navi/README.md

@@ -31,11 +31,6 @@ In navi/navi, you can run the following commands:
 - `scripts/run_onnx.sh` for [Onnx](https://onnx.ai/)
 
 Do note that you need to create a models directory and create some versions, preferably using epoch time, e.g., `1679693908377`.
-so the models structure looks like:
-  models/
-       -web_click
-        - 1809000
-        - 1809010
 
 ## Build
 You can adapt the above scripts to build using Cargo.

navi/dr_transform/Cargo.toml

@@ -3,6 +3,7 @@ name = "dr_transform"
 version = "0.1.0"
 edition = "2021"
 
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 [dependencies]
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
@@ -11,6 +12,7 @@ bpr_thrift = { path = "../thrift_bpr_adapter/thrift/"}
 segdense = { path = "../segdense/"}
 thrift = "0.17.0"
 ndarray = "0.15"
+ort = {git ="https://github.com/pykeio/ort.git", tag="v1.14.2"}
 base64 = "0.20.0"
 npyz = "0.7.2"
 log = "0.4.17"
@@ -19,11 +21,6 @@ prometheus = "0.13.1"
 once_cell = "1.17.0"
 rand = "0.8.5"
 itertools = "0.10.5"
-anyhow = "1.0.70"
-[target.'cfg(not(target_os="linux"))'.dependencies]
-ort = {git ="https://github.com/pykeio/ort.git", features=["profiling"], tag="v1.14.6"}
-[target.'cfg(target_os="linux")'.dependencies]
-ort = {git ="https://github.com/pykeio/ort.git", features=["profiling", "tensorrt", "cuda", "copy-dylibs"], tag="v1.14.6"}
 [dev-dependencies]
 criterion = "0.3.0"
 

navi/dr_transform/src/all_config.rs

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

navi/dr_transform/src/converter.rs

@@ -2,9 +2,6 @@ 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;
@@ -19,6 +16,8 @@ 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,
@@ -29,24 +28,20 @@ pub fn log_feature_match(
 
     for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap() {
         debug!(
-            "{} - Continous Datarecord => Feature ID: {}, Feature value: {}",
-            dr_type, feature_id, feature_value
+            "{dr_type} - Continuous Datarecord => Feature ID: {feature_id}, Feature value: {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!(
-            "{} - Binary Datarecord => Feature ID: {}",
-            dr_type, feature_id
-        );
+        debug!("{dr_type} - Binary Datarecord => Feature ID: {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:?}")
             }
         }
     }
@@ -95,19 +90,18 @@ impl BatchPredictionRequestToTorchTensorConverter {
         model_version: &str,
         reporting_feature_ids: Vec<(i64, &str)>,
         register_metric_fn: Option<impl Fn(&HistogramVec)>,
-    ) -> anyhow::Result<BatchPredictionRequestToTorchTensorConverter> {
-        let all_config_path = format!("{}/{}/all_config.json", model_dir, model_version);
-        let seg_dense_config_path = format!(
-            "{}/{}/segdense_transform_spec_home_recap_2022.json",
-            model_dir, model_version
-        );
-        let seg_dense_config = util::load_config(&seg_dense_config_path)?;
+    ) -> BatchPredictionRequestToTorchTensorConverter {
+        let all_config_path = format!("{model_dir}/{model_version}/all_config.json");
+        let seg_dense_config_path =
+            format!("{model_dir}/{model_version}/segdense_transform_spec_home_recap_2022.json");
+        let seg_dense_config = util::load_config(&seg_dense_config_path);
         let all_config = all_config::parse(
             &fs::read_to_string(&all_config_path)
-                .with_context(|| "error loading all_config.json - ")?,
-        )?;
+                .unwrap_or_else(|error| panic!("error loading all_config.json - {error}")),
+        )
+        .unwrap();
 
-        let feature_mapper = util::load_from_parsed_config(seg_dense_config.clone())?;
+        let feature_mapper = util::load_from_parsed_config_ref(&seg_dense_config);
 
         let user_embedding_feature_id = Self::get_feature_id(
             &all_config
@@ -137,11 +131,11 @@ 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(&[
-                        0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
+                    .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,
                         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");
@@ -150,18 +144,18 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     ":navi:feature_id:continuous",
                     "continuous Feature ID values",
                 )
-                .buckets(Vec::from(&[
-                    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])),
+                .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,
+                    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,
+                ])),
                 &["feature_id"],
             )
             .expect("metric cannot be created");
-            register_metric_fn.map(|r| {
+            if let Some(r) = register_metric_fn {
                 r(&discrete);
                 r(&continuous);
-            });
+            }
             (discrete, continuous)
         });
 
@@ -170,16 +164,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
         for (feature_id, feature_type) in reporting_feature_ids.iter() {
             match *feature_type {
-                "discrete" => discrete_features_to_report.insert(feature_id.clone()),
-                "continuous" => continuous_features_to_report.insert(feature_id.clone()),
-                _ => bail!(
-                    "Invalid feature type {} for reporting metrics!",
-                    feature_type
-                ),
+                "discrete" => discrete_features_to_report.insert(*feature_id),
+                "continuous" => continuous_features_to_report.insert(*feature_id),
+                _ => panic!("Invalid feature type {feature_type} for reporting metrics!"),
             };
         }
 
-        Ok(BatchPredictionRequestToTorchTensorConverter {
+        BatchPredictionRequestToTorchTensorConverter {
             all_config,
             seg_dense_config,
             all_config_path,
@@ -192,7 +183,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
             continuous_features_to_report,
             discrete_feature_metrics,
             continuous_feature_metrics,
-        })
+        }
     }
 
     fn get_feature_id(feature_name: &str, seg_dense_config: &Root) -> i64 {
@@ -227,9 +218,9 @@ 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.as_ref().unwrap().tensors.is_some() {
-                    if bpr
+            if bpr.common_features.is_some()
+                && bpr.common_features.as_ref().unwrap().tensors.is_some()
+                && bpr
                     .common_features
                     .as_ref()
                     .unwrap()
@@ -267,8 +258,6 @@ 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()
@@ -309,9 +298,9 @@ 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: usize = batch_ends[batch_ends.len() - 1];
-        let cols: usize = 5293;
-        let full_size: usize = rows * cols;
+        let rows = batch_ends[batch_ends.len() - 1];
+        let cols = 5293;
+        let full_size = rows * cols;
         let default_val = f32::NAN;
 
         let mut tensor = vec![default_val; full_size];
@@ -336,19 +325,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     .unwrap();
 
                 for feature in common_features {
-                    match self.feature_mapper.get(feature.0) {
-                        Some(f_info) => {
+                    if let Some(f_info) = self.feature_mapper.get(feature.0) {
                         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: usize = r * cols + idx;
+                                let flat_index = 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()])
@@ -363,28 +349,24 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
             // Process the batch of datarecords
             for r in bpr_start..bpr_end {
-                let dr: &DataRecord =
-                    &bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
+                let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
                 if dr.continuous_features.is_some() {
                     for feature in dr.continuous_features.as_ref().unwrap() {
-                        match self.feature_mapper.get(&feature.0) {
-                            Some(f_info) => {
+                        if let Some(f_info) = self.feature_mapper.get(feature.0) {
                             let idx = f_info.index_within_tensor as usize;
-                                let flat_index: usize = r * cols + idx;
+                            let flat_index = 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() as f64)
+                                .observe(feature.1.into_inner())
                         } 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() as f64)
+                                .observe(feature.1.into_inner())
                         }
                     }
                 }
@@ -401,10 +383,10 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
     fn get_binary(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
         // These need to be part of model schema
-        let rows: usize = batch_ends[batch_ends.len() - 1];
-        let cols: usize = 149;
-        let full_size: usize = rows * cols;
-        let default_val: i64 = 0;
+        let rows = batch_ends[batch_ends.len() - 1];
+        let cols = 149;
+        let full_size = rows * cols;
+        let default_val = 0;
 
         let mut v = vec![default_val; full_size];
 
@@ -428,19 +410,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     .unwrap();
 
                 for feature in common_features {
-                    match self.feature_mapper.get(feature) {
-                        Some(f_info) => {
+                    if let Some(f_info) = self.feature_mapper.get(feature) {
                         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: usize = r * cols + idx;
+                                let flat_index = r * cols + idx;
                                 v[flat_index] = 1;
                             }
                         }
                     }
-                        None => (),
-                    }
                 }
             }
 
@@ -449,14 +428,11 @@ 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() {
-                        match self.feature_mapper.get(&feature) {
-                            Some(f_info) => {
+                        if let Some(f_info) = self.feature_mapper.get(feature) {
                             let idx = f_info.index_within_tensor as usize;
-                                let flat_index: usize = r * cols + idx;
+                            let flat_index = r * cols + idx;
                             v[flat_index] = 1;
                         }
-                            None => (),
-                        }
                     }
                 }
             }
@@ -472,10 +448,10 @@ 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: usize = batch_ends[batch_ends.len() - 1];
-        let cols: usize = 320;
-        let full_size: usize = rows * cols;
-        let default_val: i64 = 0;
+        let rows = batch_ends[batch_ends.len() - 1];
+        let cols = 320;
+        let full_size = rows * cols;
+        let default_val = 0;
 
         let mut v = vec![default_val; full_size];
 
@@ -499,19 +475,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
                     .unwrap();
 
                 for feature in common_features {
-                    match self.feature_mapper.get(feature.0) {
-                        Some(f_info) => {
+                    if let Some(f_info) = self.feature_mapper.get(feature.0) {
                         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: usize = r * cols + idx;
+                                let flat_index = 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()])
@@ -522,19 +495,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
 
             // Process the batch of datarecords
             for r in bpr_start..bpr_end {
-                let dr: &DataRecord = &bpr.individual_features_list[usize::try_from(r).unwrap()];
+                let dr: &DataRecord = &bpr.individual_features_list[r];
                 if dr.discrete_features.is_some() {
                     for feature in dr.discrete_features.as_ref().unwrap() {
-                        match self.feature_mapper.get(&feature.0) {
-                            Some(f_info) => {
+                        if let Some(f_info) = self.feature_mapper.get(feature.0) {
                             let idx = f_info.index_within_tensor as usize;
-                                let flat_index: usize = r * cols + idx;
+                            let flat_index = 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()])
@@ -599,7 +569,7 @@ impl Converter for BatchPredictionRequestToTorchTensorConverter {
             .map(|bpr| bpr.individual_features_list.len())
             .scan(0usize, |acc, e| {
                 //running total
-                *acc = *acc + e;
+                *acc += e;
                 Some(*acc)
             })
             .collect::<Vec<_>>();

navi/dr_transform/src/lib.rs

@@ -3,4 +3,3 @@ pub mod converter;
 #[cfg(test)]
 mod test;
 pub mod util;
-pub extern crate ort;

navi/navi/Cargo.toml

@@ -1,7 +1,8 @@
 [package]
 name = "navi"
-version = "2.0.45"
+version = "2.0.42"
 edition = "2021"
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 
 [[bin]]
 name = "navi"
@@ -15,19 +16,12 @@ required-features=["torch"]
 name = "navi_onnx"
 path = "src/bin/navi_onnx.rs"
 required-features=["onnx"]
-[[bin]]
-name = "navi_onnx_test"
-path = "src/bin/bin_tests/navi_onnx_test.rs"
-[[bin]]
-name = "navi_torch_test"
-path = "src/bin/bin_tests/navi_torch_test.rs"
-required-features=["torch"]
 
 [features]
 default=[]
 navi_console=[]
 torch=["tch"]
-onnx=[]
+onnx=["ort"]
 tf=["tensorflow"]
 [dependencies]
 itertools = "0.10.5"
@@ -53,7 +47,6 @@ parking_lot = "0.12.1"
 rand = "0.8.5"
 rand_pcg = "0.3.1"
 random = "0.12.2"
-x509-parser = "0.15.0"
 sha256 = "1.0.3"
 tonic = { version = "0.6.2", features=['compression', 'tls'] }
 tokio = { version = "1.17.0", features = ["macros", "rt-multi-thread", "fs", "process"] }
@@ -62,12 +55,16 @@ npyz = "0.7.3"
 base64 = "0.21.0"
 histogram = "0.6.9"
 tch = {version = "0.10.3", optional = true}
-tensorflow = { version = "0.18.0", optional = true }
+tensorflow = { version = "0.20.0", optional = true }
 once_cell = {version = "1.17.1"}
 ndarray = "0.15"
 serde = "1.0.154"
 serde_json = "1.0.94"
 dr_transform = { path = "../dr_transform"}
+[target.'cfg(not(target_os="linux"))'.dependencies]
+ort = {git ="https://github.com/pykeio/ort.git", features=["profiling"], optional = true, tag="v1.14.2"}
+[target.'cfg(target_os="linux")'.dependencies]
+ort = {git ="https://github.com/pykeio/ort.git", features=["profiling", "tensorrt", "cuda", "copy-dylibs"], optional = true, tag="v1.14.2"}
 [build-dependencies]
 tonic-build = {version = "0.6.2", features=['prost', "compression"] }
 [profile.release]
@@ -77,5 +74,3 @@ ndarray-rand = "0.14.0"
 tokio-test = "*"
 assert_cmd = "2.0"
 criterion = "0.4.0"
-
-

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(mdan): Define TFT_SHAPE and add more examples.
+  // TODO: 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(mdan): Quantized types, legacy representations (e.g. ref)
+  // TODO
   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(mdan): Represent as TFT_COMPLEX[TFT_DOUBLE] instead?
+  // TODO: 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(mdan): Properly document this thing.
+  // TODO: 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(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc.
+    // TODO: 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(zhifengc):
+// TODO:
 //   * 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(josh11b): Add some examples here showing best practices.
+  // TODO: 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(josh11b): bool is_optional?
+    // TODO: 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(josh11b): Implement that optimization.
+  // TODO: 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(tucker): Use some more compact form of node identity than
+  // TODO: 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(touts): sort out the 0-rank issues.
+  // Shape of the tensor.  TODO: 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(shikharagarwal): Should we just remove this tag so that it can be
+    // TODO: 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(b/129330037): Add a single API that consistently treats
+    // TODO: 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(pbar) Turn this into a TraceOptions proto which allows
+  // TODO 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(nareshmodi): Include some sort of function/cache-key identifier?
+    // TODO: 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(b/195990880): Consider splitting this into a different RPC service.
+  // TODO: 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(cais): More visible documentation of this in g3docs.
+  // TODO: 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(cais): Document the detailed column names and semantics in a separate
+// TODO: 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(cais): Test the uniqueness guarantee in multi-host settings.
+  // TODO: 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(cais): When backporting to V1 Session.run() support, add more fields
+  // TODO 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(b/204231601): Use GRPC API once supported.
+// TODO: 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(nareshmodi): Consider adding NodeExecStats here to be able to
+  // TODO: 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(mrry): Return something about the operation?
+  // TODO: 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(b/169361281): support calling saved ConcreteFunction with structured
+  // TODO: 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(zongheng,zhifengc): maybe in the future, we can add information about
+// TODO: 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(mrry): Optionally add summary stats for the graph.
+  // TODO: 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(suharshs): Package these in a RunMetadata instead.
+  // TODO: 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(b/33279154): Add more metadata as mentioned in the bug.
+  // TODO: 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(b/30898016): Stop using these fields, and ultimately remove them here.
+  // TODO: 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(b/30898016): Remove 2019-10-31 or later.
+  // TODO: 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(b/31336131): DEPRECATED.
+// TODO: 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(b/31336131): DEPRECATED. Please use 'model_platform' instead.
+  // TODO: DEPRECATED. Please use 'model_platform' instead.
   ModelType model_type = 3 [deprecated = true];
 
   // Type of model (e.g. "tensorflow").

navi/navi/scripts/run_onnx.sh

@@ -1,9 +1,10 @@
 #!/bin/sh
 #RUST_LOG=debug LD_LIBRARY_PATH=so/onnx/lib target/release/navi_onnx --port 30 --num-worker-threads 8 --intra-op-parallelism 8 --inter-op-parallelism 8 \
 RUST_LOG=info LD_LIBRARY_PATH=so/onnx/lib cargo run --bin navi_onnx --features onnx -- \
-    --port 8030 --num-worker-threads 8 \
+    --port 30 --num-worker-threads 8 --intra-op-parallelism 8 --inter-op-parallelism 8 \
     --model-check-interval-secs 30 \
+    --model-dir models/int8 \
+    --output caligrated_probabilities \
+    --input "" \
     --modelsync-cli "echo" \
-    --onnx-ep-options use_arena=true \
-    --model-dir models/prod_home --output caligrated_probabilities  --input "" --intra-op-parallelism 8 --inter-op-parallelism 8 --max-batch-size 1 --batch-time-out-millis 1 \
-    --model-dir models/prod_home1 --output caligrated_probabilities  --input "" --intra-op-parallelism 8 --inter-op-parallelism 8 --max-batch-size 1 --batch-time-out-millis 1 \
+    --onnx-ep-options use_arena=true

navi/navi/src/bin/navi_onnx.rs

@@ -1,24 +1,11 @@
 use anyhow::Result;
-use log::info;
 use navi::cli_args::{ARGS, MODEL_SPECS};
 use navi::onnx_model::onnx::OnnxModel;
 use navi::{bootstrap, metrics};
 
 fn main() -> Result<()> {
     env_logger::init();
-    info!("global: {:?}", ARGS.onnx_global_thread_pool_options);
-    let assert_session_params = if ARGS.onnx_global_thread_pool_options.is_empty() {
-        // std::env::set_var("OMP_NUM_THREADS", "1");
-        info!("now we use per session thread pool");
-        MODEL_SPECS.len()
-    }
-    else {
-       info!("now we use global thread pool");
-        0
-    };
-    assert_eq!(assert_session_params, ARGS.inter_op_parallelism.len());
-    assert_eq!(assert_session_params, ARGS.inter_op_parallelism.len());
-
+    assert_eq!(MODEL_SPECS.len(), ARGS.inter_op_parallelism.len());
     metrics::register_custom_metrics();
     bootstrap::bootstrap(OnnxModel::new)
 }

navi/navi/src/bootstrap.rs

@@ -1,6 +1,5 @@
 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::{
@@ -28,7 +27,6 @@ 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;
@@ -209,9 +207,6 @@ impl<T: Model> PredictionService for PredictService<T> {
                         PredictResult::DropDueToOverload => Err(Status::resource_exhausted("")),
                         PredictResult::ModelNotFound(idx) => {
                             Err(Status::not_found(format!("model index {}", idx)))
-                        },
-                        PredictResult::ModelNotReady(idx) => {
-                            Err(Status::unavailable(format!("model index {}", idx)))
                         }
                         PredictResult::ModelVersionNotFound(idx, version) => Err(
                             Status::not_found(format!("model index:{}, version {}", idx, version)),
@@ -235,12 +230,6 @@ 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
@@ -257,7 +246,6 @@ 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)
@@ -275,21 +263,6 @@ 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/cli_args.rs

@@ -87,11 +87,13 @@ pub struct Args {
     pub intra_op_parallelism: Vec<String>,
     #[clap(
         long,
+        default_value = "14",
         help = "number of threads to parallelize computations of the graph"
     )]
     pub inter_op_parallelism: Vec<String>,
     #[clap(
         long,
+        default_value = "serving_default",
         help = "signature of a serving. only TF"
     )]
     pub serving_sig: Vec<String>,
@@ -105,8 +107,6 @@ pub struct Args {
         help = "max warmup records to use. warmup only implemented for TF"
     )]
     pub max_warmup_records: usize,
-    #[clap(long, value_parser = Args::parse_key_val::<String, String>, value_delimiter=',')]
-    pub onnx_global_thread_pool_options: Vec<(String, String)>,
     #[clap(
         long,
         default_value = "true",

navi/navi/src/lib.rs

@@ -146,7 +146,6 @@ pub enum PredictResult {
     Ok(Vec<TensorScores>, i64),
     DropDueToOverload,
     ModelNotFound(usize),
-    ModelNotReady(usize),
     ModelVersionNotFound(usize, i64),
 }
 

navi/navi/src/metrics.rs

@@ -171,9 +171,6 @@ 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() {
@@ -252,10 +249,6 @@ 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

@@ -13,22 +13,21 @@ pub mod onnx {
     use dr_transform::converter::{BatchPredictionRequestToTorchTensorConverter, Converter};
     use itertools::Itertools;
     use log::{debug, info};
-    use dr_transform::ort::environment::Environment;
-    use dr_transform::ort::session::Session;
-    use dr_transform::ort::tensor::InputTensor;
-    use dr_transform::ort::{ExecutionProvider, GraphOptimizationLevel, SessionBuilder};
-    use dr_transform::ort::LoggingLevel;
+    use ort::environment::Environment;
+    use ort::session::Session;
+    use ort::tensor::InputTensor;
+    use ort::{ExecutionProvider, GraphOptimizationLevel, SessionBuilder};
     use serde_json::Value;
     use std::fmt::{Debug, Display};
     use std::sync::Arc;
     use std::{fmt, fs};
     use tokio::time::Instant;
+
     lazy_static! {
         pub static ref ENVIRONMENT: Arc<Environment> = Arc::new(
             Environment::builder()
                 .with_name("onnx home")
-                .with_log_level(LoggingLevel::Error)
-                .with_global_thread_pool(ARGS.onnx_global_thread_pool_options.clone())
+                .with_log_level(ort::LoggingLevel::Error)
                 .build()
                 .unwrap()
         );
@@ -102,9 +101,7 @@ pub mod onnx {
             let meta_info = format!("{}/{}/{}", ARGS.model_dir[idx], version, META_INFO);
             let mut builder = SessionBuilder::new(&ENVIRONMENT)?
                 .with_optimization_level(GraphOptimizationLevel::Level3)?
-                .with_parallel_execution(ARGS.onnx_use_parallel_mode == "true")?;
-            if ARGS.onnx_global_thread_pool_options.is_empty() {
-                builder = builder
+                .with_parallel_execution(ARGS.onnx_use_parallel_mode == "true")?
                 .with_inter_threads(
                     utils::get_config_or(
                         model_config,
@@ -120,12 +117,7 @@ pub mod onnx {
                         &ARGS.intra_op_parallelism[idx],
                     )
                     .parse()?,
-                    )?;
-            }
-            else {
-                builder = builder.with_disable_per_session_threads()?;
-            }
-            builder = builder
+                )?
                 .with_memory_pattern(ARGS.onnx_use_memory_pattern == "true")?
                 .with_execution_providers(&OnnxModel::ep_choices())?;
             match &ARGS.profiling {
@@ -189,7 +181,7 @@ pub mod onnx {
                     &version,
                     reporting_feature_ids,
                     Some(metrics::register_dynamic_metrics),
-                )?),
+                )),
             };
             onnx_model.warmup()?;
             Ok(onnx_model)

navi/navi/src/predict_service.rs

@@ -1,7 +1,7 @@
 use anyhow::{anyhow, Result};
 use arrayvec::ArrayVec;
 use itertools::Itertools;
-use log::{error, info};
+use log::{error, info, warn};
 use std::fmt::{Debug, Display};
 use std::string::String;
 use std::sync::Arc;
@@ -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(
@@ -179,17 +179,17 @@ impl<T: Model> PredictService<T> {
         //initialize the latest version array
         let mut cur_versions = vec!["".to_owned(); MODEL_SPECS.len()];
         loop {
+            let config = utils::read_config(&meta_file).unwrap_or_else(|e| {
+                warn!("config file {} not found due to: {}", meta_file, e);
+                Value::Null
+            });
             info!("***polling for models***"); //nice deliminter
+            info!("config:{}", config);
             if let Some(ref cli) = ARGS.modelsync_cli {
                 if let Err(e) = call_external_modelsync(cli, &cur_versions).await {
                     error!("model sync cli running error:{}", e)
                 }
             }
-            let config = utils::read_config(&meta_file).unwrap_or_else(|e| {
-                info!("config file {} not found due to: {}", meta_file, e);
-                Value::Null
-            });
-            info!("config:{}", config);
             for (idx, cur_version) in cur_versions.iter_mut().enumerate() {
                 let model_dir = &ARGS.model_dir[idx];
                 PredictService::scan_load_latest_model_from_model_dir(
@@ -222,18 +222,13 @@ 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: ArrayVec::<ArrayVec<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>, MAX_NUM_MODELS> =
-            (0 ..MAX_NUM_MODELS).map( |_| ArrayVec::<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>::new()).collect();
+        let mut all_model_predictors =
+            ArrayVec::<ArrayVec<BatchPredictor<T>, MAX_VERSIONS_PER_MODEL>, MAX_NUM_MODELS>::new();
         loop {
             let msg = rx.try_recv();
             let no_more_msg = match msg {
                 Ok(PredictMessage::Predict(model_spec_at, version, val, resp, ts)) => {
                     if let Some(model_predictors) = all_model_predictors.get_mut(model_spec_at) {
-                        if model_predictors.is_empty() {
-                            resp.send(PredictResult::ModelNotReady(model_spec_at))
-                                .unwrap_or_else(|e| error!("cannot send back model not ready error: {:?}", e));
-                        }
-                        else {
                         match version {
                             None => model_predictors[0].push(val, resp, ts),
                             Some(the_version) => match model_predictors
@@ -251,10 +246,9 @@ impl<T: Model> PredictService<T> {
                                 Some(predictor) => predictor.push(val, resp, ts),
                             },
                         }
-                        }
                     } else {
                         resp.send(PredictResult::ModelNotFound(model_spec_at))
-                            .unwrap_or_else(|e| error!("cannot send back model not found error: {:?}", e))
+                            .unwrap_or_else(|e| error!("cannot send back model error: {:?}", e))
                     }
                     MPSC_CHANNEL_SIZE.dec();
                     false
@@ -272,23 +266,27 @@ impl<T: Model> PredictService<T> {
                         queue_reset_ts: Instant::now(),
                         queue_earliest_rq_ts: Instant::now(),
                     };
-                    assert!(idx < all_model_predictors.len());
+                    if 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/Cargo.toml

@@ -3,9 +3,9 @@ name = "segdense"
 version = "0.1.0"
 edition = "2021"
 
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 
 [dependencies]
-env_logger = "0.10.0"
 serde = { version = "1.0.104", features = ["derive"] }
 serde_json = "1.0.48"
 log = "0.4.17"

navi/segdense/src/error.rs

@@ -19,21 +19,11 @@ 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::JsonMissingObject => {
-                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!")
-            }
+      SegDenseError::JsonMissingRoot => write!(f, "{}", "SegDense JSON: Root Node note found!"),
+      SegDenseError::JsonMissingObject => 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 mapper;
 pub mod segdense_transform_spec_home_recap_2022; 
+pub mod mapper;
 pub mod util;

navi/segdense/src/main.rs

@@ -20,3 +20,4 @@ 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

@@ -164,6 +164,7 @@ 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::{Map, Value};
+use serde_json::{Value, Map};
 
 use crate::error::SegDenseError;
-use crate::mapper::{FeatureInfo, FeatureMapper, MapWriter};
+use crate::mapper::{FeatureMapper, FeatureInfo, MapWriter};
 use crate::segdense_transform_spec_home_recap_2022::{self as seg_dense, InputFeature};
 
-pub fn load_config(file_name: &str) -> Result<seg_dense::Root, SegDenseError> {
-    let json_str = fs::read_to_string(file_name)?;
-    // &format!("Unable to load segdense file {}", file_name));
-    let seg_dense_config = parse(&json_str)?;
-    // &format!("Unable to parse segdense file {}", file_name));
-    Ok(seg_dense_config)
+pub fn load_config(file_name: &str) -> seg_dense::Root {
+    let json_str = fs::read_to_string(file_name).expect(
+        &format!("Unable to load segdense file {}", file_name));
+    let seg_dense_config = parse(&json_str).expect(
+        &format!("Unable to parse segdense file {}", file_name));
+    return 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)?;
-    Ok(root)
+    return Ok(root);
 }
 
 /**
@@ -44,8 +44,15 @@ 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) -> Result<FeatureMapper, SegDenseError> {
+pub fn load_from_parsed_config(root: seg_dense::Root) ->
+    Result<FeatureMapper, SegDenseError> {
+
     let v = root.input_features_map;
 
     // Do error check
@@ -79,7 +86,7 @@ pub fn load_from_parsed_config(root: seg_dense::Root) -> Result<FeatureMapper, S
             Some(info) => {
                 debug!("{:?}", info);
                 fm.set(feature_id, info)
-            }
+            },
             None => (),
         }
     }
@@ -87,10 +94,7 @@ pub fn load_from_parsed_config(root: seg_dense::Root) -> Result<FeatureMapper, S
     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);
@@ -99,7 +103,7 @@ fn add_feature_info_to_mapper(
                 Some(info) => {
                     debug!("{:?}", info);
                     feature_mapper.set(feature_id, info)
-            }
+                },
                 None => (),
             }
         }
@@ -135,7 +139,7 @@ pub fn to_feature_info(input_feature: &seg_dense::InputFeature) -> Option<Featur
             2 => 0,
             3 => 2,
             _ => -1,
-        },
+        }
     };
 
     if input_feature.index < 0 {
@@ -152,3 +156,4 @@ pub fn to_feature_info(input_feature: &seg_dense::InputFeature) -> Option<Featur
         index_within_tensor: input_feature.index,
     })
 }
+

unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/behavioral_client_event/BUILD

@@ -0,0 +1,13 @@
+scala_library(
+    sources = [
+        "*.scala",
+    ],
+    tags = ["bazel-compatible"],
+    dependencies = [
+        "client-events/thrift/src/thrift/storage/twitter/behavioral_event:behavioral_event-scala",
+        "kafka/finagle-kafka/finatra-kafka/src/main/scala",
+        "unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter:base",
+        "unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/common",
+        "unified_user_actions/thrift/src/main/thrift/com/twitter/unified_user_actions:unified_user_actions-scala",
+    ],
+)

unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/behavioral_client_event/BaseBCEAdapter.scala

@@ -0,0 +1,96 @@
+package com.twitter.unified_user_actions.adapter.behavioral_client_event
+
+import com.twitter.client_event_entities.serverside_context_key.latest.thriftscala.FlattenedServersideContextKey
+import com.twitter.storage.behavioral_event.thriftscala.EventLogContext
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.adapter.common.AdapterUtils
+import com.twitter.unified_user_actions.thriftscala.ActionType
+import com.twitter.unified_user_actions.thriftscala.BreadcrumbTweet
+import com.twitter.unified_user_actions.thriftscala.ClientEventNamespace
+import com.twitter.unified_user_actions.thriftscala.EventMetadata
+import com.twitter.unified_user_actions.thriftscala.Item
+import com.twitter.unified_user_actions.thriftscala.ProductSurface
+import com.twitter.unified_user_actions.thriftscala.ProductSurfaceInfo
+import com.twitter.unified_user_actions.thriftscala.SourceLineage
+import com.twitter.unified_user_actions.thriftscala.UnifiedUserAction
+import com.twitter.unified_user_actions.thriftscala.UserIdentifier
+
+case class ProductSurfaceRelated(
+  productSurface: Option[ProductSurface],
+  productSurfaceInfo: Option[ProductSurfaceInfo])
+
+trait BaseBCEAdapter {
+  def toUUA(e: FlattenedEventLog): Seq[UnifiedUserAction]
+
+  protected def getUserIdentifier(c: EventLogContext): UserIdentifier =
+    UserIdentifier(
+      userId = c.userId,
+      guestIdMarketing = c.guestIdMarketing
+    )
+
+  protected def getEventMetadata(e: FlattenedEventLog): EventMetadata =
+    EventMetadata(
+      sourceLineage = SourceLineage.BehavioralClientEvents,
+      sourceTimestampMs =
+        e.context.driftAdjustedEventCreatedAtMs.getOrElse(e.context.eventCreatedAtMs),
+      receivedTimestampMs = AdapterUtils.currentTimestampMs,
+      // Client UI language or from Gizmoduck which is what user set in Twitter App.
+      // Please see more at https://sourcegraph.twitter.biz/git.twitter.biz/source/-/blob/finatra-internal/international/src/main/scala/com/twitter/finatra/international/LanguageIdentifier.scala
+      // The format should be ISO 639-1.
+      language = e.context.languageCode.map(AdapterUtils.normalizeLanguageCode),
+      // Country code could be IP address (geoduck) or User registration country (gizmoduck) and the former takes precedence.
+      // We don’t know exactly which one is applied, unfortunately,
+      // see https://sourcegraph.twitter.biz/git.twitter.biz/source/-/blob/finatra-internal/international/src/main/scala/com/twitter/finatra/international/CountryIdentifier.scala
+      // The format should be ISO_3166-1_alpha-2.
+      countryCode = e.context.countryCode.map(AdapterUtils.normalizeCountryCode),
+      clientAppId = e.context.clientApplicationId,
+      clientVersion = e.context.clientVersion,
+      clientPlatform = e.context.clientPlatform,
+      viewHierarchy = e.v1ViewTypeHierarchy,
+      clientEventNamespace = Some(
+        ClientEventNamespace(
+          page = e.page,
+          section = e.section,
+          element = e.element,
+          action = e.actionName,
+          subsection = e.subsection
+        )),
+      breadcrumbViews = e.v1BreadcrumbViewTypeHierarchy,
+      breadcrumbTweets = e.v1BreadcrumbTweetIds.map { breadcrumbs =>
+        breadcrumbs.map { breadcrumb =>
+          BreadcrumbTweet(
+            tweetId = breadcrumb.serversideContextId.toLong,
+            sourceComponent = breadcrumb.sourceComponent)
+        }
+      }
+    )
+
+  protected def getBreadcrumbTweetIds(
+    breadcrumbTweetIds: Option[Seq[FlattenedServersideContextKey]]
+  ): Seq[BreadcrumbTweet] =
+    breadcrumbTweetIds
+      .getOrElse(Nil).map(breadcrumb => {
+        BreadcrumbTweet(
+          tweetId = breadcrumb.serversideContextId.toLong,
+          sourceComponent = breadcrumb.sourceComponent)
+      })
+
+  protected def getBreadcrumbViews(breadcrumbView: Option[Seq[String]]): Seq[String] =
+    breadcrumbView.getOrElse(Nil)
+
+  protected def getUnifiedUserAction(
+    event: FlattenedEventLog,
+    actionType: ActionType,
+    item: Item,
+    productSurface: Option[ProductSurface] = None,
+    productSurfaceInfo: Option[ProductSurfaceInfo] = None
+  ): UnifiedUserAction =
+    UnifiedUserAction(
+      userIdentifier = getUserIdentifier(event.context),
+      actionType = actionType,
+      item = item,
+      eventMetadata = getEventMetadata(event),
+      productSurface = productSurface,
+      productSurfaceInfo = productSurfaceInfo
+    )
+}

unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/behavioral_client_event/BehavioralClientEventAdapter.scala

@@ -0,0 +1,39 @@
+package com.twitter.unified_user_actions.adapter.behavioral_client_event
+
+import com.twitter.finagle.stats.NullStatsReceiver
+import com.twitter.finagle.stats.StatsReceiver
+import com.twitter.finatra.kafka.serde.UnKeyed
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.adapter.AbstractAdapter
+import com.twitter.unified_user_actions.thriftscala._
+
+class BehavioralClientEventAdapter
+    extends AbstractAdapter[FlattenedEventLog, UnKeyed, UnifiedUserAction] {
+
+  import BehavioralClientEventAdapter._
+
+  override def adaptOneToKeyedMany(
+    input: FlattenedEventLog,
+    statsReceiver: StatsReceiver = NullStatsReceiver
+  ): Seq[(UnKeyed, UnifiedUserAction)] =
+    adaptEvent(input).map { e => (UnKeyed, e) }
+}
+
+object BehavioralClientEventAdapter {
+  def adaptEvent(e: FlattenedEventLog): Seq[UnifiedUserAction] =
+    // See go/bcecoverage for event namespaces, usage and coverage details
+    Option(e)
+      .map { e =>
+        (e.page, e.actionName) match {
+          case (Some("tweet_details"), Some("impress")) =>
+            TweetImpressionBCEAdapter.TweetDetails.toUUA(e)
+          case (Some("fullscreen_video"), Some("impress")) =>
+            TweetImpressionBCEAdapter.FullscreenVideo.toUUA(e)
+          case (Some("fullscreen_image"), Some("impress")) =>
+            TweetImpressionBCEAdapter.FullscreenImage.toUUA(e)
+          case (Some("profile"), Some("impress")) =>
+            ProfileImpressionBCEAdapter.Profile.toUUA(e)
+          case _ => Nil
+        }
+      }.getOrElse(Nil)
+}

unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/behavioral_client_event/ImpressionBCEAdapter.scala

@@ -0,0 +1,34 @@
+package com.twitter.unified_user_actions.adapter.behavioral_client_event
+
+import com.twitter.client.behavioral_event.action.impress.latest.thriftscala.Impress
+import com.twitter.client_event_entities.serverside_context_key.latest.thriftscala.FlattenedServersideContextKey
+import com.twitter.unified_user_actions.thriftscala.Item
+
+trait ImpressionBCEAdapter extends BaseBCEAdapter {
+  type ImpressedItem <: Item
+
+  def getImpressedItem(
+    context: FlattenedServersideContextKey,
+    impression: Impress
+  ): ImpressedItem
+
+  /**
+   * The start time of an impression in milliseconds since epoch. In BCE, the impression
+   * tracking clock will start immediately after the page is visible with no initial delay.
+   */
+  def getImpressedStartTimestamp(impression: Impress): Long =
+    impression.visibilityPctDwellStartMs
+
+  /**
+   * The end time of an impression in milliseconds since epoch. In BCE, the impression
+   * tracking clock will end before the user exit the page.
+   */
+  def getImpressedEndTimestamp(impression: Impress): Long =
+    impression.visibilityPctDwellEndMs
+
+  /**
+   * The UI component that hosted the impressed item.
+   */
+  def getImpressedUISourceComponent(context: FlattenedServersideContextKey): String =
+    context.sourceComponent
+}

unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/behavioral_client_event/ProfileImpressionBCEAdapter.scala

@@ -0,0 +1,52 @@
+package com.twitter.unified_user_actions.adapter.behavioral_client_event
+
+import com.twitter.client.behavioral_event.action.impress.latest.thriftscala.Impress
+import com.twitter.client_event_entities.serverside_context_key.latest.thriftscala.FlattenedServersideContextKey
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.thriftscala.ActionType
+import com.twitter.unified_user_actions.thriftscala.ClientProfileV2Impression
+import com.twitter.unified_user_actions.thriftscala.Item
+import com.twitter.unified_user_actions.thriftscala.ProductSurface
+import com.twitter.unified_user_actions.thriftscala.ProfileActionInfo
+import com.twitter.unified_user_actions.thriftscala.ProfileInfo
+import com.twitter.unified_user_actions.thriftscala.UnifiedUserAction
+
+object ProfileImpressionBCEAdapter {
+  val Profile = new ProfileImpressionBCEAdapter()
+}
+
+class ProfileImpressionBCEAdapter extends ImpressionBCEAdapter {
+  override type ImpressedItem = Item.ProfileInfo
+
+  override def toUUA(e: FlattenedEventLog): Seq[UnifiedUserAction] =
+    (e.v2Impress, e.v1UserIds) match {
+      case (Some(v2Impress), Some(v1UserIds)) =>
+        v1UserIds.map { user =>
+          getUnifiedUserAction(
+            event = e,
+            actionType = ActionType.ClientProfileV2Impression,
+            item = getImpressedItem(user, v2Impress),
+            productSurface = Some(ProductSurface.ProfilePage)
+          )
+        }
+      case _ => Nil
+    }
+
+  override def getImpressedItem(
+    context: FlattenedServersideContextKey,
+    impression: Impress
+  ): ImpressedItem =
+    Item.ProfileInfo(
+      ProfileInfo(
+        actionProfileId = context.serversideContextId.toLong,
+        profileActionInfo = Some(
+          ProfileActionInfo.ClientProfileV2Impression(
+            ClientProfileV2Impression(
+              impressStartTimestampMs = getImpressedStartTimestamp(impression),
+              impressEndTimestampMs = getImpressedEndTimestamp(impression),
+              sourceComponent = getImpressedUISourceComponent(context)
+            )
+          )
+        )
+      ))
+}

unified_user_actions/adapter/src/main/scala/com/twitter/unified_user_actions/adapter/behavioral_client_event/TweetImpressionBCEAdapter.scala

@@ -0,0 +1,84 @@
+package com.twitter.unified_user_actions.adapter.behavioral_client_event
+
+import com.twitter.client.behavioral_event.action.impress.latest.thriftscala.Impress
+import com.twitter.client_event_entities.serverside_context_key.latest.thriftscala.FlattenedServersideContextKey
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.thriftscala.ActionType
+import com.twitter.unified_user_actions.thriftscala.ClientTweetV2Impression
+import com.twitter.unified_user_actions.thriftscala.Item
+import com.twitter.unified_user_actions.thriftscala.ProductSurface
+import com.twitter.unified_user_actions.thriftscala.TweetActionInfo
+import com.twitter.unified_user_actions.thriftscala.TweetInfo
+import com.twitter.unified_user_actions.thriftscala.UnifiedUserAction
+
+object TweetImpressionBCEAdapter {
+  val TweetDetails = new TweetImpressionBCEAdapter(ActionType.ClientTweetV2Impression)
+  val FullscreenVideo = new TweetImpressionBCEAdapter(
+    ActionType.ClientTweetVideoFullscreenV2Impression)
+  val FullscreenImage = new TweetImpressionBCEAdapter(
+    ActionType.ClientTweetImageFullscreenV2Impression)
+}
+
+class TweetImpressionBCEAdapter(actionType: ActionType) extends ImpressionBCEAdapter {
+  override type ImpressedItem = Item.TweetInfo
+
+  override def toUUA(e: FlattenedEventLog): Seq[UnifiedUserAction] =
+    (actionType, e.v2Impress, e.v1TweetIds, e.v1BreadcrumbTweetIds) match {
+      case (ActionType.ClientTweetV2Impression, Some(v2Impress), Some(v1TweetIds), _) =>
+        toUUAEvents(e, v2Impress, v1TweetIds)
+      case (
+            ActionType.ClientTweetVideoFullscreenV2Impression,
+            Some(v2Impress),
+            _,
+            Some(v1BreadcrumbTweetIds)) =>
+        toUUAEvents(e, v2Impress, v1BreadcrumbTweetIds)
+      case (
+            ActionType.ClientTweetImageFullscreenV2Impression,
+            Some(v2Impress),
+            _,
+            Some(v1BreadcrumbTweetIds)) =>
+        toUUAEvents(e, v2Impress, v1BreadcrumbTweetIds)
+      case _ => Nil
+    }
+
+  private def toUUAEvents(
+    e: FlattenedEventLog,
+    v2Impress: Impress,
+    v1TweetIds: Seq[FlattenedServersideContextKey]
+  ): Seq[UnifiedUserAction] =
+    v1TweetIds.map { tweet =>
+      getUnifiedUserAction(
+        event = e,
+        actionType = actionType,
+        item = getImpressedItem(tweet, v2Impress),
+        productSurface = getProductSurfaceRelated.productSurface,
+        productSurfaceInfo = getProductSurfaceRelated.productSurfaceInfo
+      )
+    }
+
+  override def getImpressedItem(
+    context: FlattenedServersideContextKey,
+    impression: Impress
+  ): ImpressedItem =
+    Item.TweetInfo(
+      TweetInfo(
+        actionTweetId = context.serversideContextId.toLong,
+        tweetActionInfo = Some(
+          TweetActionInfo.ClientTweetV2Impression(
+            ClientTweetV2Impression(
+              impressStartTimestampMs = getImpressedStartTimestamp(impression),
+              impressEndTimestampMs = getImpressedEndTimestamp(impression),
+              sourceComponent = getImpressedUISourceComponent(context)
+            )
+          ))
+      ))
+
+  private def getProductSurfaceRelated: ProductSurfaceRelated =
+    actionType match {
+      case ActionType.ClientTweetV2Impression =>
+        ProductSurfaceRelated(
+          productSurface = Some(ProductSurface.TweetDetailsPage),
+          productSurfaceInfo = None)
+      case _ => ProductSurfaceRelated(productSurface = None, productSurfaceInfo = None)
+    }
+}

unified_user_actions/adapter/src/test/scala/com/twitter/unified_user_actions/adapter/BehavioralClientEventAdapterSpec.scala

@@ -0,0 +1,139 @@
+package com.twitter.unified_user_actions.adapter
+
+import com.twitter.inject.Test
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.adapter.TestFixtures.BCEFixture
+import com.twitter.unified_user_actions.adapter.behavioral_client_event.BehavioralClientEventAdapter
+import com.twitter.unified_user_actions.thriftscala._
+import com.twitter.util.Time
+import org.scalatest.prop.TableDrivenPropertyChecks
+
+class BehavioralClientEventAdapterSpec extends Test with TableDrivenPropertyChecks {
+
+  test("basic event conversion should be correct") {
+    new BCEFixture {
+      Time.withTimeAt(frozenTime) { _ =>
+        val tests = Table(
+          ("event", "expected", "description"),
+          (
+            makeBCEEvent(),
+            makeUUAImpressEvent(productSurface = Some(ProductSurface.TweetDetailsPage)),
+            "tweet_details conversion"),
+          (makeBCEProfileImpressEvent(), makeUUAProfileImpressEvent(), "profile conversion"),
+          (
+            makeBCEVideoFullscreenImpressEvent(),
+            makeUUAVideoFullscreenImpressEvent(),
+            "fullscreen_video conversion"),
+          (
+            makeBCEImageFullscreenImpressEvent(),
+            makeUUAImageFullscreenImpressEvent(),
+            "fullscreen_image conversion"),
+        )
+        forEvery(tests) { (input: FlattenedEventLog, expected: UnifiedUserAction, desc: String) =>
+          assert(Seq(expected) === BehavioralClientEventAdapter.adaptEvent(input), desc)
+        }
+      }
+    }
+  }
+
+  test(
+    "tweet_details is NOT missing productSurface[Info] when empty breadcrumb components and breadcrumbs tweets id") {
+    new BCEFixture {
+      Time.withTimeAt(frozenTime) { _ =>
+        val input = makeBCEEvent(v1BreadcrumbViewTypeHierarchy = None, v1BreadcrumbTweetIds = None)
+        val expected =
+          makeUUAImpressEvent(
+            productSurface = Some(ProductSurface.TweetDetailsPage),
+            breadcrumbViews = None,
+            breadcrumbTweets = None)
+        val actual = BehavioralClientEventAdapter.adaptEvent(input)
+
+        assert(Seq(expected) === actual)
+      }
+    }
+  }
+
+  test("tweet_details is not missing productSurface[Info] when only breadcrumb tweets is empty") {
+    new BCEFixture {
+      Time.withTimeAt(frozenTime) { _ =>
+        val input = makeBCEEvent(v1BreadcrumbTweetIds = None)
+        val expected = makeUUAImpressEvent(
+          productSurface = Some(ProductSurface.TweetDetailsPage),
+          breadcrumbViews = Some(viewBreadcrumbs),
+          breadcrumbTweets = None
+        )
+        val actual = BehavioralClientEventAdapter.adaptEvent(input)
+
+        assert(Seq(expected) === actual)
+      }
+    }
+  }
+
+  test("unsupported events should be skipped") {
+    new BCEFixture {
+      val unsupportedPage = "unsupported_page"
+      val unsupportedAction = "unsupported_action"
+      val supportedNamespaces = Table(
+        ("page", "actions"),
+        ("tweet_details", Seq("impress")),
+        ("profile", Seq("impress")),
+      )
+
+      forAll(supportedNamespaces) { (page: String, actions: Seq[String]) =>
+        actions.foreach { supportedAction =>
+          assert(
+            BehavioralClientEventAdapter
+              .adaptEvent(
+                makeBCEEvent(
+                  currentPage = Some(unsupportedPage),
+                  actionName = Some(supportedAction))).isEmpty)
+
+          assert(BehavioralClientEventAdapter
+            .adaptEvent(
+              makeBCEEvent(currentPage = Some(page), actionName = Some(unsupportedAction))).isEmpty)
+        }
+      }
+    }
+  }
+
+  test("event w/ missing info should be skipped") {
+    new BCEFixture {
+      val eventsWithMissingInfo = Table(
+        ("event", "description"),
+        (null.asInstanceOf[FlattenedEventLog], "null event"),
+        (makeBCEEvent(v2Impress = None), "impression event missing v2Impress"),
+        (makeBCEEvent(v1TweetIds = None), "tweet event missing v1TweetIds"),
+        (makeBCEProfileImpressEvent(v1UserIds = None), "profile event missing v1UserIds"),
+        (
+          makeBCEVideoFullscreenImpressEvent(v1BreadcrumbTweetIds = None),
+          "fullscreen_video event missing v1BreadcrumbTweetIds"),
+        (
+          makeBCEImageFullscreenImpressEvent(v1BreadcrumbTweetIds = None),
+          "fullscreen_image event missing v1BreadcrumbTweetIds"),
+      )
+
+      forEvery(eventsWithMissingInfo) { (event: FlattenedEventLog, desc: String) =>
+        assert(
+          BehavioralClientEventAdapter
+            .adaptEvent(event).isEmpty,
+          desc)
+      }
+    }
+  }
+
+  test("use eventCreateAtMs when driftAdjustedTimetampMs is empty") {
+    new BCEFixture {
+      Time.withTimeAt(frozenTime) { _ =>
+        val input = makeBCEEvent(
+          context = makeBCEContext(driftAdjustedEventCreatedAtMs = None)
+        )
+        val expected = makeUUAImpressEvent(
+          createTs = eventCreatedTime,
+          productSurface = Some(ProductSurface.TweetDetailsPage))
+        val actual = BehavioralClientEventAdapter.adaptEvent(input)
+
+        assert(Seq(expected) === actual)
+      }
+    }
+  }
+}

unified_user_actions/service/src/main/scala/com/twitter/unified_user_actions/service/BehavioralClientEventService.scala

@@ -0,0 +1,25 @@
+package com.twitter.unified_user_actions.service;
+
+import com.twitter.finatra.decider.modules.DeciderModule
+import com.twitter.finatra.kafka.serde.UnKeyed
+import com.twitter.inject.server.TwitterServer
+import com.twitter.kafka.client.processor.AtLeastOnceProcessor
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.service.module.KafkaProcessorBehavioralClientEventModule
+
+object BehavioralClientEventServiceMain extends BehavioralClientEventService
+
+class BehavioralClientEventService extends TwitterServer {
+  override val modules = Seq(
+    KafkaProcessorBehavioralClientEventModule,
+    DeciderModule
+  )
+
+  override protected def setup(): Unit = {}
+
+  override protected def start(): Unit = {
+    val processor = injector.instance[AtLeastOnceProcessor[UnKeyed, FlattenedEventLog]]
+    closeOnExit(processor)
+    processor.start()
+  }
+}

unified_user_actions/service/src/main/scala/com/twitter/unified_user_actions/service/module/KafkaProcessorBehavioralClientEventModule.scala

@@ -0,0 +1,87 @@
+package com.twitter.unified_user_actions.service.module
+
+import com.google.inject.Provides
+import com.twitter.decider.Decider
+import com.twitter.finagle.stats.StatsReceiver
+import com.twitter.finatra.kafka.serde.UnKeyed
+import com.twitter.finatra.kafka.serde.UnKeyedSerde
+import com.twitter.inject.annotations.Flag
+import com.twitter.inject.TwitterModule
+import com.twitter.kafka.client.processor.AtLeastOnceProcessor
+import com.twitter.storage.behavioral_event.thriftscala.FlattenedEventLog
+import com.twitter.unified_user_actions.adapter.behavioral_client_event.BehavioralClientEventAdapter
+import com.twitter.unified_user_actions.kafka.CompressionTypeFlag
+import com.twitter.unified_user_actions.kafka.serde.NullableScalaSerdes
+import com.twitter.util.Duration
+import com.twitter.util.StorageUnit
+import com.twitter.util.logging.Logging
+import javax.inject.Singleton
+
+object KafkaProcessorBehavioralClientEventModule extends TwitterModule with Logging {
+  override def modules = Seq(FlagsModule)
+
+  private val adapter: BehavioralClientEventAdapter = new BehavioralClientEventAdapter
+  private final val processorName: String = "uuaProcessor"
+
+  @Provides
+  @Singleton
+  def providesKafkaProcessor(
+    decider: Decider,
+    @Flag(FlagsModule.cluster) cluster: String,
+    @Flag(FlagsModule.kafkaSourceCluster) kafkaSourceCluster: String,
+    @Flag(FlagsModule.kafkaDestCluster) kafkaDestCluster: String,
+    @Flag(FlagsModule.kafkaSourceTopic) kafkaSourceTopic: String,
+    @Flag(FlagsModule.kafkaSinkTopics) kafkaSinkTopics: Seq[String],
+    @Flag(FlagsModule.kafkaGroupId) kafkaGroupId: String,
+    @Flag(FlagsModule.kafkaProducerClientId) kafkaProducerClientId: String,
+    @Flag(FlagsModule.kafkaMaxPendingRequests) kafkaMaxPendingRequests: Int,
+    @Flag(FlagsModule.kafkaWorkerThreads) kafkaWorkerThreads: Int,
+    @Flag(FlagsModule.commitInterval) commitInterval: Duration,
+    @Flag(FlagsModule.maxPollRecords) maxPollRecords: Int,
+    @Flag(FlagsModule.maxPollInterval) maxPollInterval: Duration,
+    @Flag(FlagsModule.sessionTimeout) sessionTimeout: Duration,
+    @Flag(FlagsModule.fetchMax) fetchMax: StorageUnit,
+    @Flag(FlagsModule.batchSize) batchSize: StorageUnit,
+    @Flag(FlagsModule.linger) linger: Duration,
+    @Flag(FlagsModule.bufferMem) bufferMem: StorageUnit,
+    @Flag(FlagsModule.compressionType) compressionTypeFlag: CompressionTypeFlag,
+    @Flag(FlagsModule.retries) retries: Int,
+    @Flag(FlagsModule.retryBackoff) retryBackoff: Duration,
+    @Flag(FlagsModule.requestTimeout) requestTimeout: Duration,
+    @Flag(FlagsModule.enableTrustStore) enableTrustStore: Boolean,
+    @Flag(FlagsModule.trustStoreLocation) trustStoreLocation: String,
+    statsReceiver: StatsReceiver,
+  ): AtLeastOnceProcessor[UnKeyed, FlattenedEventLog] = {
+    KafkaProcessorProvider.provideDefaultAtLeastOnceProcessor(
+      name = processorName,
+      kafkaSourceCluster = kafkaSourceCluster,
+      kafkaGroupId = kafkaGroupId,
+      kafkaSourceTopic = kafkaSourceTopic,
+      sourceKeyDeserializer = UnKeyedSerde.deserializer,
+      sourceValueDeserializer = NullableScalaSerdes
+        .Thrift[FlattenedEventLog](statsReceiver.counter("deserializerErrors")).deserializer,
+      commitInterval = commitInterval,
+      maxPollRecords = maxPollRecords,
+      maxPollInterval = maxPollInterval,
+      sessionTimeout = sessionTimeout,
+      fetchMax = fetchMax,
+      processorMaxPendingRequests = kafkaMaxPendingRequests,
+      processorWorkerThreads = kafkaWorkerThreads,
+      adapter = adapter,
+      kafkaSinkTopics = kafkaSinkTopics,
+      kafkaDestCluster = kafkaDestCluster,
+      kafkaProducerClientId = kafkaProducerClientId,
+      batchSize = batchSize,
+      linger = linger,
+      bufferMem = bufferMem,
+      compressionType = compressionTypeFlag.compressionType,
+      retries = retries,
+      retryBackoff = retryBackoff,
+      requestTimeout = requestTimeout,
+      statsReceiver = statsReceiver,
+      trustStoreLocationOpt = if (enableTrustStore) Some(trustStoreLocation) else None,
+      decider = decider,
+      zone = ZoneFiltering.zoneMapping(cluster),
+    )
+  }
+}