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refactor: track onnx examples in main repo (#121)

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This commit is contained in:
dante
2023-02-26 16:56:17 +00:00
committed by GitHub
parent 898696167a
commit 1a0c92b8ea
44 changed files with 1465 additions and 61 deletions
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33
.github/workflows/rust.yml vendored
View File

@@ -55,7 +55,6 @@ jobs:
library-tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions-rs/toolchain@v1
@@ -69,11 +68,9 @@ jobs:
run: cargo test --lib --verbose
mock-proving-tests:
runs-on: ubuntu-latest
runs-on: ubuntu-latest-32-cores
steps:
- uses: actions/checkout@v3
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly
@@ -81,24 +78,21 @@ jobs:
components: rustfmt, clippy
- name: Mock proving tests (public outputs)
run: cargo test --release --verbose tests::mock_public_outputs_ -- --test-threads 16
run: cargo test --release --verbose tests::mock_public_outputs_ -- --test-threads 32
- name: Mock proving tests (public inputs)
run: cargo test --release --verbose tests::mock_public_inputs_ -- --test-threads 16
run: cargo test --release --verbose tests::mock_public_inputs_ -- --test-threads 32
- name: Mock proving tests (public params)
run: cargo test --release --verbose tests::mock_public_params_ -- --test-threads 16
run: cargo test --release --verbose tests::mock_public_params_ -- --test-threads 32
prove-and-verify-evm-tests:
runs-on: ubuntu-latest-16-cores
steps:
- uses: actions/checkout@v3
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly
override: true
components: rustfmt, clippy
- name: Install solc
run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.17 && solc --version
- name: Install Anvil
@@ -110,14 +104,11 @@ jobs:
runs-on: ubuntu-latest-16-cores
steps:
- uses: actions/checkout@v3
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly
override: true
components: rustfmt, clippy
- name: KZG prove and verify tests
run: cargo test --release --verbose tests::kzg_prove_and_verify_ -- --test-threads 4
@@ -125,23 +116,18 @@ jobs:
runs-on: self-hosted
steps:
- uses: actions/checkout@v3
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly
override: true
components: rustfmt, clippy
- name: KZG prove and verify aggr tests
run: cargo test --release --verbose tests_aggr::kzg_aggr_prove_and_verify_ -- --test-threads 4
run: cargo test --release --verbose tests_aggr::kzg_aggr_prove_and_verify_
prove-and-verify-evm-aggr-tests:
prove-and-verify-aggr-evm-tests:
runs-on: self-hosted
steps:
- uses: actions/checkout@v3
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly
@@ -150,10 +136,10 @@ jobs:
- name: Install solc
run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.17 && solc --version
- name: KZG prove and verify aggr tests
run: cargo test --release --verbose tests_evm::kzg_evm_aggr_prove_and_verify_ -- --test-threads 4 --include-ignored
run: cargo test --release --verbose tests_evm::kzg_evm_aggr_prove_and_verify_ -- --include-ignored
examples:
runs-on: self-hosted
runs-on: ubuntu-latest-32-cores
steps:
- uses: actions/checkout@v3
- uses: actions-rs/toolchain@v1
@@ -171,13 +157,10 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly
override: true
components: rustfmt, clippy
- name: Mock proving tests (should fail)
run: cargo test neg_tests::neg_examples_

4
.gitmodules vendored
View File

@@ -1,4 +0,0 @@
[submodule "examples/onnx"]
path = examples/onnx
url = https://github.com/zkonduit/onnx-examples
branch = main

30
README.md
View File

@@ -46,8 +46,6 @@ Note that the library requires a nightly version of the rust toolchain. You can
rustup override set nightly
```
This repository includes onnx example files as a submodule for testing out the cli. Either pass the `--recurse-submodules` flag to the `clone` command or after cloning run `git submodule update --init --recursive`.
### docs 📖
Use `cargo doc --open` to compile and open the docs in your default browser.
@@ -64,26 +62,26 @@ The `ezkl` cli provides a simple interface to load `.onnx` files, which represen
You can easily create an `.onnx` file using `pytorch`. For samples of Onnx files see [here](https://github.com/zkonduit/onnx-examples). For a tutorial on how to quickly generate Onnx files using python, check out [pyezkl](https://github.com/zkonduit/pyezkl).
These examples are also available as a submodule in `./examples/onnx`. To generate a proof on one of the examples, first build ezkl (`cargo build --release`) and add it to your favourite `PATH` variables, then generate a structured reference string (SRS):
Sample onnx files are also available in `./examples/onnx`. To generate a proof on one of the examples, first build ezkl (`cargo build --release`) and add it to your favourite `PATH` variables, then generate a structured reference string (SRS):
```bash
ezkl -K=17 gen-srs --pfsys=kzg --params-path=kzg.params
```
```bash
ezkl --bits=16 -K=17 prove -D ./examples/onnx/examples/1l_relu/input.json -M ./examples/onnx/examples/1l_relu/network.onnx --proof-path 1l_relu.pf --vk-path 1l_relu.vk --params-path=kzg.params
ezkl --bits=16 -K=17 prove -D ./examples/onnx/1l_relu/input.json -M ./examples/onnx/1l_relu/network.onnx --proof-path 1l_relu.pf --vk-path 1l_relu.vk --params-path=kzg.params
```
This command generates a proof that the model was correctly run on private inputs (this is the default setting). It then outputs the resulting proof at the path specfifed by `--proof-path`, parameters that can be used for subsequent verification at `--params-path` and the verifier key at `--vk-path`.
Luckily `ezkl` also provides command to verify the generated proofs:
```bash
ezkl --bits=16 -K=17 verify -M ./examples/onnx/examples/1l_relu/network.onnx --proof-path 1l_relu.pf --vk-path 1l_relu.vk --params-path=kzg.params
ezkl --bits=16 -K=17 verify -M ./examples/onnx/1l_relu/network.onnx --proof-path 1l_relu.pf --vk-path 1l_relu.vk --params-path=kzg.params
```
To display a table of the loaded onnx nodes, their associated parameters, set `RUST_LOG=DEBUG` or run:
```bash
cargo run --release --bin ezkl -- table -M ./examples/onnx/examples/1l_relu/network.onnx
cargo run --release --bin ezkl -- table -M ./examples/onnx/1l_relu/network.onnx
```
@@ -93,11 +91,11 @@ Note that the above prove and verify stats can also be run with an EVM verifier.
```bash
# gen proof
ezkl --bits=16 -K=17 prove -D ./examples/onnx/examples/1l_relu/input.json -M ./examples/onnx/examples/1l_relu/network.onnx --proof-path 1l_relu.pf --vk-path 1l_relu.vk --params-path=kzg.params --transcript=evm
ezkl --bits=16 -K=17 prove -D ./examples/onnx/1l_relu/input.json -M ./examples/onnx/1l_relu/network.onnx --proof-path 1l_relu.pf --vk-path 1l_relu.vk --params-path=kzg.params --transcript=evm
```
```bash
# gen evm verifier
ezkl -K=17 --bits=16 create-evm-verifier -D ./examples/onnx/examples/1l_relu/input.json -M ./examples/onnx/examples/1l_relu/network.onnx --pfsys=kzg --deployment-code-path 1l_relu.code --params-path=kzg.params --vk-path 1l_relu.vk --sol-code-path 1l_relu.sol
ezkl -K=17 --bits=16 create-evm-verifier -D ./examples/onnx/1l_relu/input.json -M ./examples/onnx/1l_relu/network.onnx --pfsys=kzg --deployment-code-path 1l_relu.code --params-path=kzg.params --vk-path 1l_relu.vk --sol-code-path 1l_relu.sol
```
```bash
# Verify (EVM)
@@ -115,12 +113,12 @@ ezkl -K=20 gen-srs --pfsys=kzg --params-path=kzg.params
```bash
# Single proof -> single proof we are going to feed into aggregation circuit. (Mock)-verifies + verifies natively as sanity check
ezkl -K=17 --bits=16 prove --pfsys=kzg --transcript=poseidon --strategy=accum -D ./examples/onnx/examples/1l_relu/input.json -M ./examples/onnx/examples/1l_relu/network.onnx --proof-path 1l_relu.pf --params-path=kzg.params --vk-path=1l_relu.vk
ezkl -K=17 --bits=16 prove --pfsys=kzg --transcript=poseidon --strategy=accum -D ./examples/onnx/1l_relu/input.json -M ./examples/onnx/1l_relu/network.onnx --proof-path 1l_relu.pf --params-path=kzg.params --vk-path=1l_relu.vk
```
```bash
# Aggregate -> generates aggregate proof and also (mock)-verifies + verifies natively as sanity check
ezkl -K=17 --bits=16 aggregate --transcript=evm -M ./examples/onnx/examples/1l_relu/network.onnx --pfsys=kzg --aggregation-snarks=1l_relu.pf --aggregation-vk-paths 1l_relu.vk --vk-path aggr_1l_relu.vk --proof-path aggr_1l_relu.pf --params-path=kzg.params
ezkl -K=17 --bits=16 aggregate --transcript=evm -M ./examples/onnx/1l_relu/network.onnx --pfsys=kzg --aggregation-snarks=1l_relu.pf --aggregation-vk-paths 1l_relu.vk --vk-path aggr_1l_relu.vk --proof-path aggr_1l_relu.pf --params-path=kzg.params
```
```bash
@@ -219,6 +217,18 @@ criterion_group! {
}
```
## onnx examples
This repository includes onnx example files as a submodule for testing out the cli.
If you want to add a model to `examples/onnx`, open a PR creating a new folder within `examples/onnx` with a descriptive model name. This folder should contain:
- an `input.json` input file, with the fields expected by the [ezkl](https://github.com/zkonduit/ezkl) cli.
- a `network.onnx` file representing the trained model
- a `gen.py` file for generating the `.json` and `.onnx` files following the general structure of `examples/tutorial/tutorial.py`.
TODO: add associated python files in the onnx model directories.
## library examples 🔍
Beyond the `.onnx` examples detailed above, we also include examples which directly use some of our rust API; allowing users to code up computational graphs and circuits from scratch in rust without having to go via python.

1
examples/onnx

Submodule examples/onnx deleted from b1cee58c04

51
examples/onnx/1l_average/input.json Normal file
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@@ -0,0 +1,51 @@
{
"input_shapes": [
[
1,
5,
5
]
],
"input_data": [
[
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1
]
],
"output_data": [
[
0.914,
0.914,
0.914,
0.914,
0.914,
0.914,
0.914,
0.914,
0.914
]
]
}

BIN
examples/onnx/1l_average/network.onnx Normal file
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Binary file not shown.

16
examples/onnx/1l_conv/gen.py Normal file
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@@ -0,0 +1,16 @@
from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.Conv2d(3, 1, (1, 1), 1, 1)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape = [3, 1, 1])

30
examples/onnx/1l_conv/input.json Normal file
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@@ -0,0 +1,30 @@
{
"input_shapes": [
[
3,
1,
1
]
],
"input_data": [
[
0.011350071988999844,
0.03404385969042778,
0.04626564309000969
]
],
"output_data": [
[
-0.36590576171875,
-0.36590576171875,
-0.36590576171875,
-0.36590576171875,
-0.3919677734375,
-0.36590576171875,
-0.36590576171875,
-0.36590576171875,
-0.36590576171875
]
]
}

27
examples/onnx/1l_conv/network.onnx Normal file
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@@ -0,0 +1,27 @@
pytorch1.13.1:¿
š
input
layer.weight
layer.biasoutput /layer/Conv"Conv*
dilations@@ *
group *
kernel_shape@@ *
pads@@@@ *
strides@@  torch_jit*&B layer.weightJ ´‚¾(ȾF
Õ¾*B
layer.biasJPW»¾Z)
input


batch_size


b*
output


batch_size


B

50
examples/onnx/1l_flatten/gen.py Normal file
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@@ -0,0 +1,50 @@
import io
import numpy as np
from torch import nn
import torch.onnx
import torch.nn as nn
import torch.nn.init as init
import json
class Circuit(nn.Module):
def __init__(self):
super(Circuit, self).__init__()
self.flatten = nn.Flatten()
def forward(self, x):
return self.flatten(x)
def main():
torch_model = Circuit()
# Input to the model
shape = [3, 2, 3]
x = 0.1*torch.rand(1,*shape, requires_grad=True)
torch_out = torch_model(x)
# Export the model
torch.onnx.export(torch_model, # model being run
x, # model input (or a tuple for multiple inputs)
"network.onnx", # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=10, # 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'}})
d = ((x).detach().numpy()).reshape([-1]).tolist()
data = dict(input_shapes = [shape, shape, shape],
input_data = [d],
output_data = [((o).detach().numpy()).reshape([-1]).tolist() for o in torch_out])
# Serialize data into file:
json.dump( data, open( "input.json", 'w' ) )
if __name__ == "__main__":
main()

63
examples/onnx/1l_flatten/input.json Normal file
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@@ -0,0 +1,63 @@
{
"input_shapes": [
[
3,
2,
3
],
[
3,
2,
3
],
[
3,
2,
3
]
],
"input_data": [
[
0.024982912465929985,
0.09144836664199829,
0.009130030870437622,
0.010118866339325905,
0.08052441477775574,
0.005395460408180952,
0.0011400461662560701,
0.07372475415468216,
0.023353761062026024,
0.05521431565284729,
0.09113214910030365,
0.04682333394885063,
0.007070374675095081,
0.03275662660598755,
0.013910329900681973,
0.06051855906844139,
0.05030276998877525,
0.002268546959385276
]
],
"output_data": [
[
0.024982912465929985,
0.09144836664199829,
0.009130030870437622,
0.010118866339325905,
0.08052441477775574,
0.005395460408180952,
0.0011400461662560701,
0.07372475415468216,
0.023353761062026024,
0.05521431565284729,
0.09113214910030365,
0.04682333394885063,
0.007070374675095081,
0.03275662660598755,
0.013910329900681973,
0.06051855906844139,
0.05030276998877525,
0.002268546959385276
]
]
}

16
examples/onnx/1l_flatten/network.onnx Normal file
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@@ -0,0 +1,16 @@
pytorch1.12.1:Œ
0
inputoutput Flatten_0"Flatten*
axis  torch_jitZ)
input


batch_size


b"
output


batch_size
B

16
examples/onnx/1l_leakyrelu/gen.py Normal file
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@@ -0,0 +1,16 @@
from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.LeakyReLU(negative_slope=0.05)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape = [3])

21
examples/onnx/1l_leakyrelu/input.json Normal file
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@@ -0,0 +1,21 @@
{
"input_shapes": [
[
3
]
],
"input_data": [
[
0.06149055436253548,
0.04009602218866348,
0.027487868443131447
]
],
"output_data": [
[
0.06149055436253548,
0.04009602218866348,
0.027487868443131447
]
]
}

14
examples/onnx/1l_leakyrelu/network.onnx Normal file
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@@ -0,0 +1,14 @@
pytorch1.12.1:Ś
8
inputoutput LeakyRelu_0" LeakyRelu*
alphaÍĚL=  torch_jitZ!
input


batch_size
b"
output


batch_size
B

22
examples/onnx/1l_mlp/input.json Normal file
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@@ -0,0 +1,22 @@
{
"input_shapes": [
[
3
]
],
"input_data": [
[
1.54172945022583,
0.5346152782440186,
1.2172532081604004
]
],
"output_data": [
[
0.28125,
0.6484375,
0.0,
0.0
]
]
}

BIN
examples/onnx/1l_mlp/network.onnx Normal file
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Binary file not shown.

16
examples/onnx/1l_prelu/gen.py Normal file
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@@ -0,0 +1,16 @@
from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.PReLU(num_parameters=3, init=0.25)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape = [3, 2, 2])

41
examples/onnx/1l_prelu/input.json Normal file
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@@ -0,0 +1,41 @@
{
"input_shapes": [
[
3,
2,
2
]
],
"input_data": [
[
0.024342495948076248,
0.04569540172815323,
0.07267124205827713,
0.013060438446700573,
0.09049484878778458,
0.023895228281617165,
0.06646782904863358,
0.005524409003555775,
0.08089068531990051,
0.0037329555489122868,
0.065357506275177,
0.04939475655555725
]
],
"output_data": [
[
0.024342495948076248,
0.04569540172815323,
0.07267124205827713,
0.013060438446700573,
0.09049484878778458,
0.023895228281617165,
0.06646782904863358,
0.005524409003555775,
0.08089068531990051,
0.0037329555489122868,
0.065357506275177,
0.04939475655555725
]
]
}

BIN
examples/onnx/1l_prelu/network.onnx Normal file
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Binary file not shown.

16
examples/onnx/1l_relu/gen.py Normal file
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@@ -0,0 +1,16 @@
from torch import nn
from ezkl import export
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.layer = nn.ReLU()
def forward(self, x):
return self.layer(x)
circuit = MyModel()
export(circuit, input_shape = [3])

21
examples/onnx/1l_relu/input.json Normal file
View File

@@ -0,0 +1,21 @@
{
"input_shapes": [
[
3
]
],
"input_data": [
[
-0.40077725052833557,
2.493845224380493,
0.5796360969543457
]
],
"output_data": [
[
0.0,
2.493845224380493,
0.5796360969543457
]
]
}

13
examples/onnx/1l_relu/network.onnx Normal file
View File

@@ -0,0 +1,13 @@
pytorch1.12.1:q

inputoutputRelu_0"Relu torch_jitZ!
input


batch_size
b"
output


batch_size
B

28
examples/onnx/1l_reshape/input.json Normal file
View File

@@ -0,0 +1,28 @@
{
"input_shapes": [
[
3,
2
]
],
"input_data": [
[
0.9399577379226685,
-0.1435815989971161,
-1.6204580068588257,
0.8732473850250244,
-0.12983344495296478,
-0.1989465206861496
]
],
"output_data": [
[
0.9399577379226685,
-0.1435815989971161,
-1.6204580068588257,
0.8732473850250244,
-0.12983344495296478,
-0.1989465206861496
]
]
}

BIN
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16
examples/onnx/1l_sigmoid/gen.py Normal file
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@@ -0,0 +1,16 @@
from torch import nn
from ezkl import export
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.layer = nn.Sigmoid()
def forward(self, x):
return self.layer(x)
circuit = MyModel()
export(circuit, input_shape = [3])

21
examples/onnx/1l_sigmoid/input.json Normal file
View File

@@ -0,0 +1,21 @@
{
"input_shapes": [
[
3
]
],
"input_data": [
[
-0.008400974795222282,
0.18489880859851837,
-0.7444106340408325
]
],
"output_data": [
[
0.4978998005390167,
0.5460934638977051,
0.3220404088497162
]
]
}

13
examples/onnx/1l_sigmoid/network.onnx Normal file
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@@ -0,0 +1,13 @@
pytorch1.12.1:w
#
inputoutput Sigmoid_0"Sigmoid torch_jitZ!
input


batch_size
b"
output


batch_size
B

BIN
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401
examples/onnx/2l_relu_sigmoid/input.json Normal file
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@@ -0,0 +1,401 @@
{
"input_shapes": [
[
3,
8,
8
]
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]
}

19
examples/onnx/2l_relu_sigmoid/network.onnx Normal file
View File

@@ -0,0 +1,19 @@
pytorch1.12.1:š
&
inputonnx::Sigmoid_5Relu_0"Relu
-
onnx::Sigmoid_5output Sigmoid_1"Sigmoid torch_jitZ)
input


batch_size


b*
output


batch_size


B

309
examples/onnx/2l_relu_sigmoid_conv/input.json Normal file
View File

@@ -0,0 +1,309 @@
{
"input_shapes": [
[
3,
8,
8
]
],
"input_data": [
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BIN
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21
examples/onnx/2l_relu_sigmoid_small/input.json Normal file
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@@ -0,0 +1,21 @@
{
"input_shapes": [
[
3
]
],
"input_data": [
[
-0.3827013373374939,
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-0.08920183777809143
]
],
"output_data": [
[
0.5,
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0.5
]
]
}

15
examples/onnx/2l_relu_sigmoid_small/network.onnx Normal file
View File

@@ -0,0 +1,15 @@
pytorch1.12.1:Š
&
inputonnx::Sigmoid_3Relu_0"Relu
-
onnx::Sigmoid_3output Sigmoid_1"Sigmoid torch_jitZ!
input


batch_size
b"
output


batch_size
B

21
examples/onnx/2l_relu_small/input.json Normal file
View File

@@ -0,0 +1,21 @@
{
"input_shapes": [
[
3
]
],
"input_data": [
[
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]
],
"output_data": [
[
3.394426107406616,
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0.0
]
]
}

15
examples/onnx/2l_relu_small/network.onnx Normal file
View File

@@ -0,0 +1,15 @@
pytorch1.12.1:“

inputinput.1Relu_0"Relu

input.1outputRelu_1"Relu torch_jitZ!
input


batch_size
b"
output


batch_size
B

BIN
examples/onnx/2l_sigmoid_conv/network.onnx Normal file
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BIN
examples/onnx/3l_linear_mlp/network.onnx Normal file
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104
examples/onnx/large_op_graph/input.json Normal file
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@@ -0,0 +1,104 @@
{
"input_shapes": [
[
3,
2,
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]
],
"input_data": [
[
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BIN
examples/onnx/mnist_net/network.onnx Normal file
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42
tests/integration_tests.rs
View File

@@ -220,9 +220,9 @@ fn neg_mock(example_name: String, counter_example: String) {
"-K=17",
"mock",
"-D",
format!("./examples/onnx/examples/{}/input.json", counter_example).as_str(),
format!("./examples/onnx/{}/input.json", counter_example).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
// "-K",
// "2", //causes failure
])
@@ -248,9 +248,9 @@ fn mock(example_name: String) {
"-K=17",
"mock",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
// "-K",
// "2", //causes failure
])
@@ -268,9 +268,9 @@ fn mock_public_inputs(example_name: String) {
"-K=17",
"mock",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
// "-K",
// "2", //causes failure
])
@@ -288,9 +288,9 @@ fn mock_public_params(example_name: String) {
"-K=17",
"mock",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
// "-K",
// "2", //causes failure
])
@@ -308,9 +308,9 @@ fn kzg_aggr_prove_and_verify(example_name: String) {
"prove",
"--pfsys=kzg",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--proof-path",
format!("kzg_{}.pf", example_name).as_str(),
"--vk-path",
@@ -329,7 +329,7 @@ fn kzg_aggr_prove_and_verify(example_name: String) {
"aggregate",
"--pfsys=kzg",
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--aggregation-snarks",
format!("kzg_{}.pf", example_name).as_str(),
"--aggregation-vk-paths",
@@ -371,9 +371,9 @@ fn kzg_evm_aggr_prove_and_verify(example_name: String) {
"prove",
"--pfsys=kzg",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--proof-path",
format!("kzg_{}.pf", example_name).as_str(),
"--vk-path",
@@ -392,7 +392,7 @@ fn kzg_evm_aggr_prove_and_verify(example_name: String) {
"aggregate",
"--pfsys=kzg",
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--aggregation-snarks",
format!("kzg_{}.pf", example_name).as_str(),
"--aggregation-vk-paths",
@@ -447,9 +447,9 @@ fn kzg_prove_and_verify(example_name: String) {
"prove",
"--pfsys=kzg",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--proof-path",
format!("kzg_{}.pf", example_name).as_str(),
"--vk-path",
@@ -468,7 +468,7 @@ fn kzg_prove_and_verify(example_name: String) {
"verify",
"--pfsys=kzg",
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--proof-path",
format!("kzg_{}.pf", example_name).as_str(),
"--vk-path",
@@ -490,9 +490,9 @@ fn kzg_evm_prove_and_verify(example_name: String, with_solidity: bool) {
"prove",
"--pfsys=kzg",
"-D",
format!("./examples/onnx/examples/{}/input.json", example_name).as_str(),
format!("./examples/onnx/{}/input.json", example_name).as_str(),
"-M",
format!("./examples/onnx/examples/{}/network.onnx", example_name).as_str(),
format!("./examples/onnx/{}/network.onnx", example_name).as_str(),
"--proof-path",
format!("kzg_{}.pf", example_name).as_str(),
"--vk-path",
@@ -505,8 +505,8 @@ fn kzg_evm_prove_and_verify(example_name: String, with_solidity: bool) {
.expect("failed to execute process");
assert!(status.success());
let input_arg = format!("./examples/onnx/examples/{}/input.json", example_name);
let network_arg = format!("./examples/onnx/examples/{}/network.onnx", example_name);
let input_arg = format!("./examples/onnx/{}/input.json", example_name);
let network_arg = format!("./examples/onnx/{}/network.onnx", example_name);
let code_arg = format!("kzg_{}.code", example_name);
let vk_arg = format!("kzg_{}.vk", example_name);