use std::collections::HashMap;
use std::path::PathBuf;
use std::sync::Arc;
use ndarray::{Array, Array2, Array3, IxDyn};
use ort::environment::Environment;
use ort::session::{Input, Output};
use ort::tensor::{FromArray, InputTensor};
use ort::{GraphOptimizationLevel, SessionBuilder};
use crate::common::Device;
use crate::common::{apply_device, match_to_inputs};
use crate::error::{Error, Result};
use crate::{try_extract_to_f32, ORTSession};
pub struct Seq2SeqDecoderModel<'a> {
model_session: ORTSession<'a>,
token_type_support: bool,
}
impl<'a> Seq2SeqDecoderModel<'a> {
pub fn new_from_memory(
env: Arc<Environment>,
model_bytes: &'a [u8],
device: Device,
optimization_level: GraphOptimizationLevel,
) -> Result<Self> {
let mut session_builder = SessionBuilder::new(&env)?;
session_builder = apply_device(session_builder, device)?;
let session = session_builder
.with_optimization_level(optimization_level)?
.with_model_from_memory(model_bytes)?;
let token_type_support = Self::validate_signature(&session.inputs, &session.outputs)?;
Ok(Self {
model_session: ORTSession::InMemory(session),
token_type_support,
})
}
pub fn new_from_file(
env: Arc<Environment>,
model_path: PathBuf,
device: Device,
optimization_level: GraphOptimizationLevel,
) -> Result<Self> {
let mut session_builder = SessionBuilder::new(&env)?;
session_builder = apply_device(session_builder, device)?;
let session = session_builder
.with_optimization_level(optimization_level)?
.with_model_from_file(model_path)?;
let token_type_support = Self::validate_signature(&session.inputs, &session.outputs)?;
Ok(Self {
model_session: ORTSession::Owned(session),
token_type_support,
})
}
fn validate_signature(inputs: &Vec<Input>, outputs: &Vec<Output>) -> Result<bool> {
let token_type_support = inputs.iter().any(|input| input.name == "token_type_ids");
let past_values: Vec<String> = inputs
.iter()
.filter(|input| input.name.contains("past_key_values"))
.map(|input| input.name.to_string())
.collect();
if past_values.len() != 0 {
return Err(Error::OnnxIncorrectInputs {
message:
"The model supports past key values. Use Seq2SeqDecoderModelWithPKVs instead"
.to_string(),
expected: vec!["input_ids".to_string(), "attention_mask".to_string()],
actual: past_values,
});
}
if inputs
.iter()
.all(|inp| inp.name != "encoder_attention_mask")
|| inputs.iter().all(|inp| inp.name != "input_ids")
|| inputs.iter().all(|inp| inp.name != "encoder_hidden_states")
{
return Err(Error::OnnxIncorrectInputs {
message: "The model does not have the required inputs.".to_string(),
actual: inputs.iter().map(|inp| inp.name.to_string()).collect(),
expected: vec![
"encoder_attention_mask".to_string(),
"input_ids".to_string(),
"encoder_hidden_states".to_string(),
],
});
}
if outputs.iter().all(|output| output.name != "logits") {
return Err(Error::OnnxIncorrectOutputs {
message: "The model does not have a logits output.".to_string(),
actual: outputs.iter().map(|inp| inp.name.to_string()).collect(),
expected: vec!["logits".to_string()],
});
}
Ok(token_type_support)
}
pub fn forward(
&self,
input_ids: Array2<u32>,
encoder_last_hidden_state: Array3<f32>,
encoder_attention_mask: Option<Array2<u32>>,
) -> Result<Array3<f32>> {
let input_map =
self.prepare_input_map(input_ids, encoder_last_hidden_state, encoder_attention_mask)?;
let model = match &self.model_session {
ORTSession::Owned(model) => model,
ORTSession::InMemory(model) => model,
};
let input_tensor = match_to_inputs(&model.inputs, input_map)?;
let output_names: Vec<String> = model
.outputs
.iter()
.map(|output| output.name.to_string())
.collect();
let output_vec = model.run(input_tensor)?;
let mut output_map = HashMap::new();
for (name, tensor) in output_names.iter().zip(output_vec) {
let extracted = try_extract_to_f32(tensor)?;
let view = extracted.view();
let owned = view.to_owned();
let dimensionality = owned.into_dimensionality::<IxDyn>()?;
output_map.insert(name.to_string(), dimensionality);
}
let output_logit = output_map.remove("logits").unwrap();
Ok(output_logit.into_dimensionality()?)
}
fn prepare_input_map(
&self,
input_ids: Array2<u32>,
encoder_last_hidden_state: Array3<f32>,
encoder_attention_mask: Option<Array2<u32>>,
) -> Result<HashMap<String, InputTensor>> {
let encoder_attention_mask = if encoder_attention_mask.is_none() {
Array::ones((
encoder_last_hidden_state.shape()[0],
encoder_last_hidden_state.shape()[1],
))
} else {
encoder_attention_mask.unwrap()
};
let mut input_map = HashMap::<String, InputTensor>::new();
input_map.insert(
"input_ids".to_string(),
InputTensor::from_array(input_ids.into_dimensionality()?),
);
input_map.insert(
"encoder_attention_mask".to_string(),
InputTensor::from_array(encoder_attention_mask.into_dimensionality()?),
);
input_map.insert(
"encoder_hidden_states".to_string(),
InputTensor::from_array(encoder_last_hidden_state.into_dimensionality()?),
);
Ok(input_map)
}
pub fn get_token_type_support(&self) -> bool {
self.token_type_support
}
}
#[cfg(test)]
mod tests {
use crate::hf_hub::hf_hub_download;
use crate::seq2seq_encoder::Seq2SeqEncoderModel;
use super::*;
#[test]
fn test_model() {
let env = Environment::builder().build().unwrap();
let arc = env.into_arc();
let decoder_model = Seq2SeqDecoderModel::new_from_file(
arc.clone(),
hf_hub_download("optimum/t5-small", "decoder_model.onnx", None, None).unwrap(),
Device::CPU,
GraphOptimizationLevel::Level3,
)
.unwrap();
let encoder_model = Seq2SeqEncoderModel::new_from_file(
arc,
hf_hub_download("optimum/t5-small", "encoder_model.onnx", None, None).unwrap(),
Device::CPU,
GraphOptimizationLevel::Level3,
)
.unwrap();
let input = vec![0, 1, 23, 23, 23, 23, 23, 23, 23, 23];
let ndarray_input = Array2::<u32>::from_shape_vec((1, 10), input.clone()).unwrap();
let encoder_output = encoder_model
.forward(ndarray_input.clone(), None, None)
.unwrap();
let decoder_output = decoder_model
.forward(ndarray_input, encoder_output, None)
.unwrap();
println!("{:?}", decoder_output);
}
}