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Merge pull request #2 from ZKStats/fix/align-computation-in-python-lib

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Align changes from zk-stats-lib
This commit is contained in:
Kevin Mai-Husan Chia
2024-01-26 23:41:00 +08:00
committed by GitHub
parent fbada506d0 c771789c2c
commit f2aa02d6b7
7 changed files with 114 additions and 42 deletions
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@@ -1 +1 @@
{"run_args":{"tolerance":{"val":0.0,"scale":1.0},"input_scale":8,"param_scale":8,"scale_rebase_multiplier":10,"lookup_range":[0,76754],"logrows":17,"num_inner_cols":2,"variables":[["batch_size",1]],"input_visibility":{"Hashed":{"hash_is_public":true,"outlets":[]}},"output_visibility":"Public","param_visibility":"Private"},"num_rows":14432,"total_assignments":606,"total_const_size":0,"model_instance_shapes":[[1],[1]],"model_output_scales":[0,8],"model_input_scales":[8],"module_sizes":{"kzg":[],"poseidon":[14432,[1]],"elgamal":[0,[0]]},"required_lookups":["Abs",{"GreaterThan":{"a":0.0}}],"check_mode":"UNSAFE","version":"7.0.0","num_blinding_factors":null}
{"run_args":{"tolerance":{"val":0.0,"scale":1.0},"input_scale":8,"param_scale":8,"scale_rebase_multiplier":10,"lookup_range":[-25112,24986],"logrows":16,"num_inner_cols":2,"variables":[["batch_size",1]],"input_visibility":{"Hashed":{"hash_is_public":true,"outlets":[]}},"output_visibility":"Public","param_visibility":"Private"},"num_rows":14432,"total_assignments":12049,"total_const_size":1816,"model_instance_shapes":[[1],[1]],"model_output_scales":[0,8],"model_input_scales":[8],"module_sizes":{"kzg":[],"poseidon":[14432,[1]],"elgamal":[0,[0]]},"required_lookups":["Abs",{"GreaterThan":{"a":0.0}},"KroneckerDelta"],"check_mode":"UNSAFE","version":"7.0.0","num_blinding_factors":null}

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@@ -30,8 +30,14 @@ export async function generateJupyterNotebookForComputation(computation: string,
async function exampleDownloadNotebook() {
const name = "mean";
const date = Date.now();
const computation = `from zkstats.models import MeanModel
Model = MeanModel`
const computation = `import torch
from zkstats.computation import State
def computation(state: State, x: list[torch.Tensor]):
x_0 = x[0]
out_0 = state.median(x_0)
out_1 = state.median(x_0)
return state.mean(torch.tensor([out_0, out_1]).reshape(1,-1,1))`
const notebook = await generateJupyterNotebookForComputation(computation, templateNotebook)
// Download for testing
const element = document.createElement("a");