tract-tensorflow 0.3.3

Tiny, no-nonsense, self contained, TensorFlow and ONNX inference
Documentation 
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use ndarray::*;

use crate::tfpb::node_def::NodeDef;
use tract_core::internal::*;
use tract_core::ops::nn::sigmoid::sigmoid_f32;
use tract_core::ops::nn::tanh::tanh_f32;

pub fn block_lstm(node: &NodeDef) -> TractResult<Box<Op>> {
    let forget_bias = node.get_attr_opt_float("forget_bias")?.unwrap_or(1.0);
    let cell_clip = node.get_attr_opt_float("cell_clip")?.unwrap_or(3.0);
    let t = node.get_attr_datum_type("T")?;
    let use_peephole = node.get_attr_opt_bool("use_peephole")?.unwrap_or(false);
    Ok(Box::new(BlockLSTM::new(forget_bias, cell_clip, t, use_peephole)))
}

#[derive(Clone, Debug, new)]
pub struct BlockLSTM {
    forget_bias: f32,
    cell_clip: f32,
    t: DatumType,
    use_peephole: bool,
}

impl Op for BlockLSTM {
    fn name(&self) -> Cow<str> {
        "tf.BlockLSTM".into()
    }
}

impl StatelessOp for BlockLSTM {
    fn eval(&self, inputs: TVec<Arc<Tensor>>) -> TractResult<TVec<Arc<Tensor>>> {
        let len = *inputs[0].cast_to::<i32>()?.to_scalar::<i32>()? as usize;

        let x = inputs[1].to_array_view::<f32>()?.into_dimensionality::<Ix3>()?;
        let cell_size = x.shape()[2];
        let cs_prev = inputs[2].to_array_view::<f32>()?;
        let h_prev = inputs[3].to_array_view::<f32>()?.into_dimensionality::<Ix2>()?;
        let w = inputs[4].to_array_view::<f32>()?.into_dimensionality::<Ix2>()?;
        let bias = inputs[8].to_array_view::<f32>()?;

        let outputs_shape = x.shape();
        let mut i = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut cs = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut f = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut o = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut ci = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut co = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut h = unsafe { ArrayD::<f32>::uninitialized(&*outputs_shape) };
        let mut h_prev = h_prev.to_owned();
        let mut cs_prev = cs_prev.to_owned();

        /*
        dbg!(&h_prev);
        dbg!(&cs_prev);
        */

        for n in 0..len {
            let x = x.index_axis(Axis(0), n);
            let mut i = i.index_axis_mut(Axis(0), n);
            let mut cs = cs.index_axis_mut(Axis(0), n);
            let mut f = f.index_axis_mut(Axis(0), n);
            let mut o = o.index_axis_mut(Axis(0), n);
            let mut ci = ci.index_axis_mut(Axis(0), n);
            let mut co = co.index_axis_mut(Axis(0), n);
            let mut h = h.index_axis_mut(Axis(0), n);

            // dbg!(x);

            // println!("x: {:?}", x.iter().take(9).collect::<Vec<_>>());
            let xh = ndarray::stack(Axis(1), &[x, h_prev.view()])?;
            //            dbg!(&xh);

            let i_ci_f_o = xh.dot(&w) + &bias;
            //            dbg!(&i_ci_f_o);

            i.assign(&i_ci_f_o.slice_axis(Axis(1), (0..cell_size).into()));
            i.mapv_inplace(sigmoid_f32); // TODO: peepholes
                                         // dbg!(&i);

            //            println!("i: {:?}", i.iter().take(6).collect::<Vec<_>>());

            f.assign(&i_ci_f_o.slice_axis(Axis(1), (2 * cell_size..3 * cell_size).into()));
            f.mapv_inplace(|x| sigmoid_f32(x + self.forget_bias)); // TODO: peepholes
                                                                   //            println!("f: {:?}", f.iter().take(6).collect::<Vec<_>>());

            ci.assign(&i_ci_f_o.slice_axis(Axis(1), (cell_size..2 * cell_size).into()));
            ci.mapv_inplace(tanh_f32);
            //            println!("ci: {:?}", ci.iter().take(6).collect::<Vec<_>>());
            /*
                        dbg!(&f);
                        dbg!(&cs_prev);
                        dbg!(&i);
                        dbg!(&ci);
            */
            cs_prev *= &f;
            cs_prev += &(ci.to_owned() * &i);
            // TODO: clip cs
            cs.assign(&cs_prev);
            //            dbg!(&cs);

            o.assign(&i_ci_f_o.slice_axis(Axis(1), (3 * cell_size..4 * cell_size).into()));
            o.mapv_inplace(sigmoid_f32); // TODO: peephole
                                         // dbg!(&o);
                                         //            println!("o: {:?}", o.iter().take(6).collect::<Vec<_>>());

            co.assign(&cs);
            co.mapv_inplace(tanh_f32);
            //            println!("co: {:?}", co.iter().take(6).collect::<Vec<_>>());

            h_prev.assign(&co);
            h_prev *= &o;
            h.assign(&h_prev);
            // println!("h: {:?}", h.iter().take(6).collect::<Vec<_>>());
            // println!("cs: {:?}", cs.iter().take(6).collect::<Vec<_>>());
        }
        /*
        dbg!(&h_prev);
        dbg!(&cs_prev);
        */
        if x.shape()[0] > len as usize {
            i.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
            cs.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
            f.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
            o.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
            ci.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
            co.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
            h.slice_axis_mut(Axis(0), (len..).into()).fill(0.0);
        }
        Ok(tvec!(
            i.into_arc_tensor(),
            cs.into_arc_tensor(),
            f.into_arc_tensor(),
            o.into_arc_tensor(),
            ci.into_arc_tensor(),
            co.into_arc_tensor(),
            h.into_arc_tensor()
        ))
    }
}

impl InferenceRulesOp for BlockLSTM {
    fn rules<'r, 'p: 'r, 's: 'r>(
        &'s self,
        s: &mut Solver<'r>,
        inputs: &'p [TensorProxy],
        outputs: &'p [TensorProxy],
    ) -> InferenceResult {
        check_input_arity(&inputs, 9)?;
        check_input_arity(&outputs, 7)?;

        s.equals(&inputs[0].rank, 0)?; // seq_len_max
        s.equals(&inputs[0].datum_type, i64::datum_type())?;

        // other inputs and outputs are consistent float-like
        s.equals_all((1..=7).map(move |i| (&inputs[i].datum_type).bex()).collect())?;

        s.equals(&inputs[1].rank, 3)?; // x:  [ time, batch, cell_size ]
        s.equals(&inputs[2].rank, 2)?; // cs_prev: [batch, cell_size]
        s.equals(&inputs[3].rank, 2)?; // h_prev: [batch, cell_size]
        s.equals(&inputs[4].rank, 2)?; // w: []
        s.equals(&inputs[5].rank, 1)?; // peephole input
        s.equals(&inputs[6].rank, 1)?; // peephole forget
        s.equals(&inputs[7].rank, 1)?; // peephole output
        s.equals(&inputs[8].rank, 1)?; // bias: [ 4*cell_size ]
        s.equals(&inputs[8].shape[0], 4 * inputs[1].shape[2].bex())?; // bias: [ 4*cell_size ]

        // i, cs, f, o, ci, co, h
        for i in 0..7 {
            s.equals(&inputs[1].datum_type, &outputs[i].datum_type)?;
            s.equals(&outputs[i].shape, &inputs[1].shape)?;
        }

        Ok(())
    }
}