Update rust.yml

Cargo.lock

@@ -598,12 +598,6 @@ version = "1.0.81"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0952808a6c2afd1aa8947271f3a60f1a6763c7b912d210184c5149b5cf147247"
 
-[[package]]
-name = "anymap"
-version = "0.12.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "33954243bd79057c2de7338850b85983a44588021f8a5fee574a8888c6de4344"
-
 [[package]]
 name = "anymap2"
 version = "0.13.0"
@@ -3328,16 +3322,14 @@ dependencies = [
 
 [[package]]
 name = "ndarray"
-version = "0.16.1"
+version = "0.15.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841"
+checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
 dependencies = [
  "matrixmultiply",
  "num-complex",
  "num-integer",
  "num-traits",
- "portable-atomic",
- "portable-atomic-util",
  "rawpointer",
 ]
 
@@ -3865,15 +3857,6 @@ version = "1.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0"
 
-[[package]]
-name = "portable-atomic-util"
-version = "0.2.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d8a2f0d8d040d7848a709caf78912debcc3f33ee4b3cac47d73d1e1069e83507"
-dependencies = [
- "portable-atomic",
-]
-
 [[package]]
 name = "poseidon"
 version = "0.2.0"
@@ -5689,11 +5672,10 @@ dependencies = [
 
 [[package]]
 name = "tract-core"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "anyhow",
- "anymap",
  "bit-set",
  "derive-new",
  "downcast-rs",
@@ -5714,8 +5696,8 @@ dependencies = [
 
 [[package]]
 name = "tract-data"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "anyhow",
  "downcast-rs",
@@ -5729,7 +5711,6 @@ dependencies = [
  "nom",
  "num-integer",
  "num-traits",
- "parking_lot",
  "scan_fmt",
  "smallvec",
  "string-interner",
@@ -5737,8 +5718,8 @@ dependencies = [
 
 [[package]]
 name = "tract-hir"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "derive-new",
  "log",
@@ -5747,8 +5728,8 @@ dependencies = [
 
 [[package]]
 name = "tract-linalg"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "byteorder",
  "cc",
@@ -5774,8 +5755,8 @@ dependencies = [
 
 [[package]]
 name = "tract-nnef"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "byteorder",
  "flate2",
@@ -5788,8 +5769,8 @@ dependencies = [
 
 [[package]]
 name = "tract-onnx"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "bytes",
  "derive-new",
@@ -5805,8 +5786,8 @@ dependencies = [
 
 [[package]]
 name = "tract-onnx-opl"
-version = "0.21.8-pre"
-source = "git+https://github.com/sonos/tract/?rev=37132e0397d0a73e5bd3a8615d932dabe44f6736#37132e0397d0a73e5bd3a8615d932dabe44f6736"
+version = "0.21.6-pre"
+source = "git+https://github.com/sonos/tract/?rev=40c64319291184814d9fea5fdf4fa16f5a4f7116#40c64319291184814d9fea5fdf4fa16f5a4f7116"
 dependencies = [
  "getrandom",
  "log",

Cargo.toml

@@ -89,7 +89,7 @@ pyo3-asyncio = { git = "https://github.com/jopemachine/pyo3-asyncio/", branch =
     "tokio-runtime",
 ], default-features = false, optional = true }
 pyo3-log = { version = "0.10.0", default-features = false, optional = true }
-tract-onnx = { git = "https://github.com/sonos/tract/", rev = "37132e0397d0a73e5bd3a8615d932dabe44f6736", default-features = false, optional = true }
+tract-onnx = { git = "https://github.com/sonos/tract/", rev = "40c64319291184814d9fea5fdf4fa16f5a4f7116", default-features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
 metal = { git = "https://github.com/gfx-rs/metal-rs", optional = true }
 objc = { version = "0.2.4", optional = true }

examples/onnx/scatter_nd/gen.py

@@ -1,52 +1,75 @@
-from torch import nn
+import torch
+import torch.nn as nn
+import sys
 import json
-import numpy as np
-import tf2onnx
+
+sys.path.append("..")
+
+class Model(nn.Module):
+    """
+    Just one Linear layer
+    """
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+
+        # Use this line if you want to visualize the weights
+        # self.Linear.weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
+        self.channels = configs.enc_in
+        self.individual = configs.individual
+        if self.individual:
+            self.Linear = nn.ModuleList()
+            for i in range(self.channels):
+                self.Linear.append(nn.Linear(self.seq_len,self.pred_len))
+        else:
+            self.Linear = nn.Linear(self.seq_len, self.pred_len)
+
+    def forward(self, x):
+        # x: [Batch, Input length, Channel]
+        if self.individual:
+            output = torch.zeros([x.size(0),self.pred_len,x.size(2)],dtype=x.dtype).to(x.device)
+            for i in range(self.channels):
+                output[:,:,i] = self.Linear[i](x[:,:,i])
+            x = output
+        else:
+            x = self.Linear(x.permute(0,2,1)).permute(0,2,1)
+        return x # [Batch, Output length, Channel]
+
+class Configs:
+    def __init__(self, seq_len, pred_len, enc_in=321, individual=True):
+      self.seq_len = seq_len
+      self.pred_len = pred_len
+      self.enc_in = enc_in
+      self.individual = individual
+
+model = 'Linear'
+seq_len = 10
+pred_len = 4
+enc_in = 3
+
+configs = Configs(seq_len, pred_len, enc_in, True)
+circuit = Model(configs)
+
+x = torch.randn(1, seq_len, pred_len)
 
 
-import tensorflow as tf
-from tensorflow.keras.layers import *
-from tensorflow.keras.models import Model
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=15,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  # the model's input names
+                  input_names=['input'],
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
 
 
-# gather_nd in tf then export to onnx
-x = in1 = Input((4, 1), dtype=tf.int32)
-w = in2 = Input((4, ), dtype=tf.int32)
-
-class MyLayer(Layer):
-    def call(self, x, w):
-        shape = tf.constant([8])
-        return tf.scatter_nd(x, w, shape)
-
-x = MyLayer()(x, w)
-
-
-
-tm = Model((in1, in2), x)
-tm.summary()
-tm.compile(optimizer='adam', loss='mse')
-
-shape = [1, 4, 1]
-index_shape = [1, 4]
-# After training, export to onnx (network.onnx) and create a data file (input.json)
-x = np.random.randint(0, 4, shape)
-# w = random int tensor
-w = np.random.randint(0, 4, index_shape)
-
-spec = tf.TensorSpec(shape, tf.int32, name='input_0')
-index_spec = tf.TensorSpec(index_shape, tf.int32, name='input_1')
-
-model_path = "network.onnx"
-
-tf2onnx.convert.from_keras(tm, input_signature=[spec, index_spec], inputs_as_nchw=['input_0', 'input_1'], opset=12, output_path=model_path)
-
-
-d = x.reshape([-1]).tolist()
-d1 = w.reshape([-1]).tolist()
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
 
 
 data = dict(
-    input_data=[d, d1],
+    input_data=[d1],
 )
 
 # Serialize data into file:

examples/onnx/scatter_nd/input.json

@@ -1,16 +1 @@
-{
-    "input_data": [
-        [
-            0,
-            1,
-            2,
-            3
-        ],
-        [
-            1,
-            0,
-            2,
-            1
-        ]
-    ]
-}
+{"input_data": [[0.1874287724494934, 1.0498261451721191, 0.22384068369865417, 1.048445224761963, -0.5670360326766968, -0.38653188943862915, 0.12878702580928802, -2.3675858974456787, 0.5800458192825317, -0.43653929233551025, -0.2511898875236511, 0.3324051797389984, 0.27960312366485596, 0.4763695001602173, 0.3796705901622772, 1.1334782838821411, -0.87981778383255, -1.2451434135437012, 0.7672272324562073, -0.24404007196426392, -0.6875824928283691, 0.3619358539581299, -0.10131897777318954, 0.7169521450996399, 1.6585893630981445, -0.5451845526695251, 0.429487019777298, 0.7426952123641968, -0.2543637454509735, 0.06546942889690399, 0.7939824461936951, 0.1579471379518509, -0.043604474514722824, -0.8621711730957031, -0.5344759821891785, -0.05880478024482727, -0.17351101338863373, 0.5095029473304749, -0.7864817976951599, -0.449171245098114]]}

src/circuit/ops/layouts.rs

@@ -1687,7 +1687,6 @@ pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd + std::
     Ok(output.into())
 }
 
-// assumes unique values in fullset
 pub(crate) fn get_missing_set_elements<
     F: PrimeField + TensorType + PartialOrd + std::hash::Hash,
 >(
@@ -1700,8 +1699,6 @@ pub(crate) fn get_missing_set_elements<
     let set_len = fullset.len();
     input.flatten();
 
-    // while fullset is less than len of input concat
-
     let is_assigned = !input.any_unknowns()? && !fullset.any_unknowns()?;
 
     let mut claimed_output: ValTensor<F> = if is_assigned {

src/graph/model.rs

@@ -656,7 +656,7 @@ impl Model {
 
         let mut symbol_values = SymbolValues::default();
         for (symbol, value) in run_args.variables.iter() {
-            let symbol = model.symbols.sym(symbol);
+            let symbol = model.symbol_table.sym(symbol);
             symbol_values = symbol_values.with(&symbol, *value as i64);
             debug!("set {} to {}", symbol, value);
         }
@@ -1199,9 +1199,9 @@ impl Model {
             // Then number of columns in the circuits
             #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
             region.debug_report();
-            trace!("input indices: {:?}", node.inputs());
-            trace!("output scales: {:?}", node.out_scales());
-            trace!(
+            debug!("input indices: {:?}", node.inputs());
+            debug!("output scales: {:?}", node.out_scales());
+            debug!(
                 "input scales: {:?}",
                 node.inputs()
                     .iter()
@@ -1220,8 +1220,8 @@ impl Model {
                 // we re-assign inputs, always from the 0 outlet
                 vec![results.get(idx).ok_or(GraphError::MissingResults)?[0].clone()]
             };
-            trace!("output dims: {:?}", node.out_dims());
-            trace!(
+            debug!("output dims: {:?}", node.out_dims());
+            debug!(
                 "input dims {:?}",
                 values.iter().map(|v| v.dims()).collect_vec()
             );

src/graph/utilities.rs

@@ -1007,21 +1007,21 @@ pub fn new_op_from_onnx(
             op
         }
         "Iff" => SupportedOp::Linear(PolyOp::Iff),
-        "<" => {
+        "Less" => {
             if inputs.len() == 2 {
                 SupportedOp::Hybrid(HybridOp::Less)
             } else {
                 return Err(GraphError::InvalidDims(idx, "less".to_string()));
             }
         }
-        "<=" => {
+        "LessEqual" => {
             if inputs.len() == 2 {
                 SupportedOp::Hybrid(HybridOp::LessEqual)
             } else {
                 return Err(GraphError::InvalidDims(idx, "less equal".to_string()));
             }
         }
-        ">" => {
+        "Greater" => {
             // Extract the slope layer hyperparams
             if inputs.len() == 2 {
                 SupportedOp::Hybrid(HybridOp::Greater)
@@ -1029,7 +1029,7 @@ pub fn new_op_from_onnx(
                 return Err(GraphError::InvalidDims(idx, "greater".to_string()));
             }
         }
-        ">=" => {
+        "GreaterEqual" => {
             // Extract the slope layer hyperparams
             if inputs.len() == 2 {
                 SupportedOp::Hybrid(HybridOp::GreaterEqual)
@@ -1250,7 +1250,7 @@ pub fn new_op_from_onnx(
         "And" => SupportedOp::Linear(PolyOp::And),
         "Or" => SupportedOp::Linear(PolyOp::Or),
         "Xor" => SupportedOp::Linear(PolyOp::Xor),
-        "==" => SupportedOp::Hybrid(HybridOp::Equals),
+        "Equals" => SupportedOp::Hybrid(HybridOp::Equals),
         "Deconv" => {
             let deconv_node: &Deconv = match node.op().downcast_ref::<Deconv>() {
                 Some(b) => b,

src/tensor/mod.rs

@@ -1109,13 +1109,6 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     pub fn expand(&self, shape: &[usize]) -> Result<Self, TensorError> {
-        // if both have length 1 then we can just return the tensor
-        if self.dims().iter().product::<usize>() == 1 && shape.iter().product::<usize>() == 1 {
-            let mut output = self.clone();
-            output.reshape(shape)?;
-            return Ok(output);
-        }
-
         if self.dims().len() > shape.len() {
             return Err(TensorError::DimError(format!(
                 "Cannot expand {:?} to the smaller shape {:?}",

src/tensor/ops.rs

@@ -1050,7 +1050,6 @@ pub fn scatter_nd<T: TensorType + Send + Sync>(
             let slice = coord.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
             let index_val = index.get_slice(&slice)?;
             let index_slice = index_val.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
-
             let src_val = src.get_slice(&slice)?;
             output.set_slice(&index_slice, &src_val)?;
             Ok::<_, TensorError>(())

tests/integration_tests.rs

@@ -636,7 +636,7 @@ mod native_tests {
             #(#[test_case(TESTS[N])])*
             fn mock_large_batch_public_outputs_(test: &str) {
                 // currently variable output rank is not supported in ONNX
-                if test != "gather_nd" && test != "lstm_large"  && test != "lstm_medium" && test != "scatter_nd" {
+                if test != "gather_nd" && test != "lstm_large"  && test != "lstm_medium" {
                     crate::native_tests::init_binary();
                     let test_dir = TempDir::new(test).unwrap();
                     let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);