minor changes

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '18.1.12'
+release = '0.0.0'
 version = release
 
 

examples/onnx/integer_div/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):          
-        return x // 3
-
-
-circuit = MyModel()
-
-x = torch.randint(0, 10, (1, 2, 2, 8)) 
-
-out = circuit(x)
-
-print(x)
-print(out)
-print(x/3)
-
-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/integer_div/input.json

@@ -1 +0,0 @@
-{"input_data": [[3, 4, 0, 9, 2, 6, 2, 5, 1, 5, 3, 5, 5, 7, 0, 2, 6, 1, 4, 4, 1, 9, 7, 7, 5, 8, 2, 0, 1, 5, 9, 8]]}

src/circuit/ops/layouts.rs

@@ -157,9 +157,7 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     // implicitly check if the prover provided output is within range
     let claimed_output = identity(config, region, &[claimed_output], true)?;
     // check if x is too large only if the decomp would support overflow in the previous op
-    if F::from_u128(IntegerRep::MAX as u128)
-        < F::from_u128(region.base() as u128).pow([region.legs() as u64]) - F::ONE
-    {
+    if (IntegerRep::MAX).abs() < ((region.base() as i128).pow(region.legs() as u32)) - 1 {
         // here we decompose and extract the sign of the input
         let sign = sign(config, region, &[claimed_output.clone()])?;
 
@@ -256,9 +254,7 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     )?;
 
     // check if x is too large only if the decomp would support overflow in the previous op
-    if F::from_u128(IntegerRep::MAX as u128)
-        < F::from_u128(region.base() as u128).pow([region.legs() as u64]) - F::ONE
-    {
+    if (IntegerRep::MAX).abs() < ((region.base() as i128).pow(region.legs() as u32)) - 1 {
         // here we decompose and extract the sign of the input
         let sign = sign(config, region, &[masked_output.clone()])?;
         let abs_value = pairwise(
@@ -2656,9 +2652,9 @@ pub fn mean_of_squares_axes<F: PrimeField + TensorType + PartialOrd + std::hash:
     let squared = pow(config, region, values, 2)?;
     let sum_squared = sum_axes(config, region, &[squared], axes)?;
 
-    let dividend: usize = values[0].len() / sum_squared.len();
+    let dividand: usize = values[0].len() / sum_squared.len();
 
-    let mean_squared = div(config, region, &[sum_squared], F::from(dividend as u64))?;
+    let mean_squared = div(config, region, &[sum_squared], F::from(dividand as u64))?;
     Ok(mean_squared)
 }
 

src/graph/utilities.rs

@@ -274,10 +274,12 @@ pub fn new_op_from_onnx(
     symbol_values: &SymbolValues,
     run_args: &crate::RunArgs,
 ) -> Result<(SupportedOp, Vec<usize>), GraphError> {
-    use crate::circuit::InputType;
     use std::f64::consts::E;
+
     use tract_onnx::tract_core::ops::array::Trilu;
 
+    use crate::circuit::InputType;
+
     let input_scales = inputs
         .iter()
         .flat_map(|x| x.out_scales())
@@ -1272,19 +1274,9 @@ pub fn new_op_from_onnx(
                     // get the non constant index
                     let denom = c.raw_values[0];
 
-                    let op = SupportedOp::Hybrid(HybridOp::Div {
+                    SupportedOp::Hybrid(HybridOp::Div {
                         denom: denom.into(),
-                    });
-
-                    // if the input is scale 0 we re up to the max scale
-                    if input_scales[0] == 0 {
-                        SupportedOp::Rescaled(Rescaled {
-                            inner: Box::new(op),
-                            scale: vec![(0, scale_to_multiplier(scales.get_max()) as u128)],
-                        })
-                    } else {
-                        op
-                    }
+                    })
                 } else {
                     return Err(GraphError::MisformedParams(
                         "only support non zero divisors of size 1".to_string(),

tests/integration_tests.rs

@@ -206,7 +206,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 99] = [
+    const TESTS: [&str; 98] = [
         "1l_mlp",     //0
         "1l_slice",   //1
         "1l_concat",  //2
@@ -309,7 +309,6 @@ mod native_tests {
         "log",         // 95
         "exp",         // 96
         "general_exp", // 97
-        "integer_div", // 98
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -548,7 +547,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=98 {
+            seq!(N in 0..=97 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]