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Merge pull request #452 from pouriya/refactor-dr_transform 

Refactor `navi/dr_transform`
This commit is contained in:
dzhao 2023-04-04 11:06:35 -07:00 committed by GitHub
parent e8147d8e5f ee5e7fc18d
commit 36588c650e
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 136 additions and 170 deletions
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3
navi/dr_transform/src/all_config.rs
View File

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

293
navi/dr_transform/src/converter.rs
View File

@ -16,8 +16,7 @@ use segdense::util;
use thrift::protocol::{TBinaryInputProtocol, TSerializable}; use thrift::protocol::{TBinaryInputProtocol, TSerializable};
use thrift::transport::TBufferChannel; use thrift::transport::TBufferChannel;
use crate::{all_config}; use crate::{all_config, all_config::AllConfig};
use crate::all_config::AllConfig;
pub fn log_feature_match( pub fn log_feature_match(
dr: &DataRecord, dr: &DataRecord,
@ -27,26 +26,22 @@ pub fn log_feature_match(
// Note the following algorithm matches features from config using linear search. // Note the following algorithm matches features from config using linear search.
// Also the record source is MinDataRecord. This includes only binary and continous features for now. // Also the record source is MinDataRecord. This includes only binary and continous features for now.
for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap().into_iter() { for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap() {
debug!( debug!(
"{} - Continous Datarecord => Feature ID: {}, Feature value: {}", "{dr_type} - Continuous Datarecord => Feature ID: {feature_id}, Feature value: {feature_value}"
dr_type, feature_id, feature_value
); );
for input_feature in &seg_dense_config.cont.input_features { for input_feature in &seg_dense_config.cont.input_features {
if input_feature.feature_id == *feature_id { 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().into_iter() { for feature_id in dr.binary_features.as_ref().unwrap() {
debug!( debug!("{dr_type} - Binary Datarecord => Feature ID: {feature_id}");
"{} - Binary Datarecord => Feature ID: {}",
dr_type, feature_id
);
for input_feature in &seg_dense_config.binary.input_features { for input_feature in &seg_dense_config.binary.input_features {
if input_feature.feature_id == *feature_id { if input_feature.feature_id == *feature_id {
debug!("Found input feature: {:?}", input_feature) debug!("Found input feature: {input_feature:?}")
} }
} }
} }
@ -96,15 +91,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
reporting_feature_ids: Vec<(i64, &str)>, reporting_feature_ids: Vec<(i64, &str)>,
register_metric_fn: Option<impl Fn(&HistogramVec)>, register_metric_fn: Option<impl Fn(&HistogramVec)>,
) -> BatchPredictionRequestToTorchTensorConverter { ) -> BatchPredictionRequestToTorchTensorConverter {
let all_config_path = format!("{}/{}/all_config.json", model_dir, model_version); let all_config_path = format!("{model_dir}/{model_version}/all_config.json");
let seg_dense_config_path = format!( let seg_dense_config_path =
"{}/{}/segdense_transform_spec_home_recap_2022.json", format!("{model_dir}/{model_version}/segdense_transform_spec_home_recap_2022.json");
model_dir, model_version
);
let seg_dense_config = util::load_config(&seg_dense_config_path); let seg_dense_config = util::load_config(&seg_dense_config_path);
let all_config = all_config::parse( let all_config = all_config::parse(
&fs::read_to_string(&all_config_path) &fs::read_to_string(&all_config_path)
.unwrap_or_else(|error| panic!("error loading all_config.json - {}", error)), .unwrap_or_else(|error| panic!("error loading all_config.json - {error}")),
) )
.unwrap(); .unwrap();
@ -138,11 +131,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
let (discrete_feature_metrics, continuous_feature_metrics) = METRICS.get_or_init(|| { let (discrete_feature_metrics, continuous_feature_metrics) = METRICS.get_or_init(|| {
let discrete = HistogramVec::new( let discrete = HistogramVec::new(
HistogramOpts::new(":navi:feature_id:discrete", "Discrete Feature ID values") HistogramOpts::new(":navi:feature_id:discrete", "Discrete Feature ID values")
.buckets(Vec::from(&[ .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, 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, 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, 300.0, 500.0, 1000.0, 10000.0, 100000.0,
] as &'static [f64])), ])),
&["feature_id"], &["feature_id"],
) )
.expect("metric cannot be created"); .expect("metric cannot be created");
@ -151,18 +144,18 @@ impl BatchPredictionRequestToTorchTensorConverter {
":navi:feature_id:continuous", ":navi:feature_id:continuous",
"continuous Feature ID values", "continuous Feature ID values",
) )
.buckets(Vec::from(&[ .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, 0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0, 500.0, 120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0,
1000.0, 10000.0, 100000.0, 500.0, 1000.0, 10000.0, 100000.0,
] as &'static [f64])), ])),
&["feature_id"], &["feature_id"],
) )
.expect("metric cannot be created"); .expect("metric cannot be created");
register_metric_fn.map(|r| { if let Some(r) = register_metric_fn {
r(&discrete); r(&discrete);
r(&continuous); r(&continuous);
}); }
(discrete, continuous) (discrete, continuous)
}); });
@ -171,16 +164,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
for (feature_id, feature_type) in reporting_feature_ids.iter() { for (feature_id, feature_type) in reporting_feature_ids.iter() {
match *feature_type { match *feature_type {
"discrete" => discrete_features_to_report.insert(feature_id.clone()), "discrete" => discrete_features_to_report.insert(*feature_id),
"continuous" => continuous_features_to_report.insert(feature_id.clone()), "continuous" => continuous_features_to_report.insert(*feature_id),
_ => panic!( _ => panic!("Invalid feature type {feature_type} for reporting metrics!"),
"Invalid feature type {} for reporting metrics!",
feature_type
),
}; };
} }
return BatchPredictionRequestToTorchTensorConverter { BatchPredictionRequestToTorchTensorConverter {
all_config, all_config,
seg_dense_config, seg_dense_config,
all_config_path, all_config_path,
@ -193,7 +183,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
continuous_features_to_report, continuous_features_to_report,
discrete_feature_metrics, discrete_feature_metrics,
continuous_feature_metrics, continuous_feature_metrics,
}; }
} }
fn get_feature_id(feature_name: &str, seg_dense_config: &Root) -> i64 { fn get_feature_id(feature_name: &str, seg_dense_config: &Root) -> i64 {
@ -203,7 +193,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
return feature.feature_id; return feature.feature_id;
} }
} }
return -1; -1
} }
fn parse_batch_prediction_request(bytes: Vec<u8>) -> BatchPredictionRequest { fn parse_batch_prediction_request(bytes: Vec<u8>) -> BatchPredictionRequest {
@ -211,7 +201,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
let mut bc = TBufferChannel::with_capacity(bytes.len(), 0); let mut bc = TBufferChannel::with_capacity(bytes.len(), 0);
bc.set_readable_bytes(&bytes); bc.set_readable_bytes(&bytes);
let mut protocol = TBinaryInputProtocol::new(bc, true); let mut protocol = TBinaryInputProtocol::new(bc, true);
return BatchPredictionRequest::read_from_in_protocol(&mut protocol).unwrap(); BatchPredictionRequest::read_from_in_protocol(&mut protocol).unwrap()
} }
fn get_embedding_tensors( fn get_embedding_tensors(
@ -228,45 +218,43 @@ impl BatchPredictionRequestToTorchTensorConverter {
let mut working_set = vec![0 as f32; total_size]; let mut working_set = vec![0 as f32; total_size];
let mut bpr_start = 0; let mut bpr_start = 0;
for (bpr, &bpr_end) in bprs.iter().zip(batch_size) { for (bpr, &bpr_end) in bprs.iter().zip(batch_size) {
if bpr.common_features.is_some() { if bpr.common_features.is_some()
if bpr.common_features.as_ref().unwrap().tensors.is_some() { && bpr.common_features.as_ref().unwrap().tensors.is_some()
if bpr && bpr
.common_features .common_features
.as_ref() .as_ref()
.unwrap() .unwrap()
.tensors .tensors
.as_ref() .as_ref()
.unwrap() .unwrap()
.contains_key(&feature_id) .contains_key(&feature_id)
{
let source_tensor = bpr
.common_features
.as_ref()
.unwrap()
.tensors
.as_ref()
.unwrap()
.get(&feature_id)
.unwrap();
let tensor = match source_tensor {
GeneralTensor::FloatTensor(float_tensor) =>
//Tensor::of_slice(
{ {
let source_tensor = bpr float_tensor
.common_features .floats
.as_ref() .iter()
.unwrap() .map(|x| x.into_inner() as f32)
.tensors .collect::<Vec<_>>()
.as_ref() }
.unwrap() _ => vec![0 as f32; cols],
.get(&feature_id) };
.unwrap();
let tensor = match source_tensor {
GeneralTensor::FloatTensor(float_tensor) =>
//Tensor::of_slice(
{
float_tensor
.floats
.iter()
.map(|x| x.into_inner() as f32)
.collect::<Vec<_>>()
}
_ => vec![0 as f32; cols],
};
// since the tensor is found in common feature, add it in all batches // since the tensor is found in common feature, add it in all batches
for row in bpr_start..bpr_end { for row in bpr_start..bpr_end {
for col in 0..cols { for col in 0..cols {
working_set[row * cols + col] = tensor[col]; working_set[row * cols + col] = tensor[col];
}
}
} }
} }
} }
@ -300,7 +288,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
} }
bpr_start = bpr_end; bpr_start = bpr_end;
} }
return Array2::<f32>::from_shape_vec([rows, cols], working_set).unwrap(); Array2::<f32>::from_shape_vec([rows, cols], working_set).unwrap()
} }
// Todo : Refactor, create a generic version with different type and field accessors // Todo : Refactor, create a generic version with different type and field accessors
@ -310,9 +298,9 @@ impl BatchPredictionRequestToTorchTensorConverter {
// (INT64 --> INT64, DataRecord.discrete_feature) // (INT64 --> INT64, DataRecord.discrete_feature)
fn get_continuous(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor { fn get_continuous(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
// These need to be part of model schema // These need to be part of model schema
let rows: usize = batch_ends[batch_ends.len() - 1]; let rows = batch_ends[batch_ends.len() - 1];
let cols: usize = 5293; let cols = 5293;
let full_size: usize = (rows * cols).try_into().unwrap(); let full_size = rows * cols;
let default_val = f32::NAN; let default_val = f32::NAN;
let mut tensor = vec![default_val; full_size]; let mut tensor = vec![default_val; full_size];
@ -337,55 +325,48 @@ impl BatchPredictionRequestToTorchTensorConverter {
.unwrap(); .unwrap();
for feature in common_features { for feature in common_features {
match self.feature_mapper.get(feature.0) { if let Some(f_info) = self.feature_mapper.get(feature.0) {
Some(f_info) => { let idx = f_info.index_within_tensor as usize;
let idx = f_info.index_within_tensor as usize; if idx < cols {
if idx < cols { // Set value in each row
// Set value in each row for r in bpr_start..bpr_end {
for r in bpr_start..bpr_end { let flat_index = r * cols + idx;
let flat_index: usize = (r * cols + idx).try_into().unwrap(); tensor[flat_index] = feature.1.into_inner() as f32;
tensor[flat_index] = feature.1.into_inner() as f32;
}
} }
} }
None => (),
} }
if self.continuous_features_to_report.contains(feature.0) { if self.continuous_features_to_report.contains(feature.0) {
self.continuous_feature_metrics self.continuous_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()]) .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) { } else if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics self.discrete_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()]) .with_label_values(&[feature.0.to_string().as_str()])
.observe(feature.1.into_inner() as f64) .observe(feature.1.into_inner())
} }
} }
} }
// Process the batch of datarecords // Process the batch of datarecords
for r in bpr_start..bpr_end { for r in bpr_start..bpr_end {
let dr: &DataRecord = let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
&bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
if dr.continuous_features.is_some() { if dr.continuous_features.is_some() {
for feature in dr.continuous_features.as_ref().unwrap() { for feature in dr.continuous_features.as_ref().unwrap() {
match self.feature_mapper.get(&feature.0) { if let Some(f_info) = self.feature_mapper.get(feature.0) {
Some(f_info) => { let idx = f_info.index_within_tensor as usize;
let idx = f_info.index_within_tensor as usize; let flat_index = r * cols + idx;
let flat_index: usize = (r * cols + idx).try_into().unwrap(); if flat_index < tensor.len() && idx < cols {
if flat_index < tensor.len() && idx < cols { tensor[flat_index] = feature.1.into_inner() as f32;
tensor[flat_index] = feature.1.into_inner() as f32;
}
} }
None => (),
} }
if self.continuous_features_to_report.contains(feature.0) { if self.continuous_features_to_report.contains(feature.0) {
self.continuous_feature_metrics self.continuous_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()]) .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) { } else if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics self.discrete_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()]) .with_label_values(&[feature.0.to_string().as_str()])
.observe(feature.1.into_inner() as f64) .observe(feature.1.into_inner())
} }
} }
} }
@ -393,22 +374,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
bpr_start = bpr_end; bpr_start = bpr_end;
} }
return InputTensor::FloatTensor( InputTensor::FloatTensor(
Array2::<f32>::from_shape_vec( Array2::<f32>::from_shape_vec([rows, cols], tensor)
[rows.try_into().unwrap(), cols.try_into().unwrap()], .unwrap()
tensor, .into_dyn(),
) )
.unwrap()
.into_dyn(),
);
} }
fn get_binary(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor { fn get_binary(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
// These need to be part of model schema // These need to be part of model schema
let rows: usize = batch_ends[batch_ends.len() - 1]; let rows = batch_ends[batch_ends.len() - 1];
let cols: usize = 149; let cols = 149;
let full_size: usize = (rows * cols).try_into().unwrap(); let full_size = rows * cols;
let default_val: i64 = 0; let default_val = 0;
let mut v = vec![default_val; full_size]; let mut v = vec![default_val; full_size];
@ -432,55 +410,48 @@ impl BatchPredictionRequestToTorchTensorConverter {
.unwrap(); .unwrap();
for feature in common_features { for feature in common_features {
match self.feature_mapper.get(feature) { if let Some(f_info) = self.feature_mapper.get(feature) {
Some(f_info) => { let idx = f_info.index_within_tensor as usize;
let idx = f_info.index_within_tensor as usize; if idx < cols {
if idx < cols { // Set value in each row
// Set value in each row for r in bpr_start..bpr_end {
for r in bpr_start..bpr_end { let flat_index = r * cols + idx;
let flat_index: usize = (r * cols + idx).try_into().unwrap(); v[flat_index] = 1;
v[flat_index] = 1;
}
} }
} }
None => (),
} }
} }
} }
// Process the batch of datarecords // Process the batch of datarecords
for r in bpr_start..bpr_end { for r in bpr_start..bpr_end {
let dr: &DataRecord = let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
&bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
if dr.binary_features.is_some() { if dr.binary_features.is_some() {
for feature in dr.binary_features.as_ref().unwrap() { for feature in dr.binary_features.as_ref().unwrap() {
match self.feature_mapper.get(&feature) { if let Some(f_info) = self.feature_mapper.get(feature) {
Some(f_info) => { let idx = f_info.index_within_tensor as usize;
let idx = f_info.index_within_tensor as usize; let flat_index = r * cols + idx;
let flat_index: usize = (r * cols + idx).try_into().unwrap(); v[flat_index] = 1;
v[flat_index] = 1;
}
None => (),
} }
} }
} }
} }
bpr_start = bpr_end; bpr_start = bpr_end;
} }
return InputTensor::Int64Tensor( InputTensor::Int64Tensor(
Array2::<i64>::from_shape_vec([rows.try_into().unwrap(), cols.try_into().unwrap()], v) Array2::<i64>::from_shape_vec([rows, cols], v)
.unwrap() .unwrap()
.into_dyn(), .into_dyn(),
); )
} }
#[allow(dead_code)] #[allow(dead_code)]
fn get_discrete(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor { fn get_discrete(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
// These need to be part of model schema // These need to be part of model schema
let rows: usize = batch_ends[batch_ends.len() - 1]; let rows = batch_ends[batch_ends.len() - 1];
let cols: usize = 320; let cols = 320;
let full_size: usize = (rows * cols).try_into().unwrap(); let full_size = rows * cols;
let default_val: i64 = 0; let default_val = 0;
let mut v = vec![default_val; full_size]; let mut v = vec![default_val; full_size];
@ -504,18 +475,15 @@ impl BatchPredictionRequestToTorchTensorConverter {
.unwrap(); .unwrap();
for feature in common_features { for feature in common_features {
match self.feature_mapper.get(feature.0) { if let Some(f_info) = self.feature_mapper.get(feature.0) {
Some(f_info) => { let idx = f_info.index_within_tensor as usize;
let idx = f_info.index_within_tensor as usize; if idx < cols {
if idx < cols { // Set value in each row
// Set value in each row for r in bpr_start..bpr_end {
for r in bpr_start..bpr_end { let flat_index = r * cols + idx;
let flat_index: usize = (r * cols + idx).try_into().unwrap(); v[flat_index] = *feature.1;
v[flat_index] = *feature.1;
}
} }
} }
None => (),
} }
if self.discrete_features_to_report.contains(feature.0) { if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics self.discrete_feature_metrics
@ -527,18 +495,15 @@ impl BatchPredictionRequestToTorchTensorConverter {
// Process the batch of datarecords // Process the batch of datarecords
for r in bpr_start..bpr_end { 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() { if dr.discrete_features.is_some() {
for feature in dr.discrete_features.as_ref().unwrap() { for feature in dr.discrete_features.as_ref().unwrap() {
match self.feature_mapper.get(&feature.0) { if let Some(f_info) = self.feature_mapper.get(feature.0) {
Some(f_info) => { let idx = f_info.index_within_tensor as usize;
let idx = f_info.index_within_tensor as usize; let flat_index = r * cols + idx;
let flat_index: usize = (r * cols + idx).try_into().unwrap(); if flat_index < v.len() && idx < cols {
if flat_index < v.len() && idx < cols { v[flat_index] = *feature.1;
v[flat_index] = *feature.1;
}
} }
None => (),
} }
if self.discrete_features_to_report.contains(feature.0) { if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics self.discrete_feature_metrics
@ -550,11 +515,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
} }
bpr_start = bpr_end; bpr_start = bpr_end;
} }
return InputTensor::Int64Tensor( InputTensor::Int64Tensor(
Array2::<i64>::from_shape_vec([rows.try_into().unwrap(), cols.try_into().unwrap()], v) Array2::<i64>::from_shape_vec([rows, cols], v)
.unwrap() .unwrap()
.into_dyn(), .into_dyn(),
); )
} }
fn get_user_embedding( fn get_user_embedding(
@ -604,7 +569,7 @@ impl Converter for BatchPredictionRequestToTorchTensorConverter {
.map(|bpr| bpr.individual_features_list.len()) .map(|bpr| bpr.individual_features_list.len())
.scan(0usize, |acc, e| { .scan(0usize, |acc, e| {
//running total //running total
*acc = *acc + e; *acc += e;
Some(*acc) Some(*acc)
}) })
.collect::<Vec<_>>(); .collect::<Vec<_>>();

10
navi/dr_transform/src/util.rs
View File

@ -9,15 +9,17 @@ use std::{
pub fn load_batch_prediction_request_base64(file_name: &str) -> Vec<Vec<u8>> { pub fn load_batch_prediction_request_base64(file_name: &str) -> Vec<Vec<u8>> {
let file = File::open(file_name).expect("could not read file"); let file = File::open(file_name).expect("could not read file");
let mut result = vec![]; let mut result = vec![];
for line in io::BufReader::new(file).lines() { for (mut line_count, line) in io::BufReader::new(file).lines().enumerate() {
line_count += 1;
match base64::decode(line.unwrap().trim()) { match base64::decode(line.unwrap().trim()) {
Ok(payload) => result.push(payload), Ok(payload) => result.push(payload),
Err(err) => println!("error decoding line {}", err), Err(err) => println!("error decoding line {file_name}:{line_count} - {err}"),
} }
} }
println!("reslt len: {}", result.len()); println!("result len: {}", result.len());
return result; result
} }
pub fn save_to_npy<T: npyz::Serialize + AutoSerialize>(data: &[T], save_to: String) { pub fn save_to_npy<T: npyz::Serialize + AutoSerialize>(data: &[T], save_to: String) {
let mut writer = WriteOptions::new() let mut writer = WriteOptions::new()
.default_dtype() .default_dtype()