tract_core/ops/array/
range.rs

1use crate::ops::cast::Cast;
2use tract_num_traits::AsPrimitive;
3use tract_num_traits::Zero;
4
5use crate::internal::*;
6
7use super::Slice;
8
9#[derive(Debug, Default, Clone, new, Hash)]
10pub struct Range {
11    len: TDim,
12}
13
14impl Op for Range {
15    fn name(&self) -> StaticName {
16        "Range".into()
17    }
18
19    op_as_typed_op!();
20}
21
22impl EvalOp for Range {
23    fn is_stateless(&self) -> bool {
24        true
25    }
26
27    fn eval_with_session(
28        &self,
29        _node_id: usize,
30        session: &SessionState,
31        inputs: TVec<TValue>,
32    ) -> TractResult<TVec<TValue>> {
33        let (start, end, step) = args_3!(inputs);
34        Ok(tvec!(self.make(&start, &end, &step, &session.resolved_symbols)?.into_tvalue()))
35    }
36}
37
38impl Range {
39    fn make_t<T: Datum + for<'a> std::ops::Add<&'a T, Output = T>>(
40        start: &Tensor,
41        step: &Tensor,
42        len: usize,
43    ) -> TractResult<Tensor> {
44        unsafe {
45            let mut result = Tensor::uninitialized::<T>(&[len])?;
46            let mut v = start.to_scalar::<T>()?.clone();
47            let step = step.to_scalar::<T>()?;
48            for i in 0..len {
49                result.as_slice_mut_unchecked::<T>()[i] = v.clone();
50                v = v + step;
51            }
52            Ok(result)
53        }
54    }
55
56    fn make(
57        &self,
58        start: &Tensor,
59        end: &Tensor,
60        step: &Tensor,
61        values: &SymbolValues,
62    ) -> TractResult<Tensor> {
63        if start.datum_type() == TDim::datum_type() {
64            let start = start.to_scalar::<TDim>()?.eval(values).to_i64()?;
65            let step = step.to_scalar::<TDim>()?.eval(values).to_i64()?;
66            let len = {
67                let end = end.to_scalar::<TDim>()?.eval(values).to_i64()?;
68                #[allow(clippy::cast_abs_to_unsigned)]
69                ((end - start).abs() as usize).divceil(step.abs() as usize)
70            };
71            Self::make_t::<i64>(&tensor0(start), &tensor0(step), len)
72        } else {
73            let len = dispatch_numbers!(Self::len_for_numbers(start.datum_type())(
74                self, start, end, step
75            ))?;
76            dispatch_numbers!(Self::make_t(start.datum_type())(start, step, len))
77        }
78    }
79
80    fn len_for_numbers<T: Datum + AsPrimitive<f64>>(
81        &self,
82        start: &Tensor,
83        end: &Tensor,
84        step: &Tensor,
85    ) -> TractResult<usize> {
86        let start = start.to_scalar::<T>()?;
87        let end = end.to_scalar::<T>()?;
88        let step = step.to_scalar::<T>()?;
89        Ok(((end.as_() - start.as_()) / (step.as_())).ceil() as usize)
90    }
91}
92
93impl TypedOp for Range {
94    fn declutter(
95        &self,
96        model: &TypedModel,
97        node: &TypedNode,
98    ) -> TractResult<Option<TypedModelPatch>> {
99        let Some(succ) = model.single_succ(node.id)? else { return Ok(None) };
100        let Some(slice) = succ.op_as::<Slice>() else { return Ok(None) };
101        if slice.start.is_zero() && slice.end.is_one() {
102            let mut patch = TypedModelPatch::default();
103            let mut wire = patch.tap_model(model, node.inputs[0])?;
104            if model.outlet_fact(node.inputs[0])?.datum_type.is_tdim() {
105                wire = patch.wire_node(
106                    format!("{}.cast-tdim", node.name),
107                    Cast { to: DatumType::I64 },
108                    &[wire],
109                )?[0];
110            }
111            let wire = patch.wire_node(&node.name, AxisOp::Add(0), &[wire])?;
112            patch.shunt_outside(model, succ.id.into(), wire[0])?;
113            return Ok(Some(patch));
114        }
115        Ok(None)
116    }
117
118    fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
119        let [start, end, step] = inputs else {
120            bail!("Expects three inputs");
121        };
122        ensure!(start.datum_type() == end.datum_type());
123        ensure!(start.datum_type() == step.datum_type());
124        ensure!(start.shape.volume().is_one());
125        ensure!(end.shape.volume().is_one());
126        ensure!(step.shape.volume().is_one());
127        if let (Some(start), Some(end), Some(step)) = (&start.konst, &end.konst, &step.konst) {
128            if start.datum_type() == TDim::datum_type() {
129                let start = start.to_scalar::<TDim>()?;
130                let end = end.to_scalar::<TDim>()?;
131                let step = step.cast_to_scalar::<i64>()?;
132                let len = if step < 0 {
133                    (start.clone() - end).divceil(-step as usize)
134                } else {
135                    (end.clone() - start).divceil(step as usize)
136                };
137                Ok(tvec!(DatumType::I64.fact([len])))
138            } else {
139                let len = dispatch_numbers!(Self::len_for_numbers(start.datum_type())(
140                    self, start, end, step
141                ))?
142                .to_dim();
143                Ok(tvec!(start.datum_type().fact([len])))
144            }
145        } else {
146            Ok(tvec!(start.datum_type.fact(std::slice::from_ref(&self.len))))
147        }
148    }
149
150    as_op!();
151}
tract_core/ops/array/range.rs

tract_core/ops/array/
range.rs
