refactor: allow for negative stride downsample

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '20.2.5'
+release = '0.0.0'
 version = release
 
 

src/circuit/ops/layouts.rs

@@ -4914,7 +4914,7 @@ pub(crate) fn downsample<F: PrimeField + TensorType + PartialOrd + std::hash::Ha
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
     axis: &usize,
-    stride: &usize,
+    stride: &isize,
     modulo: &usize,
 ) -> Result<ValTensor<F>, CircuitError> {
     let input = region.assign(&config.custom_gates.inputs[0], &values[0])?;

src/circuit/ops/poly.rs

@@ -49,7 +49,7 @@ pub enum PolyOp {
     },
     Downsample {
         axis: usize,
-        stride: usize,
+        stride: isize,
         modulo: usize,
     },
     DeConv {
@@ -108,13 +108,8 @@ pub enum PolyOp {
 }
 
 impl<
-        F: PrimeField
-            + TensorType
-            + PartialOrd
-            + std::hash::Hash
-            + Serialize
-            + for<'de> Deserialize<'de>,
-    > Op<F> for PolyOp
+    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + Serialize + for<'de> Deserialize<'de>,
+> Op<F> for PolyOp
 {
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any {
@@ -188,7 +183,8 @@ impl<
             } => {
                 format!(
                     "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={}, data_format={:?}, kernel_format={:?})",
-                    stride, padding, output_padding, group, data_format, kernel_format)
+                    stride, padding, output_padding, group, data_format, kernel_format
+                )
             }
             PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
             PolyOp::Slice { axis, start, end } => {

src/graph/utilities.rs

@@ -1,14 +1,14 @@
-use super::errors::GraphError;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use super::VarScales;
+use super::errors::GraphError;
 use super::{Rescaled, SupportedOp, Visibility};
+use crate::circuit::Op;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::hybrid::HybridOp;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::lookup::LookupOp;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::poly::PolyOp;
-use crate::circuit::Op;
 use crate::fieldutils::IntegerRep;
 use crate::tensor::{Tensor, TensorError, TensorType};
 use halo2curves::bn256::Fr as Fp;
@@ -22,6 +22,7 @@ use std::sync::Arc;
 use tract_onnx::prelude::{DatumType, Node as OnnxNode, TypedFact, TypedOp};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_core::ops::{
+    Downsample,
     array::{
         Gather, GatherElements, GatherNd, MultiBroadcastTo, OneHot, ScatterElements, ScatterNd,
         Slice, Topk,
@@ -31,7 +32,6 @@ use tract_onnx::tract_core::ops::{
     einsum::EinSum,
     element_wise::ElementWiseOp,
     nn::{LeakyRelu, Reduce, Softmax},
-    Downsample,
 };
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_hir::{
@@ -1398,7 +1398,7 @@ pub fn new_op_from_onnx(
 
             SupportedOp::Linear(PolyOp::Downsample {
                 axis: downsample_node.axis,
-                stride: downsample_node.stride as usize,
+                stride: downsample_node.stride,
                 modulo: downsample_node.modulo,
             })
         }

src/tensor/ops.rs

@@ -535,30 +535,101 @@ pub fn mult<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::S
 /// let result = downsample(&x, 1, 2, 2).unwrap();
 /// let expected = Tensor::<IntegerRep>::new(Some(&[3, 6]), &[2, 1]).unwrap();
 /// assert_eq!(result, expected);
+/// let x = Tensor::<IntegerRep>::new(
+///     Some(&[1, 2, 3, 4, 5, 6]),
+///     &[2, 3],
+/// ).unwrap();
+///
+/// // Test case 1: Negative stride along dimension 0
+/// // This should flip the order along dimension 0
+/// let result = downsample(&x, 0, -1, 0).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(
+///     Some(&[4, 5, 6, 1, 2, 3]), // Flipped order of rows
+///     &[2, 3]
+/// ).unwrap();
+/// assert_eq!(result, expected);
+///
+/// // Test case 2: Negative stride along dimension 1
+/// // This should flip the order along dimension 1
+/// let result = downsample(&x, 1, -1, 0).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(
+///     Some(&[3, 2, 1, 6, 5, 4]), // Flipped order of columns
+///     &[2, 3]
+/// ).unwrap();
+/// assert_eq!(result, expected);
+///
+/// // Test case 3: Negative stride with stride magnitude > 1
+/// // This should both skip and flip
+/// let result = downsample(&x, 1, -2, 0).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(
+///     Some(&[3, 1, 6, 4]), // Take every 2nd element in reverse
+///     &[2, 2]
+/// ).unwrap();
+/// assert_eq!(result, expected);
+///
+/// // Test case 4: Negative stride with non-zero modulo
+/// // This should start at (size - 1 - modulo) and reverse
+/// let result = downsample(&x, 1, -2, 1).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(
+///     Some(&[2, 5]), // Start at second element from end, take every 2nd in reverse
+///     &[2, 1]
+/// ).unwrap();
+/// assert_eq!(result, expected);
+///
+/// // Create a larger test case for more complex downsampling
+/// let y = Tensor::<IntegerRep>::new(
+///     Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
+///     &[3, 4],
+/// ).unwrap();
+///
+/// // Test case 5: Negative stride with modulo on larger tensor
+/// let result = downsample(&y, 1, -2, 1).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(
+///     Some(&[3, 1, 7, 5, 11, 9]), // Start at one after reverse, take every 2nd
+///     &[3, 2]
+/// ).unwrap();
+/// assert_eq!(result, expected);
+/// ```
 pub fn downsample<T: TensorType + Send + Sync>(
     input: &Tensor<T>,
     dim: usize,
-    stride: usize,
+    stride: isize, // Changed from usize to isize to support negative strides
     modulo: usize,
 ) -> Result<Tensor<T>, TensorError> {
-    let mut output_shape = input.dims().to_vec();
-    // now downsample along axis dim offset by modulo, rounding up (+1 if remaidner is non-zero)
-    let remainder = (input.dims()[dim] - modulo) % stride;
-    let div = (input.dims()[dim] - modulo) / stride;
-    output_shape[dim] = div + (remainder > 0) as usize;
-    let mut output = Tensor::<T>::new(None, &output_shape)?;
+    // Handle negative stride case
+    if stride == 0 {
+        return Err(TensorError::DimMismatch(
+            "downsample stride cannot be zero".to_string(),
+        ));
+    }
 
-    if modulo > input.dims()[dim] {
+    let stride_abs = stride.unsigned_abs();
+    let mut output_shape = input.dims().to_vec();
+
+    if modulo >= input.dims()[dim] {
         return Err(TensorError::DimMismatch("downsample".to_string()));
     }
 
-    // now downsample along axis dim offset by modulo
+    // Calculate output shape based on the absolute value of stride
+    let remainder = (input.dims()[dim] - modulo) % stride_abs;
+    let div = (input.dims()[dim] - modulo) / stride_abs;
+    output_shape[dim] = div + (remainder > 0) as usize;
+
+    let mut output = Tensor::<T>::new(None, &output_shape)?;
+
+    // Calculate indices based on stride direction
     let indices = (0..output_shape.len())
         .map(|i| {
             if i == dim {
                 let mut index = vec![0; output_shape[i]];
-                for (i, idx) in index.iter_mut().enumerate() {
-                    *idx = i * stride + modulo;
+                for (j, idx) in index.iter_mut().enumerate() {
+                    if stride > 0 {
+                        // Positive stride: move forward from modulo
+                        *idx = j * stride_abs + modulo;
+                    } else {
+                        // Negative stride: move backward from (size - 1 - modulo)
+                        *idx = (input.dims()[dim] - 1 - modulo) - j * stride_abs;
+                    }
                 }
                 index
             } else {