fix: bump python version (#761)

.github/workflows/pypi-gpu.yml

@@ -26,7 +26,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set pyproject.toml version to match github tag

.github/workflows/pypi.yml

@@ -25,7 +25,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -70,7 +70,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: ${{ matrix.target }}
 
       - name: Set Cargo.toml version to match github tag
@@ -115,7 +115,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -176,7 +176,7 @@ jobs:
   #   - uses: actions/checkout@v4
   #   - uses: actions/setup-python@v4
   #     with:
-  #       python-version: 3.7
+  #       python-version: 3.12
 
   #   - name: Install cross-compilation tools for aarch64
   #     if: matrix.target == 'aarch64'
@@ -228,7 +228,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -263,7 +263,7 @@ jobs:
             apk add py3-pip
             pip3 install -U pip
             python3 -m venv .venv
-            source .venv/bin/activate            
+            source .venv/bin/activate
             pip3 install ezkl --no-index --find-links /io/dist/ --force-reinstall
             python3 -c "import ezkl"
 
@@ -287,7 +287,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
 
       - name: Set Cargo.toml version to match github tag
         shell: bash

.github/workflows/rust.yml

@@ -184,7 +184,7 @@ jobs:
 
   wasm32-tests:
     runs-on: ubuntu-latest
-    # needs: [build,  library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -207,7 +207,7 @@ jobs:
 
   tutorial:
     runs-on: ubuntu-latest
-    needs: [build, library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -224,7 +224,7 @@ jobs:
 
   mock-proving-tests:
     runs-on: non-gpu
-    # needs: [build,  library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -281,7 +281,7 @@ jobs:
 
   prove-and-verify-evm-tests:
     runs-on: non-gpu
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -354,7 +354,7 @@ jobs:
 
   prove-and-verify-tests:
     runs-on: non-gpu
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -460,7 +460,7 @@ jobs:
 
   prove-and-verify-mock-aggr-tests:
     runs-on: self-hosted
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -495,7 +495,7 @@ jobs:
 
   prove-and-verify-aggr-tests:
     runs-on: large-self-hosted
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -512,7 +512,7 @@ jobs:
 
   prove-and-verify-aggr-evm-tests:
     runs-on: large-self-hosted
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
@@ -557,16 +557,18 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.7"
+          python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
+      - name: Install cmake
+        run: sudo apt-get install -y cmake
       - name: Install solc
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Setup Virtual Env and Install python dependencies
-        run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
+        run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt;
       - name: Install Anvil
         run: cargo install --git https://github.com/foundry-rs/foundry --rev c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
       - name: Build python ezkl
@@ -576,12 +578,12 @@ jobs:
 
   accuracy-measurement-tests:
     runs-on: ubuntu-latest-32-cores
-    # needs: [build, library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.7"
+          python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-02-06
@@ -592,7 +594,7 @@ jobs:
           crate: cargo-nextest
           locked: true
       - name: Setup Virtual Env and Install python dependencies
-        run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
+        run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt;
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
       - name: Div rebase
@@ -612,7 +614,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-02-06
@@ -626,10 +628,14 @@ jobs:
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
         run: cargo install --git https://github.com/foundry-rs/foundry --rev c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
+      - name: Install pip
+        run: python -m ensurepip --upgrade
       - name: Setup Virtual Env and Install python dependencies
-        run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
+        run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt; python -m ensurepip --upgrade
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
+      - name: Tictactoe tutorials
+        run: source .env/bin/activate; cargo nextest run py_tests::tests::tictactoe_ --test-threads 1
       # - name: authenticate-kaggle-cli
       #   shell: bash
       #   env:
@@ -645,7 +651,5 @@ jobs:
         run: source .env/bin/activate; cargo nextest run py_tests::tests::voice_
       - name: NBEATS tutorial
         run: source .env/bin/activate; cargo nextest run py_tests::tests::nbeats_
-      - name: Tictactoe tutorials
-        run: source .env/bin/activate; cargo nextest run py_tests::tests::tictactoe_
       # - name: Postgres tutorials
       #   run: source .env/bin/activate; cargo nextest run py_tests::tests::postgres_ --test-threads 1

.gitignore

@@ -48,4 +48,5 @@ node_modules
 /dist
 timingData.json
 !tests/wasm/pk.key
-!tests/wasm/vk.key
+!tests/wasm/vk.key
+!tests/wasm/vk_aggr.key

Cargo.lock

@@ -4601,9 +4601,9 @@ dependencies = [
 
 [[package]]
 name = "serde-wasm-bindgen"
-version = "0.4.5"
+version = "0.6.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3b4c031cd0d9014307d82b8abf653c0290fbdaeb4c02d00c63cf52f728628bf"
+checksum = "8302e169f0eddcc139c70f139d19d6467353af16f9fce27e8c30158036a1e16b"
 dependencies = [
  "js-sys",
  "serde",

Cargo.toml

@@ -95,10 +95,10 @@ getrandom = { version = "0.2.8", features = ["js"] }
 instant = { version = "0.1", features = ["wasm-bindgen", "inaccurate"] }
 
 [target.'cfg(all(target_arch = "wasm32", target_os = "unknown"))'.dependencies]
-wasm-bindgen-rayon = { version = "1.0", optional = true }
-wasm-bindgen-test = "0.3.34"
-serde-wasm-bindgen = "0.4"
-wasm-bindgen = { version = "0.2.81", features = ["serde-serialize"] }
+wasm-bindgen-rayon = { version = "1.2.1", optional = true }
+wasm-bindgen-test = "0.3.42"
+serde-wasm-bindgen = "0.6.5"
+wasm-bindgen = { version = "0.2.92", features = ["serde-serialize"] }
 console_error_panic_hook = "0.1.7"
 wasm-bindgen-console-logger = "0.1.1"
 

examples/notebooks/keras_simple_demo.ipynb

@@ -67,6 +67,7 @@
                 "model.add(Dense(128, activation='relu'))\n",
                 "model.add(Dropout(0.5))\n",
                 "model.add(Dense(10, activation='softmax'))\n",
+                "model.output_names=['output']\n",
                 "\n",
                 "\n",
                 "# Train the model as you like here (skipped for brevity)\n",

examples/notebooks/mnist_gan.ipynb

@@ -38,7 +38,7 @@
                 "import logging\n",
                 "\n",
                 "import tensorflow as tf\n",
-                "from tensorflow.keras.optimizers.legacy import Adam\n",
+                "from tensorflow.keras.optimizers import Adam\n",
                 "from tensorflow.keras.layers import *\n",
                 "from tensorflow.keras.models import Model\n",
                 "from tensorflow.keras.datasets import mnist\n",
@@ -71,9 +71,11 @@
             },
             "outputs": [],
             "source": [
-                "opt = Adam()\n",
                 "ZDIM = 100\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
+                "\n",
                 "# discriminator\n",
                 "# 0 if it's fake, 1 if it's real\n",
                 "x = in1 = Input((28,28))\n",
@@ -114,8 +116,11 @@
                 "\n",
                 "gm = Model(in1, x)\n",
                 "gm.compile('adam', 'mse')\n",
+                "gm.output_names=['output']\n",
                 "gm.summary()\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
                 "# GAN\n",
                 "dm.trainable = False\n",
                 "x = dm(gm.output)\n",
@@ -415,7 +420,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/notebooks/mnist_vae.ipynb

@@ -349,6 +349,8 @@
                 "z_log_var = Dense(ZDIM)(x)\n",
                 "z = Lambda(lambda x: x[0] + K.exp(0.5 * x[1]) * K.random_normal(shape=K.shape(x[0])))([z_mu, z_log_var])\n",
                 "dec = get_decoder()\n",
+                "dec.output_names=['output']\n",
+                "\n",
                 "out = dec(z)\n",
                 "\n",
                 "mse_loss = mse(Reshape((28*28,))(in1), Reshape((28*28,))(out)) * 28 * 28\n",

examples/notebooks/random_forest.ipynb

@@ -61,11 +61,10 @@
                 "from sklearn.datasets import load_iris\n",
                 "from sklearn.model_selection import train_test_split\n",
                 "from sklearn.ensemble import RandomForestClassifier as Rf\n",
-                "import sk2torch\n",
                 "import torch\n",
                 "import ezkl\n",
                 "import os\n",
-                "from torch import nn\n",
+                "from hummingbird.ml import convert\n",
                 "\n",
                 "\n",
                 "\n",
@@ -77,28 +76,12 @@
                 "clr.fit(X_train, y_train)\n",
                 "\n",
                 "\n",
-                "trees = []\n",
-                "for tree in clr.estimators_:\n",
-                "    trees.append(sk2torch.wrap(tree))\n",
                 "\n",
-                "\n",
-                "class RandomForest(nn.Module):\n",
-                "    def __init__(self, trees):\n",
-                "        super(RandomForest, self).__init__()\n",
-                "        self.trees = nn.ModuleList(trees)\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        out = self.trees[0](x)\n",
-                "        for tree in self.trees[1:]:\n",
-                "            out += tree(x)\n",
-                "        return out / len(self.trees)\n",
-                "\n",
-                "\n",
-                "torch_rf = RandomForest(trees)\n",
+                "torch_rf = convert(clr, 'torch')\n",
                 "# assert predictions from torch are = to sklearn \n",
                 "diffs = []\n",
                 "for i in range(len(X_test)):\n",
-                "    torch_pred = torch_rf(torch.tensor(X_test[i].reshape(1, -1)))\n",
+                "    torch_pred = torch_rf.predict(torch.tensor(X_test[i].reshape(1, -1)))\n",
                 "    sk_pred = clr.predict(X_test[i].reshape(1, -1))\n",
                 "    diffs.append(torch_pred[0].round() - sk_pred[0])\n",
                 "\n",
@@ -134,14 +117,12 @@
                 "\n",
                 "# export to onnx format\n",
                 "\n",
-                "torch_rf.eval()\n",
-                "\n",
                 "# Input to the model\n",
                 "shape = X_train.shape[1:]\n",
                 "x = torch.rand(1, *shape, requires_grad=False)\n",
-                "torch_out = torch_rf(x)\n",
+                "torch_out = torch_rf.predict(x)\n",
                 "# Export the model\n",
-                "torch.onnx.export(torch_rf,               # model being run\n",
+                "torch.onnx.export(torch_rf.model,               # model being run\n",
                 "                  # model input (or a tuple for multiple inputs)\n",
                 "                  x,\n",
                 "                  # where to save the model (can be a file or file-like object)\n",
@@ -158,7 +139,7 @@
                 "\n",
                 "data = dict(input_shapes=[shape],\n",
                 "            input_data=[d],\n",
-                "            output_data=[((o).detach().numpy()).reshape([-1]).tolist() for o in torch_out])\n",
+                "            output_data=[o.reshape([-1]).tolist() for o in torch_out])\n",
                 "\n",
                 "# Serialize data into file:\n",
                 "json.dump(data, open(\"input.json\", 'w'))\n"
@@ -321,7 +302,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/notebooks/tictactoe_autoencoder.ipynb

@@ -13,7 +13,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 1,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -57,7 +57,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 2,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -119,7 +119,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 3,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -163,7 +163,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 4,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -217,7 +217,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 6,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -227,6 +227,10 @@
                 "        self.length = self.compute_length(self.file_good)\n",
                 "        self.data = self.load_data(self.file_good)\n",
                 "\n",
+                "    def __iter__(self):\n",
+                "        for i in range(len(self.data)):\n",
+                "            yield self.data[i]\n",
+                "\n",
                 "    def parse_json_object(self, line):\n",
                 "        try:\n",
                 "            return json.loads(line)\n",
@@ -749,7 +753,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/notebooks/tictactoe_binary_classification.ipynb

@@ -209,6 +209,11 @@
                 "        self.length = self.compute_length(self.file_good, self.file_bad)\n",
                 "        self.data = self.load_data(self.file_good, self.file_bad)\n",
                 "\n",
+                "    def __iter__(self):\n",
+                "        for i in range(len(self.data)):\n",
+                "            yield self.data[i]\n",
+                "\n",
+                "\n",
                 "    def parse_json_object(self, line):\n",
                 "        try:\n",
                 "            return json.loads(line)\n",
@@ -637,7 +642,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

requirements.txt

@@ -1,14 +1,14 @@
-attrs==22.2.0
-exceptiongroup==1.1.1
-importlib-metadata==6.1.0
+attrs==23.2.0
+exceptiongroup==1.2.0
+importlib-metadata==7.1.0
 iniconfig==2.0.0
-maturin==1.0.1
-packaging==23.0
-pluggy==1.0.0
-pytest==7.2.2
+maturin==1.5.0
+packaging==24.0
+pluggy==1.4.0
+pytest==8.1.1
 tomli==2.0.1
-typing-extensions==4.5.0
-zipp==3.15.0
-onnx==1.14.1
-onnxruntime==1.14.1
-numpy==1.21.6
+typing-extensions==4.10.0
+zipp==3.18.1
+onnx==1.15.0
+onnxruntime==1.17.1
+numpy==1.26.4

src/circuit/ops/layouts.rs

@@ -568,10 +568,10 @@ fn _sort_ascending<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     let is_assigned = !input.any_unknowns()?;
 
     let sorted = if is_assigned {
-        input
-            .get_int_evals()?
-            .iter()
-            .sorted_by(|a, b| a.cmp(b))
+        let mut int_evals = input.get_int_evals()?;
+        int_evals.par_sort_unstable_by(|a, b| a.cmp(b));
+        int_evals
+            .par_iter()
             .map(|x| Value::known(i128_to_felt(*x)))
             .collect::<Tensor<Value<F>>>()
     } else {
@@ -753,20 +753,28 @@ pub(crate) fn dynamic_lookup<F: PrimeField + TensorType + PartialOrd + std::hash
     let _table_1 = region.assign_dynamic_lookup(&config.dynamic_lookups.tables[1], &table_1)?;
     let table_len = table_0.len();
 
+    trace!("assigning tables took: {:?}", start.elapsed());
+
     // now create a vartensor of constants for the dynamic lookup index
     let table_index = create_constant_tensor(F::from(dynamic_lookup_index as u64), table_len);
     let _table_index =
         region.assign_dynamic_lookup(&config.dynamic_lookups.tables[2], &table_index)?;
 
+    trace!("assigning table index took: {:?}", start.elapsed());
+
     let lookup_0 = region.assign(&config.dynamic_lookups.inputs[0], &lookup_0)?;
     let lookup_1 = region.assign(&config.dynamic_lookups.inputs[1], &lookup_1)?;
     let lookup_len = lookup_0.len();
 
+    trace!("assigning lookups took: {:?}", start.elapsed());
+
     // now set the lookup index
     let lookup_index = create_constant_tensor(F::from(dynamic_lookup_index as u64), lookup_len);
 
     let _lookup_index = region.assign(&config.dynamic_lookups.inputs[2], &lookup_index)?;
 
+    trace!("assigning lookup index took: {:?}", start.elapsed());
+
     if !region.is_dummy() {
         (0..table_len)
             .map(|i| {
@@ -3251,11 +3259,15 @@ pub(crate) fn softmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>
     input_scale: utils::F32,
     output_scale: utils::F32,
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
+    // get the max then subtract it
+    let max_val = max(config, region, values)?;
+    // rebase the input to 0
+    let sub = pairwise(config, region, &[values[0].clone(), max_val], BaseOp::Sub)?;
     // elementwise exponential
     let ex = nonlinearity(
         config,
         region,
-        values,
+        &[sub],
         &LookupOp::Exp { scale: input_scale },
     )?;
 

src/circuit/ops/region.rs

@@ -163,7 +163,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
 
     ///
     pub fn update_constants(&mut self, constants: ConstantsMap<F>) {
-        self.assigned_constants.extend(constants.into_iter());
+        self.assigned_constants.extend(constants);
     }
 
     ///
@@ -389,7 +389,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 shuffle_index.update(&local_reg.shuffle_index);
                 // update the constants
                 let mut constants = constants.lock().unwrap();
-                constants.extend(local_reg.assigned_constants.into_iter());
+                constants.extend(local_reg.assigned_constants);
 
                 res
             })
@@ -574,8 +574,10 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 &mut self.assigned_constants,
             )
         } else {
-            let values_map = values.create_constants_map();
-            self.assigned_constants.extend(values_map);
+            if !values.is_instance() {
+                let values_map = values.create_constants_map_iterator();
+                self.assigned_constants.extend(values_map);
+            }
             Ok(values.clone())
         }
     }
@@ -599,8 +601,10 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 &mut self.assigned_constants,
             )
         } else {
-            let values_map = values.create_constants_map();
-            self.assigned_constants.extend(values_map);
+            if !values.is_instance() {
+                let values_map = values.create_constants_map_iterator();
+                self.assigned_constants.extend(values_map);
+            }
             Ok(values.clone())
         }
     }
@@ -630,9 +634,8 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 &mut self.assigned_constants,
             )
         } else {
-            let mut values_map = values.create_constants_map();
-
             let inner_tensor = values.get_inner_tensor().unwrap();
+            let mut values_map = values.create_constants_map();
 
             for o in ommissions {
                 if let ValType::Constant(value) = inner_tensor.get_flat_index(**o) {

src/execute.rs

@@ -911,7 +911,7 @@ pub(crate) fn calibrate(
     let model = Model::from_run_args(&settings.run_args, &model_path)?;
 
     let chunks = data.split_into_batches(model.graph.input_shapes()?)?;
-    debug!("num of calibration batches: {}", chunks.len());
+    info!("num calibration batches: {}", chunks.len());
 
     debug!("running onnx predictions...");
     let original_predictions = Model::run_onnx_predictions(

src/graph/mod.rs

@@ -39,7 +39,7 @@ use halo2_proofs::{
     plonk::{Circuit, ConstraintSystem, Error as PlonkError},
 };
 use halo2curves::bn256::{self, Fr as Fp, G1Affine};
-use halo2curves::ff::PrimeField;
+use halo2curves::ff::{Field, PrimeField};
 #[cfg(not(target_arch = "wasm32"))]
 use lazy_static::lazy_static;
 use log::{debug, error, trace, warn};
@@ -1451,7 +1451,8 @@ impl GraphCircuit {
 }
 
 #[derive(Clone, Debug, Default, Serialize, Deserialize)]
-struct CircuitSize {
+/// The configuration for the graph circuit
+pub struct CircuitSize {
     num_instances: usize,
     num_advice_columns: usize,
     num_fixed: usize,
@@ -1461,7 +1462,8 @@ struct CircuitSize {
 }
 
 impl CircuitSize {
-    pub fn from_cs(cs: &ConstraintSystem<Fp>, logrows: u32) -> Self {
+    ///
+    pub fn from_cs<F: Field>(cs: &ConstraintSystem<F>, logrows: u32) -> Self {
         CircuitSize {
             num_instances: cs.num_instance_columns(),
             num_advice_columns: cs.num_advice_columns(),

src/pfsys/evm/aggregation_kzg.rs

@@ -1,4 +1,8 @@
+#[cfg(not(target_arch = "wasm32"))]
+use crate::graph::CircuitSize;
 use crate::pfsys::{Snark, SnarkWitness};
+#[cfg(not(target_arch = "wasm32"))]
+use colored_json::ToColoredJson;
 use halo2_proofs::circuit::AssignedCell;
 use halo2_proofs::plonk::{self};
 use halo2_proofs::{
@@ -16,6 +20,8 @@ use halo2_wrong_ecc::{
 use halo2curves::bn256::{Bn256, Fq, Fr, G1Affine};
 use halo2curves::ff::PrimeField;
 use itertools::Itertools;
+#[cfg(not(target_arch = "wasm32"))]
+use log::debug;
 use log::trace;
 use rand::rngs::OsRng;
 use snark_verifier::loader::native::NativeLoader;
@@ -193,6 +199,23 @@ impl AggregationConfig {
         let main_gate_config = MainGate::<F>::configure(meta);
         let range_config =
             RangeChip::<F>::configure(meta, &main_gate_config, composition_bits, overflow_bits);
+
+        #[cfg(not(target_arch = "wasm32"))]
+        {
+            let circuit_size = CircuitSize::from_cs(meta, 23);
+
+            // not wasm
+
+            debug!(
+                "circuit size: \n {}",
+                circuit_size
+                    .as_json()
+                    .unwrap()
+                    .to_colored_json_auto()
+                    .unwrap()
+            );
+        }
+
         AggregationConfig {
             main_gate_config,
             range_config,

src/tensor/val.rs

@@ -448,25 +448,39 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
     }
 
     /// Returns the number of constants in the [ValTensor].
-    pub fn num_constants(&self) -> usize {
+    pub fn create_constants_map_iterator(
+        &self,
+    ) -> core::iter::FilterMap<
+        core::slice::Iter<'_, ValType<F>>,
+        fn(&ValType<F>) -> Option<(F, ValType<F>)>,
+    > {
         match self {
-            ValTensor::Value { inner, .. } => inner.iter().filter(|x| x.is_constant()).count(),
-            ValTensor::Instance { .. } => 0,
+            ValTensor::Value { inner, .. } => inner.iter().filter_map(|x| {
+                if let ValType::Constant(v) = x {
+                    Some((*v, x.clone()))
+                } else {
+                    None
+                }
+            }),
+            ValTensor::Instance { .. } => {
+                unreachable!("Instance tensors do not have constants")
+            }
         }
     }
 
     /// Returns the number of constants in the [ValTensor].
     pub fn create_constants_map(&self) -> ConstantsMap<F> {
         match self {
-            ValTensor::Value { inner, .. } => {
-                let map = inner.iter().fold(ConstantsMap::new(), |mut acc, x| {
-                    if let ValType::Constant(c) = x {
-                        acc.insert(*c, x.clone());
+            ValTensor::Value { inner, .. } => inner
+                .par_iter()
+                .filter_map(|x| {
+                    if let ValType::Constant(v) = x {
+                        Some((*v, x.clone()))
+                    } else {
+                        None
                     }
-                    acc
-                });
-                map
-            }
+                })
+                .collect(),
             ValTensor::Instance { .. } => ConstantsMap::new(),
         }
     }

src/wasm.rs

@@ -8,12 +8,14 @@ use crate::graph::quantize_float;
 use crate::graph::scale_to_multiplier;
 use crate::graph::{GraphCircuit, GraphSettings};
 use crate::pfsys::create_proof_circuit;
+use crate::pfsys::evm::aggregation_kzg::AggregationCircuit;
 use crate::pfsys::evm::aggregation_kzg::PoseidonTranscript;
 use crate::pfsys::verify_proof_circuit;
 use crate::pfsys::TranscriptType;
 use crate::tensor::TensorType;
 use crate::CheckMode;
 use crate::Commitments;
+use console_error_panic_hook;
 use halo2_proofs::plonk::*;
 use halo2_proofs::poly::commitment::{CommitmentScheme, ParamsProver};
 use halo2_proofs::poly::ipa::multiopen::{ProverIPA, VerifierIPA};
@@ -33,11 +35,10 @@ use halo2curves::bn256::{Bn256, Fr, G1Affine};
 use halo2curves::ff::{FromUniformBytes, PrimeField};
 use snark_verifier::loader::native::NativeLoader;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
+use std::str::FromStr;
 use wasm_bindgen::prelude::*;
 use wasm_bindgen_console_logger::DEFAULT_LOGGER;
 
-use console_error_panic_hook;
-
 #[cfg(feature = "web")]
 pub use wasm_bindgen_rayon::init_thread_pool;
 
@@ -395,6 +396,90 @@ pub fn verify(
     }
 }
 
+#[wasm_bindgen]
+#[allow(non_snake_case)]
+/// Verify aggregate proof in browser using wasm
+pub fn verifyAggr(
+    proof_js: wasm_bindgen::Clamped<Vec<u8>>,
+    vk: wasm_bindgen::Clamped<Vec<u8>>,
+    logrows: u64,
+    srs: wasm_bindgen::Clamped<Vec<u8>>,
+    commitment: &str,
+) -> Result<bool, JsError> {
+    let proof: crate::pfsys::Snark<Fr, G1Affine> = serde_json::from_slice(&proof_js[..])
+        .map_err(|e| JsError::new(&format!("Failed to deserialize proof: {}", e)))?;
+
+    let mut reader = std::io::BufReader::new(&vk[..]);
+    let vk = VerifyingKey::<G1Affine>::read::<_, AggregationCircuit>(
+        &mut reader,
+        halo2_proofs::SerdeFormat::RawBytes,
+        (),
+    )
+    .map_err(|e| JsError::new(&format!("Failed to deserialize vk: {}", e)))?;
+
+    let commit = Commitments::from_str(commitment).map_err(|e| JsError::new(&format!("{}", e)))?;
+
+    let orig_n = 1 << logrows;
+
+    let mut reader = std::io::BufReader::new(&srs[..]);
+    let result = match commit {
+        Commitments::KZG => {
+            let params: ParamsKZG<Bn256> =
+                halo2_proofs::poly::commitment::Params::<'_, G1Affine>::read(&mut reader)
+                    .map_err(|e| JsError::new(&format!("Failed to deserialize params: {}", e)))?;
+            let strategy = KZGSingleStrategy::new(params.verifier_params());
+            match proof.transcript_type {
+                TranscriptType::EVM => verify_proof_circuit::<
+                    VerifierSHPLONK<'_, Bn256>,
+                    KZGCommitmentScheme<Bn256>,
+                    KZGSingleStrategy<_>,
+                    _,
+                    EvmTranscript<G1Affine, _, _, _>,
+                >(&proof, &params, &vk, strategy, orig_n),
+
+                TranscriptType::Poseidon => {
+                    verify_proof_circuit::<
+                        VerifierSHPLONK<'_, Bn256>,
+                        KZGCommitmentScheme<Bn256>,
+                        KZGSingleStrategy<_>,
+                        _,
+                        PoseidonTranscript<NativeLoader, _>,
+                    >(&proof, &params, &vk, strategy, orig_n)
+                }
+            }
+        }
+        Commitments::IPA => {
+            let params: ParamsIPA<_> =
+                halo2_proofs::poly::commitment::Params::<'_, G1Affine>::read(&mut reader)
+                    .map_err(|e| JsError::new(&format!("Failed to deserialize params: {}", e)))?;
+            let strategy = IPASingleStrategy::new(params.verifier_params());
+            match proof.transcript_type {
+                TranscriptType::EVM => verify_proof_circuit::<
+                    VerifierIPA<_>,
+                    IPACommitmentScheme<G1Affine>,
+                    IPASingleStrategy<_>,
+                    _,
+                    EvmTranscript<G1Affine, _, _, _>,
+                >(&proof, &params, &vk, strategy, orig_n),
+                TranscriptType::Poseidon => {
+                    verify_proof_circuit::<
+                        VerifierIPA<_>,
+                        IPACommitmentScheme<G1Affine>,
+                        IPASingleStrategy<_>,
+                        _,
+                        PoseidonTranscript<NativeLoader, _>,
+                    >(&proof, &params, &vk, strategy, orig_n)
+                }
+            }
+        }
+    };
+
+    match result {
+        Ok(_) => Ok(true),
+        Err(e) => Err(JsError::new(&format!("{}", e))),
+    }
+}
+
 /// Prove in browser using wasm
 #[wasm_bindgen]
 pub fn prove(

tests/py_integration_tests.rs

@@ -56,33 +56,38 @@ mod py_tests {
             // source .env/bin/activate
             // pip install -r requirements.txt
             // maturin develop --release --features python-bindings
-
+            // first install tf2onnx as it has protobuf conflict with onnx
+            let status = Command::new("pip")
+                .args(["install", "tf2onnx==1.16.1"])
+                .status()
+                .expect("failed to execute process");
+            assert!(status.success());
             // now install torch, pandas, numpy, seaborn, jupyter
             let status = Command::new("pip")
                 .args([
                     "install",
-                    "torch-geometric==2.5.0",
-                    "torch==2.0.1",
-                    "torchvision==0.15.2",
-                    "pandas==2.0.3",
-                    "numpy==1.23",
-                    "seaborn==0.12.2",
-                    "jupyter==1.0.0",
-                    "onnx==1.14.0",
-                    "kaggle==1.5.15",
-                    "py-solc-x==1.1.1",
-                    "web3==6.5.0",
-                    "librosa==0.10.0.post2",
-                    "keras==2.12.0",
-                    "tensorflow==2.12.0",
-                    "tensorflow-datasets==4.9.3",
-                    "tf2onnx==1.14.0",
-                    "pytorch-lightning==2.0.6",
+                    "torch-geometric==2.5.2",
+                    "torch==2.2.2",
+                    "torchvision==0.17.2",
+                    "pandas==2.2.1",
+                    "numpy==1.26.4",
+                    "seaborn==0.13.2",
+                    "notebook==7.1.2",
+                    "nbconvert==7.16.3",
+                    "onnx==1.16.0",
+                    "kaggle==1.6.8",
+                    "py-solc-x==2.0.2",
+                    "web3==6.16.0",
+                    "librosa==0.10.1",
+                    "keras==3.1.1",
+                    "tensorflow==2.16.1",
+                    "tensorflow-datasets==4.9.4",
+                    "pytorch-lightning==2.2.1",
                     "sk2torch==1.2.0",
-                    "scikit-learn==1.3.1",
-                    "xgboost==1.7.6",
-                    "hummingbird-ml==0.4.9",
-                    "lightgbm==4.0.0",
+                    "scikit-learn==1.4.1.post1",
+                    "xgboost==2.0.3",
+                    "hummingbird-ml==0.4.11",
+                    "lightgbm==4.3.0",
                 ])
                 .status()
                 .expect("failed to execute process");

tests/wasm.rs

@@ -11,7 +11,7 @@ mod wasm32 {
         bufferToVecOfFelt, compiledCircuitValidation, encodeVerifierCalldata, feltToBigEndian,
         feltToFloat, feltToInt, feltToLittleEndian, genPk, genVk, genWitness, inputValidation,
         pkValidation, poseidonHash, proofValidation, prove, settingsValidation, srsValidation,
-        u8_array_to_u128_le, verify, vkValidation, witnessValidation,
+        u8_array_to_u128_le, verify, verifyAggr, vkValidation, witnessValidation,
     };
     use halo2_solidity_verifier::encode_calldata;
     use halo2curves::bn256::{Fr, G1Affine};
@@ -27,10 +27,29 @@ mod wasm32 {
     pub const NETWORK: &[u8] = include_bytes!("../tests/wasm/network.onnx");
     pub const INPUT: &[u8] = include_bytes!("../tests/wasm/input.json");
     pub const PROOF: &[u8] = include_bytes!("../tests/wasm/proof.json");
+    pub const PROOF_AGGR: &[u8] = include_bytes!("../tests/wasm/proof_aggr.json");
     pub const SETTINGS: &[u8] = include_bytes!("../tests/wasm/settings.json");
     pub const PK: &[u8] = include_bytes!("../tests/wasm/pk.key");
     pub const VK: &[u8] = include_bytes!("../tests/wasm/vk.key");
+    pub const VK_AGGR: &[u8] = include_bytes!("../tests/wasm/vk_aggr.key");
     pub const SRS: &[u8] = include_bytes!("../tests/wasm/kzg");
+    pub const SRS1: &[u8] = include_bytes!("../tests/wasm/kzg1.srs");
+
+    #[wasm_bindgen_test]
+    async fn can_verify_aggr() {
+        let value = verifyAggr(
+            wasm_bindgen::Clamped(PROOF_AGGR.to_vec()),
+            wasm_bindgen::Clamped(VK_AGGR.to_vec()),
+            21,
+            wasm_bindgen::Clamped(SRS1.to_vec()),
+            "kzg",
+        )
+        .map_err(|_| "failed")
+        .unwrap();
+
+        // should not fail
+        assert!(value);
+    }
 
     #[wasm_bindgen_test]
     async fn verify_encode_verifier_calldata() {