feat: lookupless rounding ops

zkonduit · Oct 30, 2024 · 1018031 · 1018031
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diff --git a/examples/onnx/rounding_ops/gen.py b/examples/onnx/rounding_ops/gen.py
@@ -21,9 +21,9 @@ def main():
     torch_model = Circuit()
     # Input to the model
     shape = [3, 2, 3]
-    w = 0.1*torch.rand(1, *shape, requires_grad=True)
-    x = 0.1*torch.rand(1, *shape, requires_grad=True)
-    y = 0.1*torch.rand(1, *shape, requires_grad=True)
+    w = 2 * torch.rand(1, *shape, requires_grad=True) - 1
+    x = 2 * torch.rand(1, *shape, requires_grad=True) - 1
+    y = 2 * torch.rand(1, *shape, requires_grad=True) - 1
     torch_out = torch_model(w, x, y)
     # Export the model
     torch.onnx.export(torch_model,               # model being run

diff --git a/examples/onnx/rounding_ops/input.json b/examples/onnx/rounding_ops/input.json
@@ -1 +1,148 @@
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+}
diff --git a/examples/onnx/rounding_ops/network.onnx b/examples/onnx/rounding_ops/network.onnx
@@ -1,10 +1,11 @@
-pytorch2.0.1:�
+pytorch2.2.2:�
 
 woutput_w/Round"Round
 
 xoutput_x/Floor"Floor
 
-youtput_y/Ceil"Ceil	torch_jitZ%
+youtput_y/Ceil"Ceil
+main_graphZ%
 w 
 
 

diff --git a/src/circuit/ops/hybrid.rs b/src/circuit/ops/hybrid.rs
@@ -13,6 +13,18 @@ use serde::{Deserialize, Serialize};
 /// An enum representing the operations that consist of both lookups and arithmetic operations.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum HybridOp {
+    Ceil {
+        scale: utils::F32,
+        legs: usize,
+    },
+    Floor {
+        scale: utils::F32,
+        legs: usize,
+    },
+    Round {
+        scale: utils::F32,
+        legs: usize,
+    },
     Recip {
         input_scale: utils::F32,
         output_scale: utils::F32,
@@ -96,6 +108,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
 
     fn as_string(&self) -> String {
         match self {
+            HybridOp::Ceil { scale, legs } => format!("CEIL(scale={}, legs={})", scale, legs),
+            HybridOp::Floor { scale, legs } => format!("FLOOR(scale={}, legs={})", scale, legs),
+            HybridOp::Round { scale, legs } => format!("ROUND(scale={}, legs={})", scale, legs),
             HybridOp::Max => format!("MAX"),
             HybridOp::Min => format!("MIN"),
             HybridOp::Recip {
@@ -166,6 +181,15 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
         values: &[ValTensor<F>],
     ) -> Result<Option<ValTensor<F>>, CircuitError> {
         Ok(Some(match self {
+            HybridOp::Ceil { scale, legs } => {
+                layouts::ceil(config, region, values[..].try_into()?, *scale, *legs)?
+            }
+            HybridOp::Floor { scale, legs } => {
+                layouts::floor(config, region, values[..].try_into()?, *scale, *legs)?
+            }
+            HybridOp::Round { scale, legs } => {
+                layouts::round(config, region, values[..].try_into()?, *scale, *legs)?
+            }
             HybridOp::Max => layouts::max_comp(config, region, values[..].try_into()?)?,
             HybridOp::Min => layouts::min_comp(config, region, values[..].try_into()?)?,
             HybridOp::SumPool {