mirror of
https://github.com/twitter/the-algorithm.git
synced 2024-12-22 10:25:29 +00:00
ref(navi/dr_transform): fix clippy & formatting issues
This commit is contained in:
Pouriya Jahanbakhsh
parent
ec83d01dca
commit
2dbdfe173c
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@ -44,6 +44,5 @@ pub struct RenamedFeatures {
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}
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pub fn parse(json_str: &str) -> Result<AllConfig, Error> {
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let all_config: AllConfig = serde_json::from_str(json_str)?;
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return std::result::Result::Ok(all_config);
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serde_json::from_str(json_str)
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}
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@ -16,8 +16,7 @@ use segdense::util;
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use thrift::protocol::{TBinaryInputProtocol, TSerializable};
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use thrift::transport::TBufferChannel;
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use crate::{all_config};
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use crate::all_config::AllConfig;
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use crate::{all_config, all_config::AllConfig};
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pub fn log_feature_match(
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dr: &DataRecord,
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@ -27,26 +26,22 @@ pub fn log_feature_match(
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// Note the following algorithm matches features from config using linear search.
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// Also the record source is MinDataRecord. This includes only binary and continous features for now.
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for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap().into_iter() {
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for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap() {
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debug!(
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"{} - Continous Datarecord => Feature ID: {}, Feature value: {}",
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dr_type, feature_id, feature_value
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"{dr_type} - Continuous Datarecord => Feature ID: {feature_id}, Feature value: {feature_value}"
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);
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for input_feature in &seg_dense_config.cont.input_features {
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if input_feature.feature_id == *feature_id {
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debug!("Matching input feature: {:?}", input_feature)
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debug!("Matching input feature: {input_feature:?}")
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}
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}
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}
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for feature_id in dr.binary_features.as_ref().unwrap().into_iter() {
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debug!(
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"{} - Binary Datarecord => Feature ID: {}",
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dr_type, feature_id
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);
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for feature_id in dr.binary_features.as_ref().unwrap() {
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debug!("{dr_type} - Binary Datarecord => Feature ID: {feature_id}");
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for input_feature in &seg_dense_config.binary.input_features {
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if input_feature.feature_id == *feature_id {
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debug!("Found input feature: {:?}", input_feature)
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debug!("Found input feature: {input_feature:?}")
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}
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}
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}
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@ -96,15 +91,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
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reporting_feature_ids: Vec<(i64, &str)>,
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register_metric_fn: Option<impl Fn(&HistogramVec)>,
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) -> BatchPredictionRequestToTorchTensorConverter {
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let all_config_path = format!("{}/{}/all_config.json", model_dir, model_version);
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let seg_dense_config_path = format!(
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"{}/{}/segdense_transform_spec_home_recap_2022.json",
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model_dir, model_version
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);
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let all_config_path = format!("{model_dir}/{model_version}/all_config.json");
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let seg_dense_config_path =
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format!("{model_dir}/{model_version}/segdense_transform_spec_home_recap_2022.json");
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let seg_dense_config = util::load_config(&seg_dense_config_path);
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let all_config = all_config::parse(
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&fs::read_to_string(&all_config_path)
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.unwrap_or_else(|error| panic!("error loading all_config.json - {}", error)),
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.unwrap_or_else(|error| panic!("error loading all_config.json - {error}")),
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)
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.unwrap();
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@ -138,11 +131,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
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let (discrete_feature_metrics, continuous_feature_metrics) = METRICS.get_or_init(|| {
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let discrete = HistogramVec::new(
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HistogramOpts::new(":navi:feature_id:discrete", "Discrete Feature ID values")
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.buckets(Vec::from(&[
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0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
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.buckets(Vec::from([
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0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
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120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0,
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300.0, 500.0, 1000.0, 10000.0, 100000.0,
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] as &'static [f64])),
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])),
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&["feature_id"],
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)
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.expect("metric cannot be created");
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@ -151,18 +144,18 @@ impl BatchPredictionRequestToTorchTensorConverter {
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":navi:feature_id:continuous",
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"continuous Feature ID values",
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)
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.buckets(Vec::from(&[
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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,
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130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0, 500.0,
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1000.0, 10000.0, 100000.0,
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] as &'static [f64])),
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.buckets(Vec::from([
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0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
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120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0,
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500.0, 1000.0, 10000.0, 100000.0,
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])),
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&["feature_id"],
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)
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.expect("metric cannot be created");
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register_metric_fn.map(|r| {
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if let Some(r) = register_metric_fn {
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r(&discrete);
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r(&continuous);
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});
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}
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(discrete, continuous)
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});
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@ -171,16 +164,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
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for (feature_id, feature_type) in reporting_feature_ids.iter() {
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match *feature_type {
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"discrete" => discrete_features_to_report.insert(feature_id.clone()),
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"continuous" => continuous_features_to_report.insert(feature_id.clone()),
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_ => panic!(
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"Invalid feature type {} for reporting metrics!",
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feature_type
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),
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"discrete" => discrete_features_to_report.insert(*feature_id),
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"continuous" => continuous_features_to_report.insert(*feature_id),
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_ => panic!("Invalid feature type {feature_type} for reporting metrics!"),
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};
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}
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return BatchPredictionRequestToTorchTensorConverter {
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BatchPredictionRequestToTorchTensorConverter {
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all_config,
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seg_dense_config,
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all_config_path,
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@ -193,7 +183,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
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continuous_features_to_report,
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discrete_feature_metrics,
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continuous_feature_metrics,
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};
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}
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}
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fn get_feature_id(feature_name: &str, seg_dense_config: &Root) -> i64 {
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@ -203,7 +193,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
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return feature.feature_id;
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}
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}
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return -1;
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-1
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}
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fn parse_batch_prediction_request(bytes: Vec<u8>) -> BatchPredictionRequest {
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@ -211,7 +201,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
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let mut bc = TBufferChannel::with_capacity(bytes.len(), 0);
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bc.set_readable_bytes(&bytes);
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let mut protocol = TBinaryInputProtocol::new(bc, true);
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return BatchPredictionRequest::read_from_in_protocol(&mut protocol).unwrap();
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BatchPredictionRequest::read_from_in_protocol(&mut protocol).unwrap()
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}
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fn get_embedding_tensors(
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@ -228,45 +218,43 @@ impl BatchPredictionRequestToTorchTensorConverter {
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let mut working_set = vec![0 as f32; total_size];
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let mut bpr_start = 0;
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for (bpr, &bpr_end) in bprs.iter().zip(batch_size) {
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if bpr.common_features.is_some() {
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if bpr.common_features.as_ref().unwrap().tensors.is_some() {
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if bpr
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.common_features
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.as_ref()
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.unwrap()
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.tensors
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.as_ref()
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.unwrap()
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.contains_key(&feature_id)
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if bpr.common_features.is_some()
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&& bpr.common_features.as_ref().unwrap().tensors.is_some()
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&& bpr
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.common_features
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.as_ref()
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.unwrap()
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.tensors
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.as_ref()
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.unwrap()
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.contains_key(&feature_id)
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{
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let source_tensor = bpr
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.common_features
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.as_ref()
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.unwrap()
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.tensors
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.as_ref()
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.unwrap()
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.get(&feature_id)
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.unwrap();
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let tensor = match source_tensor {
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GeneralTensor::FloatTensor(float_tensor) =>
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//Tensor::of_slice(
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{
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let source_tensor = bpr
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.common_features
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.as_ref()
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.unwrap()
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.tensors
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.as_ref()
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.unwrap()
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.get(&feature_id)
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.unwrap();
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let tensor = match source_tensor {
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GeneralTensor::FloatTensor(float_tensor) =>
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//Tensor::of_slice(
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{
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float_tensor
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.floats
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.iter()
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.map(|x| x.into_inner() as f32)
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.collect::<Vec<_>>()
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}
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_ => vec![0 as f32; cols],
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};
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float_tensor
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.floats
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.iter()
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.map(|x| x.into_inner() as f32)
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.collect::<Vec<_>>()
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}
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_ => vec![0 as f32; cols],
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};
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// since the tensor is found in common feature, add it in all batches
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for row in bpr_start..bpr_end {
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for col in 0..cols {
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working_set[row * cols + col] = tensor[col];
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}
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}
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// since the tensor is found in common feature, add it in all batches
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for row in bpr_start..bpr_end {
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for col in 0..cols {
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working_set[row * cols + col] = tensor[col];
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}
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}
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}
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@ -300,7 +288,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
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}
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bpr_start = bpr_end;
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}
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return Array2::<f32>::from_shape_vec([rows, cols], working_set).unwrap();
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Array2::<f32>::from_shape_vec([rows, cols], working_set).unwrap()
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}
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// Todo : Refactor, create a generic version with different type and field accessors
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@ -310,9 +298,9 @@ impl BatchPredictionRequestToTorchTensorConverter {
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// (INT64 --> INT64, DataRecord.discrete_feature)
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fn get_continuous(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
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// These need to be part of model schema
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let rows: usize = batch_ends[batch_ends.len() - 1];
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let cols: usize = 5293;
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let full_size: usize = (rows * cols).try_into().unwrap();
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let rows = batch_ends[batch_ends.len() - 1];
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let cols = 5293;
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let full_size = rows * cols;
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let default_val = f32::NAN;
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let mut tensor = vec![default_val; full_size];
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@ -337,55 +325,48 @@ impl BatchPredictionRequestToTorchTensorConverter {
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.unwrap();
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for feature in common_features {
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match self.feature_mapper.get(feature.0) {
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Some(f_info) => {
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let idx = f_info.index_within_tensor as usize;
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if idx < cols {
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// Set value in each row
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for r in bpr_start..bpr_end {
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let flat_index: usize = (r * cols + idx).try_into().unwrap();
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tensor[flat_index] = feature.1.into_inner() as f32;
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}
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if let Some(f_info) = self.feature_mapper.get(feature.0) {
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let idx = f_info.index_within_tensor as usize;
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if idx < cols {
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// Set value in each row
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for r in bpr_start..bpr_end {
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let flat_index = r * cols + idx;
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tensor[flat_index] = feature.1.into_inner() as f32;
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}
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}
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None => (),
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}
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if self.continuous_features_to_report.contains(feature.0) {
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self.continuous_feature_metrics
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.with_label_values(&[feature.0.to_string().as_str()])
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.observe(feature.1.into_inner() as f64)
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.observe(feature.1.into_inner())
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} else if self.discrete_features_to_report.contains(feature.0) {
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self.discrete_feature_metrics
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.with_label_values(&[feature.0.to_string().as_str()])
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.observe(feature.1.into_inner() as f64)
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.observe(feature.1.into_inner())
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}
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}
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}
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// Process the batch of datarecords
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for r in bpr_start..bpr_end {
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let dr: &DataRecord =
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&bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
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let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
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if dr.continuous_features.is_some() {
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for feature in dr.continuous_features.as_ref().unwrap() {
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match self.feature_mapper.get(&feature.0) {
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Some(f_info) => {
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let idx = f_info.index_within_tensor as usize;
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let flat_index: usize = (r * cols + idx).try_into().unwrap();
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if flat_index < tensor.len() && idx < cols {
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tensor[flat_index] = feature.1.into_inner() as f32;
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}
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if let Some(f_info) = self.feature_mapper.get(feature.0) {
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let idx = f_info.index_within_tensor as usize;
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let flat_index = r * cols + idx;
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if flat_index < tensor.len() && idx < cols {
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tensor[flat_index] = feature.1.into_inner() as f32;
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}
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None => (),
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}
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if self.continuous_features_to_report.contains(feature.0) {
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self.continuous_feature_metrics
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.with_label_values(&[feature.0.to_string().as_str()])
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.observe(feature.1.into_inner() as f64)
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.observe(feature.1.into_inner())
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} else if self.discrete_features_to_report.contains(feature.0) {
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self.discrete_feature_metrics
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.with_label_values(&[feature.0.to_string().as_str()])
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.observe(feature.1.into_inner() as f64)
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.observe(feature.1.into_inner())
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}
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}
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}
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@ -393,22 +374,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
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bpr_start = bpr_end;
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}
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return InputTensor::FloatTensor(
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Array2::<f32>::from_shape_vec(
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[rows.try_into().unwrap(), cols.try_into().unwrap()],
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tensor,
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)
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.unwrap()
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.into_dyn(),
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);
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InputTensor::FloatTensor(
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Array2::<f32>::from_shape_vec([rows, cols], tensor)
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.unwrap()
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.into_dyn(),
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)
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}
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fn get_binary(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
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// These need to be part of model schema
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let rows: usize = batch_ends[batch_ends.len() - 1];
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let cols: usize = 149;
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let full_size: usize = (rows * cols).try_into().unwrap();
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let default_val: i64 = 0;
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let rows = batch_ends[batch_ends.len() - 1];
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let cols = 149;
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let full_size = rows * cols;
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let default_val = 0;
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let mut v = vec![default_val; full_size];
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|
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@ -432,55 +410,48 @@ impl BatchPredictionRequestToTorchTensorConverter {
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.unwrap();
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for feature in common_features {
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match self.feature_mapper.get(feature) {
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Some(f_info) => {
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let idx = f_info.index_within_tensor as usize;
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if idx < cols {
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// Set value in each row
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for r in bpr_start..bpr_end {
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let flat_index: usize = (r * cols + idx).try_into().unwrap();
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v[flat_index] = 1;
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}
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if let Some(f_info) = self.feature_mapper.get(feature) {
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let idx = f_info.index_within_tensor as usize;
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if idx < cols {
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// Set value in each row
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for r in bpr_start..bpr_end {
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let flat_index = r * cols + idx;
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v[flat_index] = 1;
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}
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}
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None => (),
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}
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}
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}
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// Process the batch of datarecords
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for r in bpr_start..bpr_end {
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let dr: &DataRecord =
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&bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
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let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
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if dr.binary_features.is_some() {
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for feature in dr.binary_features.as_ref().unwrap() {
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match self.feature_mapper.get(&feature) {
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Some(f_info) => {
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let idx = f_info.index_within_tensor as usize;
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let flat_index: usize = (r * cols + idx).try_into().unwrap();
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v[flat_index] = 1;
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}
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None => (),
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if let Some(f_info) = self.feature_mapper.get(feature) {
|
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let idx = f_info.index_within_tensor as usize;
|
||||
let flat_index = r * cols + idx;
|
||||
v[flat_index] = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
bpr_start = bpr_end;
|
||||
}
|
||||
return InputTensor::Int64Tensor(
|
||||
Array2::<i64>::from_shape_vec([rows.try_into().unwrap(), cols.try_into().unwrap()], v)
|
||||
InputTensor::Int64Tensor(
|
||||
Array2::<i64>::from_shape_vec([rows, cols], v)
|
||||
.unwrap()
|
||||
.into_dyn(),
|
||||
);
|
||||
)
|
||||
}
|
||||
|
||||
#[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).try_into().unwrap();
|
||||
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];
|
||||
|
||||
|
|
@ -504,18 +475,15 @@ impl BatchPredictionRequestToTorchTensorConverter {
|
|||
.unwrap();
|
||||
|
||||
for feature in common_features {
|
||||
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: usize = (r * cols + idx).try_into().unwrap();
|
||||
v[flat_index] = *feature.1;
|
||||
}
|
||||
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 = r * cols + idx;
|
||||
v[flat_index] = *feature.1;
|
||||
}
|
||||
}
|
||||
None => (),
|
||||
}
|
||||
if self.discrete_features_to_report.contains(feature.0) {
|
||||
self.discrete_feature_metrics
|
||||
|
|
@ -527,18 +495,15 @@ 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) => {
|
||||
let idx = f_info.index_within_tensor as usize;
|
||||
let flat_index: usize = (r * cols + idx).try_into().unwrap();
|
||||
if flat_index < v.len() && idx < cols {
|
||||
v[flat_index] = *feature.1;
|
||||
}
|
||||
if let Some(f_info) = self.feature_mapper.get(feature.0) {
|
||||
let idx = f_info.index_within_tensor as usize;
|
||||
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
|
||||
|
|
@ -550,11 +515,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
|
|||
}
|
||||
bpr_start = bpr_end;
|
||||
}
|
||||
return InputTensor::Int64Tensor(
|
||||
Array2::<i64>::from_shape_vec([rows.try_into().unwrap(), cols.try_into().unwrap()], v)
|
||||
InputTensor::Int64Tensor(
|
||||
Array2::<i64>::from_shape_vec([rows, cols], v)
|
||||
.unwrap()
|
||||
.into_dyn(),
|
||||
);
|
||||
)
|
||||
}
|
||||
|
||||
fn get_user_embedding(
|
||||
|
|
@ -604,7 +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<_>>();
|
||||
|
|
|
|||
|
|
@ -12,11 +12,11 @@ pub fn load_batch_prediction_request_base64(file_name: &str) -> Vec<Vec<u8>> {
|
|||
for line in io::BufReader::new(file).lines() {
|
||||
match base64::decode(line.unwrap().trim()) {
|
||||
Ok(payload) => result.push(payload),
|
||||
Err(err) => println!("error decoding line {}", err),
|
||||
Err(err) => println!("error decoding line {err}"),
|
||||
}
|
||||
}
|
||||
println!("reslt len: {}", result.len());
|
||||
return result;
|
||||
println!("result len: {}", result.len());
|
||||
return result
|
||||
}
|
||||
pub fn save_to_npy<T: npyz::Serialize + AutoSerialize>(data: &[T], save_to: String) {
|
||||
let mut writer = WriteOptions::new()
|
||||
|
|
|
|||
Loading…
Reference in a new issue