@ -7,7 +7,7 @@ option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobu
 
 // Used to serialize and transmit tensorflow::Status payloads through
 // grpc::Status `error_details` since grpc::Status lacks payload API.
-// TODO: Use GRPC API once supported.
+// TODO(b/204231601): Use GRPC API once supported.
 message GrpcPayloadContainer {
   map<string, bytes> payloads = 1;
 }

navi/navi/proto/tensorflow/core/protobuf/eager_service.proto

@@ -172,7 +172,7 @@ message WaitQueueDoneRequest {
 }
 
 message WaitQueueDoneResponse {
-  // TODO: Consider adding NodeExecStats here to be able to
+  // TODO(nareshmodi): Consider adding NodeExecStats here to be able to
   // propagate some stats.
 }
 

navi/navi/proto/tensorflow/core/protobuf/master.proto

@@ -94,7 +94,7 @@ message ExtendSessionRequest {
 }
 
 message ExtendSessionResponse {
-  // TODO: Return something about the operation?
+  // TODO(mrry): Return something about the operation?
 
   // The new version number for the extended graph, to be used in the next call
   // to ExtendSession.

navi/navi/proto/tensorflow/core/protobuf/saved_object_graph.proto

@@ -176,7 +176,7 @@ message SavedBareConcreteFunction {
   // allows the ConcreteFunction to be called with nest structure inputs. This
   // field may not be populated. If this field is absent, the concrete function
   // can only be called with flat inputs.
-  // TODO: support calling saved ConcreteFunction with structured
+  // TODO(b/169361281): support calling saved ConcreteFunction with structured
   // inputs in C++ SavedModel API.
   FunctionSpec function_spec = 4;
 }

navi/navi/proto/tensorflow/core/protobuf/tensor_bundle.proto

@@ -17,7 +17,7 @@ option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobu
 
 // Special header that is associated with a bundle.
 //
-// TODO: maybe in the future, we can add information about
+// TODO(zongheng,zhifengc): maybe in the future, we can add information about
 // which binary produced this checkpoint, timestamp, etc. Sometime, these can be
 // valuable debugging information. And if needed, these can be used as defensive
 // information ensuring reader (binary version) of the checkpoint and the writer

navi/navi/proto/tensorflow/core/protobuf/worker.proto

@@ -188,7 +188,7 @@ message DeregisterGraphRequest {
 }
 
 message DeregisterGraphResponse {
-  // TODO: Optionally add summary stats for the graph.
+  // TODO(mrry): Optionally add summary stats for the graph.
 }
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -294,7 +294,7 @@ message RunGraphResponse {
 
   // If the request asked for execution stats, the cost graph, or the partition
   // graphs, these are returned here.
-  // TODO: Package these in a RunMetadata instead.
+  // TODO(suharshs): Package these in a RunMetadata instead.
   StepStats step_stats = 2;
   CostGraphDef cost_graph = 3;
   repeated GraphDef partition_graph = 4;

navi/navi/proto/tensorflow_serving/apis/logging.proto

@@ -13,5 +13,5 @@ message LogMetadata {
   SamplingConfig sampling_config = 2;
   // List of tags used to load the relevant MetaGraphDef from SavedModel.
   repeated string saved_model_tags = 3;
-  // TODO: Add more metadata as mentioned in the bug.
+  // TODO(b/33279154): Add more metadata as mentioned in the bug.
 }

navi/navi/proto/tensorflow_serving/config/file_system_storage_path_source.proto

@@ -58,7 +58,7 @@ message FileSystemStoragePathSourceConfig {
 
   // A single servable name/base_path pair to monitor.
   // DEPRECATED: Use 'servables' instead.
-  // TODO: Stop using these fields, and ultimately remove them here.
+  // TODO(b/30898016): Stop using these fields, and ultimately remove them here.
   string servable_name = 1 [deprecated = true];
   string base_path = 2 [deprecated = true];
 
@@ -76,7 +76,7 @@ message FileSystemStoragePathSourceConfig {
   // check for a version to appear later.)
   // DEPRECATED: Use 'servable_versions_always_present' instead, which includes
   // this behavior.
-  // TODO: Remove 2019-10-31 or later.
+  // TODO(b/30898016): Remove 2019-10-31 or later.
   bool fail_if_zero_versions_at_startup = 4 [deprecated = true];
 
   // If true, the servable is always expected to exist on the underlying

navi/navi/proto/tensorflow_serving/config/model_server_config.proto

@@ -9,7 +9,7 @@ import "tensorflow_serving/config/logging_config.proto";
 option cc_enable_arenas = true;
 
 // The type of model.
-// TODO: DEPRECATED.
+// TODO(b/31336131): DEPRECATED.
 enum ModelType {
   MODEL_TYPE_UNSPECIFIED = 0 [deprecated = true];
   TENSORFLOW = 1 [deprecated = true];
@@ -31,7 +31,7 @@ message ModelConfig {
   string base_path = 2;
 
   // Type of model.
-  // TODO: DEPRECATED. Please use 'model_platform' instead.
+  // TODO(b/31336131): DEPRECATED. Please use 'model_platform' instead.
   ModelType model_type = 3 [deprecated = true];
 
   // Type of model (e.g. "tensorflow").

navi/navi/src/bootstrap.rs

@@ -1,5 +1,6 @@
 use anyhow::Result;
 use log::{info, warn};
+use x509_parser::{prelude::{parse_x509_pem}, parse_x509_certificate};
 use std::collections::HashMap;
 use tokio::time::Instant;
 use tonic::{
@@ -27,6 +28,7 @@ use crate::cli_args::{ARGS, INPUTS, OUTPUTS};
 use crate::metrics::{
     NAVI_VERSION, NUM_PREDICTIONS, NUM_REQUESTS_FAILED, NUM_REQUESTS_FAILED_BY_MODEL,
     NUM_REQUESTS_RECEIVED, NUM_REQUESTS_RECEIVED_BY_MODEL, RESPONSE_TIME_COLLECTOR,
+    CERT_EXPIRY_EPOCH
 };
 use crate::predict_service::{Model, PredictService};
 use crate::tf_proto::tensorflow_serving::model_spec::VersionChoice::Version;
@@ -233,6 +235,12 @@ impl<T: Model> PredictionService for PredictService<T> {
     }
 }
 
+// A function that takes a timestamp as input and returns a ticker stream
+fn report_expiry(expiry_time: i64) {
+    info!("Certificate expires at epoch: {:?}", expiry_time);
+    CERT_EXPIRY_EPOCH.set(expiry_time as i64);
+}
+
 pub fn bootstrap<T: Model>(model_factory: ModelFactory<T>) -> Result<()> {
     info!("package: {}, version: {}, args: {:?}", NAME, VERSION, *ARGS);
     //we follow SemVer. So here we assume MAJOR.MINOR.PATCH
@@ -249,6 +257,7 @@ pub fn bootstrap<T: Model>(model_factory: ModelFactory<T>) -> Result<()> {
         );
     }
 
+    
     tokio::runtime::Builder::new_multi_thread()
         .thread_name("async worker")
         .worker_threads(ARGS.num_worker_threads)
@@ -266,6 +275,21 @@ pub fn bootstrap<T: Model>(model_factory: ModelFactory<T>) -> Result<()> {
             let mut builder = if ARGS.ssl_dir.is_empty() {
                 Server::builder()
             } else {
+                // Read the pem file as a string
+                let pem_str = std::fs::read_to_string(format!("{}/server.crt", ARGS.ssl_dir)).unwrap();
+                let res = parse_x509_pem(&pem_str.as_bytes());
+                match res {
+                    Ok((rem, pem_2)) => {
+                        assert!(rem.is_empty());
+                        assert_eq!(pem_2.label, String::from("CERTIFICATE"));
+                        let res_x509 = parse_x509_certificate(&pem_2.contents);
+                        info!("Certificate label: {}", pem_2.label);
+                        assert!(res_x509.is_ok());
+                        report_expiry(res_x509.unwrap().1.validity().not_after.timestamp());
+                    },
+                    _ => panic!("PEM parsing failed: {:?}", res),
+                }
+
                 let key = tokio::fs::read(format!("{}/server.key", ARGS.ssl_dir))
                     .await
                     .expect("can't find key file");

navi/navi/src/metrics.rs

@@ -171,6 +171,9 @@ lazy_static! {
         &["model_name"]
     )
     .expect("metric can be created");
+    pub static ref CERT_EXPIRY_EPOCH: IntGauge =
+        IntGauge::new(":navi:cert_expiry_epoch", "Timestamp when the current cert expires")
+            .expect("metric can be created");
 }
 
 pub fn register_custom_metrics() {
@@ -249,6 +252,10 @@ pub fn register_custom_metrics() {
     REGISTRY
         .register(Box::new(CONVERTER_TIME_COLLECTOR.clone()))
         .expect("collector can be registered");
+    REGISTRY
+    .register(Box::new(CERT_EXPIRY_EPOCH.clone()))
+    .expect("collector can be registered");
+
 }
 
 pub fn register_dynamic_metrics(c: &HistogramVec) {

navi/navi/src/onnx_model.rs

@@ -189,7 +189,7 @@ pub mod onnx {
                     &version,
                     reporting_feature_ids,
                     Some(metrics::register_dynamic_metrics),
-                )),
+                )?),
             };
             onnx_model.warmup()?;
             Ok(onnx_model)

navi/navi/src/predict_service.rs

@@ -24,7 +24,7 @@ use serde_json::{self, Value};
 
 pub trait Model: Send + Sync + Display + Debug + 'static {
     fn warmup(&self) -> Result<()>;
-    //TODO: refactor this to return Vec<Vec<TensorScores>>, i.e.
+    //TODO: refactor this to return vec<vec<TensorScores>>, i.e.
     //we have the underlying runtime impl to split the response to each client.
     //It will eliminate some inefficient memory copy in onnx_model.rs as well as simplify code
     fn do_predict(
@@ -222,8 +222,8 @@ impl<T: Model> PredictService<T> {
             .map(|b| b.parse().unwrap())
             .collect::<Vec<u64>>();
         let no_msg_wait_millis = *batch_time_out_millis.iter().min().unwrap();
-        let mut all_model_predictors =
+        let mut all_model_predictors: ArrayVec::<ArrayVec<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>, MAX_NUM_MODELS> =
-            ArrayVec::<ArrayVec<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>, MAX_NUM_MODELS>::new();
+            (0 ..MAX_NUM_MODELS).map( |_| ArrayVec::<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>::new()).collect();
         loop {
             let msg = rx.try_recv();
             let no_more_msg = match msg {
@@ -272,27 +272,23 @@ impl<T: Model> PredictService<T> {
                         queue_reset_ts: Instant::now(),
                         queue_earliest_rq_ts: Instant::now(),
                     };
-                    if idx < all_model_predictors.len() {
+                    assert!(idx < all_model_predictors.len());
                     metrics::NEW_MODEL_SNAPSHOT
                         .with_label_values(&[&MODEL_SPECS[idx]])
                         .inc();
 
-                        info!("now we serve updated model: {}", predictor.model);
                     //we can do this since the vector is small
                     let predictors = &mut all_model_predictors[idx];
+                    if predictors.len() == 0 {
+                        info!("now we serve new model: {}", predictor.model);
+                    }
+                    else {
+                        info!("now we serve updated model: {}", predictor.model);
+                    }
                     if predictors.len() == ARGS.versions_per_model {
                         predictors.remove(predictors.len() - 1);
                     }
                     predictors.insert(0, predictor);
-                    } else {
-                        info!("now we serve new model: {:}", predictor.model);
-                        let mut predictors =
-                            ArrayVec::<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>::new();
-                        predictors.push(predictor);
-                        all_model_predictors.push(predictors);
-                        //check the invariant that we always push the last model to the end
-                        assert_eq!(all_model_predictors.len(), idx + 1)
-                    }
                     false
                 }
                 Err(TryRecvError::Empty) => true,

navi/segdense/src/error.rs

@@ -19,11 +19,21 @@ impl Display for SegDenseError {
         match self {
             SegDenseError::IoError(io_error) => write!(f, "{}", io_error),
             SegDenseError::Json(serde_json) => write!(f, "{}", serde_json),
-      SegDenseError::JsonMissingRoot => write!(f, "{}", "SegDense JSON: Root Node note found!"),
+            SegDenseError::JsonMissingRoot => {
-      SegDenseError::JsonMissingObject => write!(f, "{}", "SegDense JSON: Object note found!"),
+                write!(f, "{}", "SegDense JSON: Root Node note found!")
-      SegDenseError::JsonMissingArray => write!(f, "{}", "SegDense JSON: Array Node note found!"),
+            }
-      SegDenseError::JsonArraySize => write!(f, "{}", "SegDense JSON: Array size not as expected!"),
+            SegDenseError::JsonMissingObject => {
-      SegDenseError::JsonMissingInputFeature => write!(f, "{}", "SegDense JSON: Missing input feature!"),
+                write!(f, "{}", "SegDense JSON: Object note found!")
+            }
+            SegDenseError::JsonMissingArray => {
+                write!(f, "{}", "SegDense JSON: Array Node note found!")
+            }
+            SegDenseError::JsonArraySize => {
+                write!(f, "{}", "SegDense JSON: Array size not as expected!")
+            }
+            SegDenseError::JsonMissingInputFeature => {
+                write!(f, "{}", "SegDense JSON: Missing input feature!")
+            }
         }
     }
 }

navi/segdense/src/lib.rs

@@ -1,4 +1,4 @@
 pub mod error;
-pub mod segdense_transform_spec_home_recap_2022; 
 pub mod mapper;
+pub mod segdense_transform_spec_home_recap_2022;
 pub mod util;

navi/segdense/src/main.rs

@@ -20,4 +20,3 @@ fn main() -> Result<(), SegDenseError> {
 
     Ok(())
 }
-

navi/segdense/src/mapper.rs

@@ -19,7 +19,7 @@ pub struct FeatureMapper {
 impl FeatureMapper {
     pub fn new() -> FeatureMapper {
         FeatureMapper {
-            map: HashMap::new()
+            map: HashMap::new(),
         }
     }
 }

navi/segdense/src/segdense_transform_spec_home_recap_2022.rs

@@ -164,7 +164,6 @@ pub struct ComplexFeatureTypeTransformSpec {
     pub tensor_shape: Vec<i64>,
 }
 
-
 #[derive(Default, Debug, Clone, PartialEq, Serialize, Deserialize)]
 #[serde(rename_all = "camelCase")]
 pub struct InputFeatureMapRecord {

navi/segdense/src/util.rs

@@ -1,23 +1,23 @@
+use log::debug;
 use std::fs;
-use log::{debug};
 
-use serde_json::{Value, Map};
+use serde_json::{Map, Value};
 
 use crate::error::SegDenseError;
-use crate::mapper::{FeatureMapper, FeatureInfo, MapWriter};
+use crate::mapper::{FeatureInfo, FeatureMapper, MapWriter};
 use crate::segdense_transform_spec_home_recap_2022::{self as seg_dense, InputFeature};
 
-pub fn load_config(file_name: &str) -> seg_dense::Root {
+pub fn load_config(file_name: &str) -> Result<seg_dense::Root, SegDenseError> {
-    let json_str = fs::read_to_string(file_name).expect(
+    let json_str = fs::read_to_string(file_name)?;
-        &format!("Unable to load segdense file {}", file_name));
+    // &format!("Unable to load segdense file {}", file_name));
-    let seg_dense_config = parse(&json_str).expect(
+    let seg_dense_config = parse(&json_str)?;
-        &format!("Unable to parse segdense file {}", file_name));
+    // &format!("Unable to parse segdense file {}", file_name));
-    return seg_dense_config;
+    Ok(seg_dense_config)
 }
 
 pub fn parse(json_str: &str) -> Result<seg_dense::Root, SegDenseError> {
     let root: seg_dense::Root = serde_json::from_str(json_str)?;
-    return Ok(root);
+    Ok(root)
 }
 
 /**
@@ -44,15 +44,8 @@ pub fn safe_load_config(json_str: &str) -> Result<FeatureMapper, SegDenseError>
     load_from_parsed_config(root)
 }
 
-pub fn load_from_parsed_config_ref(root: &seg_dense::Root) -> FeatureMapper {
-    load_from_parsed_config(root.clone()).unwrap_or_else(
-      |error| panic!("Error loading all_config.json - {}", error))
-}
-
 // Perf note : make 'root' un-owned
-pub fn load_from_parsed_config(root: seg_dense::Root) ->
+pub fn load_from_parsed_config(root: seg_dense::Root) -> Result<FeatureMapper, SegDenseError> {
-    Result<FeatureMapper, SegDenseError> {
-
     let v = root.input_features_map;
 
     // Do error check
@@ -86,7 +79,7 @@ pub fn load_from_parsed_config(root: seg_dense::Root) ->
             Some(info) => {
                 debug!("{:?}", info);
                 fm.set(feature_id, info)
-            },
+            }
             None => (),
         }
     }
@@ -94,7 +87,10 @@ pub fn load_from_parsed_config(root: seg_dense::Root) ->
     Ok(fm)
 }
 #[allow(dead_code)]
-fn add_feature_info_to_mapper(feature_mapper: &mut FeatureMapper, input_features: &Vec<InputFeature>) {
+fn add_feature_info_to_mapper(
+    feature_mapper: &mut FeatureMapper,
+    input_features: &Vec<InputFeature>,
+) {
     for input_feature in input_features.iter() {
         let feature_id = input_feature.feature_id;
         let feature_info = to_feature_info(input_feature);
@@ -103,7 +99,7 @@ fn add_feature_info_to_mapper(feature_mapper: &mut FeatureMapper, input_features
             Some(info) => {
                 debug!("{:?}", info);
                 feature_mapper.set(feature_id, info)
-                },
+            }
             None => (),
         }
     }
@@ -139,7 +135,7 @@ pub fn to_feature_info(input_feature: &seg_dense::InputFeature) -> Option<Featur
             2 => 0,
             3 => 2,
             _ => -1,
-        }
+        },
     };
 
     if input_feature.index < 0 {
@@ -156,4 +152,3 @@ pub fn to_feature_info(input_feature: &seg_dense::InputFeature) -> Option<Featur
         index_within_tensor: input_feature.index,
     })
 }
-