ci: update version string in docs

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==15.1.2
+ezkl==15.1.1
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '15.1.2'
+release = '15.1.1'
 version = release
 
 

examples/onnx/rsqrt/gen.py

@@ -1,42 +0,0 @@
-from torch import nn
-import torch
-import json
-import numpy as np
-
-
-class MyModel(nn.Module):
-    def __init__(self):
-        super(MyModel, self).__init__()
-
-    def forward(self, x):         
-        # reciprocal sqrt 
-        m = 1 / torch.sqrt(x)
-        return m 
-
-
-circuit = MyModel()
-
-x = torch.empty(1, 8).uniform_(0, 1)
-
-out = circuit(x)
-
-print(out)
-
-torch.onnx.export(circuit, x, "network.onnx",
-                  export_params=True,        # store the trained parameter weights inside the model file
-                  opset_version=17,          # the ONNX version to export the model to
-                  do_constant_folding=True,  # whether to execute constant folding for optimization
-                  input_names=['input'],   # the model's input names
-                  output_names=['output'],  # the model's output names
-                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
-                                'output': {0: 'batch_size'}})
-
-
-d1 = ((x).detach().numpy()).reshape([-1]).tolist()
-
-data = dict(
-    input_data=[d1],
-)
-
-# Serialize data into file:
-json.dump(data, open("input.json", 'w'))

examples/onnx/rsqrt/input.json

@@ -1 +0,0 @@
-{"input_data": [[0.8590779900550842, 0.4029041528701782, 0.6507361531257629, 0.9782488942146301, 0.37392884492874146, 0.6867020726203918, 0.11407750844955444, 0.362740159034729]]}

examples/onnx/rsqrt/network.onnx

@@ -1,17 +0,0 @@
-pytorch2.2.2:Ź

-$
-input/Sqrt_output_0/Sqrt"Sqrt
-1
-/Sqrt_output_0output/Reciprocal"
-Reciprocal
-main_graphZ!
-input
-

-
-batch_size
-b"
-output
-

-
-batch_size
-B

src/circuit/ops/hybrid.rs

@@ -16,6 +16,7 @@ pub enum HybridOp {
     Recip {
         input_scale: utils::F32,
         output_scale: utils::F32,
+        use_range_check_for_int: bool,
     },
     Div {
         denom: utils::F32,
@@ -101,9 +102,10 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             HybridOp::Recip {
                 input_scale,
                 output_scale,
+                use_range_check_for_int,
             } => format!(
-                "RECIP (input_scale={}, output_scale={})",
-                input_scale, output_scale
+                "RECIP (input_scale={}, output_scale={}, use_range_check_for_int={})",
+                input_scale, output_scale, use_range_check_for_int
             ),
             HybridOp::Div {
                 denom,
@@ -185,13 +187,31 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             HybridOp::Recip {
                 input_scale,
                 output_scale,
-            } => layouts::recip(
-                config,
-                region,
-                values[..].try_into()?,
-                integer_rep_to_felt(input_scale.0 as i128),
-                integer_rep_to_felt(output_scale.0 as i128),
-            )?,
+                use_range_check_for_int,
+            } => {
+                if input_scale.0.fract() == 0.0
+                    && output_scale.0.fract() == 0.0
+                    && *use_range_check_for_int
+                {
+                    layouts::recip(
+                        config,
+                        region,
+                        values[..].try_into()?,
+                        integer_rep_to_felt(input_scale.0 as i128),
+                        integer_rep_to_felt(output_scale.0 as i128),
+                    )?
+                } else {
+                    layouts::nonlinearity(
+                        config,
+                        region,
+                        values.try_into()?,
+                        &LookupOp::Recip {
+                            input_scale: *input_scale,
+                            output_scale: *output_scale,
+                        },
+                    )?
+                }
+            }
             HybridOp::Div {
                 denom,
                 use_range_check_for_int,

src/circuit/ops/lookup.rs

@@ -15,32 +15,89 @@ use halo2curves::ff::PrimeField;
 /// An enum representing the operations that can be used to express more complex operations via accumulation
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Deserialize, Serialize)]
 pub enum LookupOp {
-    Div { denom: utils::F32 },
-    Cast { scale: utils::F32 },
-    Ceil { scale: utils::F32 },
-    Floor { scale: utils::F32 },
-    Round { scale: utils::F32 },
-    RoundHalfToEven { scale: utils::F32 },
-    Sqrt { scale: utils::F32 },
-    Rsqrt { scale: utils::F32 },
-    Sigmoid { scale: utils::F32 },
-    Ln { scale: utils::F32 },
-    Exp { scale: utils::F32 },
-    Cos { scale: utils::F32 },
-    ACos { scale: utils::F32 },
-    Cosh { scale: utils::F32 },
-    ACosh { scale: utils::F32 },
-    Sin { scale: utils::F32 },
-    ASin { scale: utils::F32 },
-    Sinh { scale: utils::F32 },
-    ASinh { scale: utils::F32 },
-    Tan { scale: utils::F32 },
-    ATan { scale: utils::F32 },
-    Tanh { scale: utils::F32 },
-    ATanh { scale: utils::F32 },
-    Erf { scale: utils::F32 },
-    Pow { scale: utils::F32, a: utils::F32 },
-    HardSwish { scale: utils::F32 },
+    Div {
+        denom: utils::F32,
+    },
+    Cast {
+        scale: utils::F32,
+    },
+    Ceil {
+        scale: utils::F32,
+    },
+    Floor {
+        scale: utils::F32,
+    },
+    Round {
+        scale: utils::F32,
+    },
+    RoundHalfToEven {
+        scale: utils::F32,
+    },
+    Sqrt {
+        scale: utils::F32,
+    },
+    Rsqrt {
+        scale: utils::F32,
+    },
+    Recip {
+        input_scale: utils::F32,
+        output_scale: utils::F32,
+    },
+    Sigmoid {
+        scale: utils::F32,
+    },
+    Ln {
+        scale: utils::F32,
+    },
+    Exp {
+        scale: utils::F32,
+    },
+    Cos {
+        scale: utils::F32,
+    },
+    ACos {
+        scale: utils::F32,
+    },
+    Cosh {
+        scale: utils::F32,
+    },
+    ACosh {
+        scale: utils::F32,
+    },
+    Sin {
+        scale: utils::F32,
+    },
+    ASin {
+        scale: utils::F32,
+    },
+    Sinh {
+        scale: utils::F32,
+    },
+    ASinh {
+        scale: utils::F32,
+    },
+    Tan {
+        scale: utils::F32,
+    },
+    ATan {
+        scale: utils::F32,
+    },
+    Tanh {
+        scale: utils::F32,
+    },
+    ATanh {
+        scale: utils::F32,
+    },
+    Erf {
+        scale: utils::F32,
+    },
+    Pow {
+        scale: utils::F32,
+        a: utils::F32,
+    },
+    HardSwish {
+        scale: utils::F32,
+    },
 }
 
 impl LookupOp {
@@ -61,6 +118,10 @@ impl LookupOp {
             LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
             LookupOp::Div { denom } => format!("div_{}", denom),
             LookupOp::Cast { scale } => format!("cast_{}", scale),
+            LookupOp::Recip {
+                input_scale,
+                output_scale,
+            } => format!("recip_{}_{}", input_scale, output_scale),
             LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
             LookupOp::Sqrt { scale } => format!("sqrt_{}", scale),
             LookupOp::Rsqrt { scale } => format!("rsqrt_{}", scale),
@@ -112,6 +173,14 @@ impl LookupOp {
                 LookupOp::Cast { scale } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::const_div(&x, f32::from(*scale).into()),
                 ),
+                LookupOp::Recip {
+                    input_scale,
+                    output_scale,
+                } => Ok::<_, TensorError>(tensor::ops::nonlinearities::recip(
+                    &x,
+                    input_scale.into(),
+                    output_scale.into(),
+                )),
                 LookupOp::Sigmoid { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::sigmoid(&x, scale.into()))
                 }
@@ -191,6 +260,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
             LookupOp::Round { scale } => format!("ROUND(scale={})", scale),
             LookupOp::RoundHalfToEven { scale } => format!("ROUND_HALF_TO_EVEN(scale={})", scale),
             LookupOp::Pow { a, scale } => format!("POW(scale={}, exponent={})", scale, a),
+            LookupOp::Recip {
+                input_scale,
+                output_scale,
+            } => format!(
+                "RECIP(input_scale={}, output_scale={})",
+                input_scale, output_scale
+            ),
             LookupOp::Div { denom, .. } => format!("DIV(denom={})", denom),
             LookupOp::Cast { scale } => format!("CAST(scale={})", scale),
             LookupOp::Ln { scale } => format!("LN(scale={})", scale),
@@ -236,6 +312,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
                 let in_scale = inputs_scale[0];
                 in_scale + multiplier_to_scale(1. / scale.0 as f64)
             }
+            LookupOp::Recip { output_scale, .. } => multiplier_to_scale(output_scale.into()),
             _ => inputs_scale[0],
         };
         Ok(scale)

src/graph/utilities.rs

@@ -809,6 +809,7 @@ pub fn new_op_from_onnx(
             SupportedOp::Hybrid(HybridOp::Recip {
                 input_scale: (scale_to_multiplier(in_scale) as f32).into(),
                 output_scale: (scale_to_multiplier(max_scale) as f32).into(),
+                use_range_check_for_int: true,
             })
         }
 
@@ -1106,17 +1107,10 @@ pub fn new_op_from_onnx(
                 if c.raw_values.len() > 1 {
                     unimplemented!("only support scalar pow")
                 }
-
-                let exponent = c.raw_values[0];
-
-                if exponent.fract() == 0.0 {
-                    SupportedOp::Linear(PolyOp::Pow(exponent as u32))
-                } else {
-                    SupportedOp::Nonlinear(LookupOp::Pow {
-                        scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
-                        a: crate::circuit::utils::F32(exponent),
-                    })
-                }
+                SupportedOp::Nonlinear(LookupOp::Pow {
+                    scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+                    a: crate::circuit::utils::F32(c.raw_values[0]),
+                })
             } else {
                 unimplemented!("only support constant pow for now")
             }

tests/integration_tests.rs

@@ -205,7 +205,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 95] = [
+    const TESTS: [&str; 94] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -304,7 +304,6 @@ mod native_tests {
         "lstm_large",  // 91
         "lstm_medium", // 92
         "lenet_5",     // 93
-        "rsqrt",       // 94
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -543,7 +542,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=94 {
+            seq!(N in 0..=93 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -1119,7 +1118,7 @@ mod native_tests {
 
             });
 
-            seq!(N in 0..4 {
+            seq!(N in 0..=93 {
                 #(#[test_case(TESTS[N])])*
                 fn kzg_evm_prove_and_verify_reusable_verifier_(test: &str) {
                     crate::native_tests::init_binary();