Add missing onnx operations (#2096)

* Add missing onnx operations

* Add tests and fix errors

* Run rustfmt

2 files changed, 736 insertions, 9 deletions

diff --git a/candle-onnx/src/eval.rs b/candle-onnx/src/eval.rs
index 75927822..417216d7 100644
--- a/candle-onnx/src/eval.rs
+++ b/candle-onnx/src/eval.rs
@@ -23,6 +23,11 @@ trait Attr {
     fn get(attr: &onnx::AttributeProto) -> Result<&Self>;
 }
 
+trait AttrOwned: Sized {
+    const TYPE: AttributeType;
+    fn get(attr: &onnx::AttributeProto) -> Result<Self>;
+}
+
 impl Attr for i64 {
     const TYPE: AttributeType = AttributeType::Int;
     fn get(attr: &onnx::AttributeProto) -> Result<&Self> {
@@ -51,6 +56,50 @@ impl Attr for str {
     }
 }
 
+impl AttrOwned for Tensor {
+    const TYPE: AttributeType = AttributeType::Tensor;
+    fn get(attr: &onnx::AttributeProto) -> Result<Self> {
+        let tensor_proto = match &attr.t {
+            Some(value) => value,
+            None => bail!(
+                "attribute {} was of type TENSOR, but no tensor was found",
+                attr.name
+            ),
+        };
+
+        let data_type = match DataType::try_from(tensor_proto.data_type) {
+            Ok(value) => value,
+            Err(_) => bail!(
+                "attribute {} of type TENSOR was an invalid data_type number {}",
+                attr.name,
+                tensor_proto.data_type
+            ),
+        };
+
+        let dtype = match dtype(data_type) {
+            Some(value) => value,
+            None => bail!(
+                "attribute {} of type TENSOR has an unsupported data_type {}",
+                attr.name,
+                data_type.as_str_name()
+            ),
+        };
+
+        let mut dims = Vec::with_capacity(tensor_proto.dims.len());
+        for dim in &tensor_proto.dims {
+            if dim < &0 {
+                bail!(
+                    "attribute {} of type TENSOR has a negative dimension, which is unsupported",
+                    attr.name
+                )
+            }
+            dims.push(*dim as usize)
+        }
+
+        Tensor::from_raw_buffer(&tensor_proto.raw_data, dtype, &dims, &Device::Cpu)
+    }
+}
+
 fn get_attr_<'a>(node: &'a onnx::NodeProto, name: &str) -> Result<&'a onnx::AttributeProto> {
     match node.attribute.iter().find(|attr| attr.name == name) {
         None => {
@@ -98,6 +147,24 @@ fn get_attr_opt<'a, T: Attr + ?Sized>(
     }
 }
 
+fn get_attr_opt_owned<T: AttrOwned>(node: &onnx::NodeProto, name: &str) -> Result<Option<T>> {
+    match node.attribute.iter().find(|attr| attr.name == name) {
+        None => Ok(None),
+        Some(attr) => {
+            if attr.r#type() != T::TYPE {
+                bail!(
+                    "unsupported type {:?} for '{name}' attribute in '{}' for {}",
+                    attr.r#type,
+                    node.op_type,
+                    node.name
+                )
+            }
+            let val = T::get(attr)?;
+            Ok(Some(val))
+        }
+    }
+}
+
 pub fn get_tensor(t: &onnx::TensorProto, name: &str) -> Result<Tensor> {
     let dims: Vec<usize> = t.dims.iter().map(|&x| x as usize).collect();
     match DataType::try_from(t.data_type) {
@@ -458,14 +525,17 @@ pub fn simple_eval(
                 }
                 values.insert(node.output[0].clone(), xs);
             }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#ConstantOfShape
             "ConstantOfShape" => {
-                let dims = get(&node.input[0])?;
-                let shape = dims
-                    .to_vec1::<i64>()?
-                    .into_iter()
-                    .map(|v| v as usize)
-                    .collect::<Vec<_>>();
-                let xs = Tensor::zeros(shape, DType::F32, dims.device())?;
+                let input = get(&node.input[0])?;
+                let value = get_attr_opt_owned::<Tensor>(node, "value")?.unwrap_or(Tensor::zeros(
+                    (),
+                    DType::F32,
+                    &Device::Cpu,
+                )?);
+
+                let xs = Tensor::ones(input.shape(), value.dtype(), input.device())?
+                    .broadcast_mul(&value)?;
                 values.insert(node.output[0].clone(), xs);
             }
             "Unsqueeze" => {
@@ -552,6 +622,82 @@ pub fn simple_eval(
                 let dims = Tensor::from_vec(dims, xs.rank(), xs.device())?;
                 values.insert(node.output[0].clone(), dims);
             }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sqrt
+            "Sqrt" => {
+                let xs = get(&node.input[0])?;
+                let output = xs.sqrt()?;
+                values.insert(node.output[0].clone(), output);
+            }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Range
+            "Range" => {
+                let start = get(&node.input[0])?;
+                let limit = get(&node.input[1])?;
+                let delta = get(&node.input[2])?;
+
+                macro_rules! arange_step {
+                    ($t: ty) => {
+                        Tensor::arange_step(
+                            start.to_vec0::<$t>()?,
+                            limit.to_vec0::<$t>()?,
+                            delta.to_vec0::<$t>()?,
+                            &Device::Cpu,
+                        )?
+                    };
+                }
+
+                let output = match start.dtype() {
+                    DType::U8 => arange_step!(u8),
+                    DType::U32 => arange_step!(u32),
+                    DType::I64 => arange_step!(i64),
+                    DType::BF16 => arange_step!(f32),
+                    DType::F16 => arange_step!(f32),
+                    DType::F32 => arange_step!(f32),
+                    DType::F64 => arange_step!(f64),
+                };
+
+                values.insert(node.output[0].clone(), output);
+            }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Greater
+            "Greater" => {
+                let a = get(&node.input[0])?;
+                let b = get(&node.input[1])?;
+
+                let output = a.broadcast_gt(b)?;
+                values.insert(node.output[0].clone(), output);
+            }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Less
+            "Less" => {
+                let a = get(&node.input[0])?;
+                let b = get(&node.input[1])?;
+
+                let output = a.broadcast_lt(b)?;
+                values.insert(node.output[0].clone(), output);
+            }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Log
+            "Log" => {
+                let a = get(&node.input[0])?;
+
+                let output = a.log()?;
+                values.insert(node.output[0].clone(), output);
+            }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Min
+            "Min" => {
+                let mut output = get(&node.input[0])?.clone();
+                for input in node.input.iter() {
+                    let input = get(input)?;
+                    output = output.broadcast_minimum(input)?
+                }
+
+                values.insert(node.output[0].clone(), output);
+            }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Where
+            "Where" => {
+                let cond = get(&node.input[0])?;
+                let a = get(&node.input[1])?;
+                let b = get(&node.input[2])?;
+                let output = cond.where_cond(a, b)?;
+                values.insert(node.output[0].clone(), output);
+            }
             "Conv" => {
                 // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Conv
                 let dilations = get_attr_opt::<[i64]>(node, "dilations")?;
diff --git a/candle-onnx/tests/ops.rs b/candle-onnx/tests/ops.rs
index fda76ec2..9b18170a 100644
--- a/candle-onnx/tests/ops.rs
+++ b/candle-onnx/tests/ops.rs
@@ -4,12 +4,16 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-use candle::{Device, NdArray, Result, Tensor};
+use candle::{DType, Device, NdArray, Result, Tensor};
+use candle_onnx::onnx;
+use candle_onnx::onnx::attribute_proto::AttributeType;
+use candle_onnx::onnx::tensor_proto::DataType;
 use candle_onnx::onnx::{AttributeProto, GraphProto, ModelProto, NodeProto, ValueInfoProto};
 use std::collections::HashMap;
 
 const INPUT_X: &str = "x";
 const INPUT_Y: &str = "y";
+const INPUT_A: &str = "a";
 const OUTPUT_Z: &str = "z";
 
 fn create_model_proto_with_graph(graph: Option<GraphProto>) -> ModelProto {
@@ -820,7 +824,137 @@ fn test_flatten_operation() -> Result<()> {
 // #[test]
 
 // "ConstantOfShape"
-// #[test]
+#[test]
+fn test_constant_of_shape() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-31
+    test(&[4i64, 3, 2], Some(1.), &[1., 1., 1.])?;
+
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-31
+    test(&[0.], Some(0i64), &[0i64])?;
+
+    // "value" defaults to 0 f32
+    test(&[1i64, 2, 3, 4], None as Option<i64>, &[0., 0., 0., 0.])?;
+
+    fn test(
+        input: impl NdArray,
+        value: Option<impl NdArray>,
+        expected: impl NdArray,
+    ) -> Result<()> {
+        let mut attribute = vec![];
+
+        if let Some(value) = value {
+            let tensor = Tensor::new(value, &Device::Cpu)?;
+
+            let (value, data_type) = match tensor.dtype() {
+                DType::U8 => (
+                    tensor.to_vec0::<u8>()?.to_le_bytes().to_vec(),
+                    DataType::Uint8,
+                ),
+                DType::U32 => (
+                    tensor.to_vec0::<u32>()?.to_le_bytes().to_vec(),
+                    DataType::Uint32,
+                ),
+                DType::I64 => (
+                    tensor.to_vec0::<i64>()?.to_le_bytes().to_vec(),
+                    DataType::Int64,
+                ),
+                DType::F32 => (
+                    tensor.to_vec0::<f32>()?.to_le_bytes().to_vec(),
+                    DataType::Float,
+                ),
+                DType::F64 => (
+                    tensor.to_vec0::<f64>()?.to_le_bytes().to_vec(),
+                    DataType::Double,
+                ),
+                _ => panic!("unsupported DType in test"),
+            };
+            let tensor = onnx::TensorProto {
+                data_type: data_type.into(),
+                dims: tensor.dims().iter().map(|v| *v as i64).collect(),
+                raw_data: value,
+                segment: None,
+                float_data: vec![],
+                int32_data: vec![],
+                string_data: vec![],
+                int64_data: vec![],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+                external_data: vec![],
+                data_location: 0,
+                double_data: vec![],
+                uint64_data: vec![],
+            };
+
+            attribute.push(AttributeProto {
+                name: "value".to_string(),
+                ref_attr_name: "value".to_string(),
+                i: 0,
+                doc_string: "value".to_string(),
+                r#type: AttributeType::Tensor.into(),
+                f: 0.0,
+                s: vec![],
+                t: Some(tensor),
+                g: None,
+                sparse_tensor: None,
+                tp: None,
+                floats: vec![],
+                ints: vec![],
+                strings: vec![],
+                tensors: vec![],
+                graphs: vec![],
+                sparse_tensors: vec![],
+                type_protos: vec![],
+            })
+        }
+
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "ConstantOfShape".to_string(),
+                domain: "".to_string(),
+                attribute,
+                input: vec![INPUT_X.to_string()],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(input, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval
+            .get(OUTPUT_Z)
+            .expect("Output 'z' not found")
+            .to_dtype(DType::F64)?;
+
+        let expected = Tensor::new(expected, &Device::Cpu)?.to_dtype(DType::F64)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+    Ok(())
+}
 
 // "Unsqueeze"
 // #[test]
@@ -1639,3 +1773,450 @@ fn test_reduce_mean() -> Result<()> {
 
     Ok(())
 }
+
+// "Sqrt"
+#[test]
+fn test_sqrt() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-155
+    test(&[1., 4., 9.], &[1., 2., 3.])?;
+
+    fn test(data: impl NdArray, expected: impl NdArray) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Sqrt".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![INPUT_X.to_string()],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(data, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
+
+        let expected = Tensor::new(expected, &Device::Cpu)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
+// "Range"
+#[test]
+fn test_range() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-113
+    test(1., 5., 2., &[1., 3.])?;
+
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-113
+    test(10i64, 6i64, -3i64, &[10i64, 7i64])?;
+
+    fn test(
+        start: impl NdArray,
+        limit: impl NdArray,
+        delta: impl NdArray,
+        expected: impl NdArray,
+    ) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Range".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![
+                    INPUT_X.to_string(),
+                    INPUT_Y.to_string(),
+                    INPUT_A.to_string(),
+                ],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(start, &Device::Cpu)?);
+        inputs.insert(INPUT_Y.to_string(), Tensor::new(limit, &Device::Cpu)?);
+        inputs.insert(INPUT_A.to_string(), Tensor::new(delta, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval
+            .get(OUTPUT_Z)
+            .expect("Output 'z' not found")
+            .to_dtype(DType::F64)?;
+
+        let expected = Tensor::new(expected, &Device::Cpu)?.to_dtype(DType::F64)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
+// "Greater"
+#[test]
+fn test_greater() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-63
+    test(&[1., 2., 3.], &[3., 2., 1.], &[0u8, 0, 1])?;
+
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-63
+    test(&[1., 2., 3.], 2., &[0u8, 0, 1])?;
+
+    fn test(a: impl NdArray, b: impl NdArray, expected: impl NdArray) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Greater".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![INPUT_X.to_string(), INPUT_Y.to_string()],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(a, &Device::Cpu)?);
+        inputs.insert(INPUT_Y.to_string(), Tensor::new(b, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval
+            .get(OUTPUT_Z)
+            .expect("Output 'z' not found")
+            .to_dtype(DType::F64)?;
+
+        let expected = Tensor::new(expected, &Device::Cpu)?.to_dtype(DType::F64)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
+// "Less"
+#[test]
+fn test_less() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-81
+    test(&[1., 2., 3.], &[3., 2., 1.], &[1u8, 0, 0])?;
+
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-81
+    test(&[1., 2., 3.], 2., &[1u8, 0, 0])?;
+
+    fn test(a: impl NdArray, b: impl NdArray, expected: impl NdArray) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Less".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![INPUT_X.to_string(), INPUT_Y.to_string()],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(a, &Device::Cpu)?);
+        inputs.insert(INPUT_Y.to_string(), Tensor::new(b, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval
+            .get(OUTPUT_Z)
+            .expect("Output 'z' not found")
+            .to_dtype(DType::F64)?;
+
+        let expected = Tensor::new(expected, &Device::Cpu)?.to_dtype(DType::F64)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
+// "Log"
+#[test]
+fn test_log() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-82
+    test(&[1., 10.], &[0., std::f64::consts::LN_10])?;
+
+    fn test(data: impl NdArray, expected: impl NdArray) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Log".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![INPUT_X.to_string()],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(data, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
+
+        let expected = Tensor::new(expected, &Device::Cpu)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
+// "Min"
+#[test]
+fn test_min() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-94
+    test(&[3., 2., 1.], &[1., 4., 4.], &[2., 5., 0.], &[1., 2., 0.])?;
+
+    fn test(
+        a: impl NdArray,
+        b: impl NdArray,
+        c: impl NdArray,
+        expected: impl NdArray,
+    ) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Min".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![
+                    INPUT_X.to_string(),
+                    INPUT_Y.to_string(),
+                    INPUT_A.to_string(),
+                ],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(a, &Device::Cpu)?);
+        inputs.insert(INPUT_Y.to_string(), Tensor::new(b, &Device::Cpu)?);
+        inputs.insert(INPUT_A.to_string(), Tensor::new(c, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
+
+        let expected = Tensor::new(expected, &Device::Cpu)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
+// "Where"
+#[test]
+fn test_where() -> Result<()> {
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-173
+    test(
+        &[[1u8, 0], [1, 1]],
+        &[[1i64, 2], [3, 4]],
+        &[[9i64, 8], [7, 6]],
+        &[[1i64, 8], [3, 4]],
+    )?;
+
+    // https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-173
+    test(
+        &[[1u8, 0], [1, 1]],
+        &[[1., 2.], [3., 4.]],
+        &[[9., 8.], [7., 6.]],
+        &[[1., 8.], [3., 4.]],
+    )?;
+
+    fn test(
+        condition: impl NdArray,
+        x: impl NdArray,
+        y: impl NdArray,
+        expected: impl NdArray,
+    ) -> Result<()> {
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "Where".to_string(),
+                domain: "".to_string(),
+                attribute: vec![],
+                input: vec![
+                    INPUT_X.to_string(),
+                    INPUT_Y.to_string(),
+                    INPUT_A.to_string(),
+                ],
+                output: vec![OUTPUT_Z.to_string()],
+                name: "".to_string(),
+                doc_string: "".to_string(),
+            }],
+            name: "".to_string(),
+            initializer: vec![],
+            input: vec![],
+            output: vec![ValueInfoProto {
+                name: OUTPUT_Z.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            }],
+            value_info: vec![],
+            doc_string: "".to_string(),
+            sparse_initializer: vec![],
+            quantization_annotation: vec![],
+        }));
+
+        let mut inputs: HashMap<String, Tensor> = HashMap::new();
+        inputs.insert(INPUT_X.to_string(), Tensor::new(condition, &Device::Cpu)?);
+        inputs.insert(INPUT_Y.to_string(), Tensor::new(x, &Device::Cpu)?);
+        inputs.insert(INPUT_A.to_string(), Tensor::new(y, &Device::Cpu)?);
+
+        let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval
+            .get(OUTPUT_Z)
+            .expect("Output 'z' not found")
+            .to_dtype(DType::F64)?;
+
+        let expected = Tensor::new(expected, &Device::Cpu)?.to_dtype(DType::F64)?;
+        match expected.dims().len() {
+            0 => assert_eq!(z.to_vec0::<f64>()?, expected.to_vec0::<f64>()?),
+            1 => assert_eq!(z.to_vec1::<f64>()?, expected.to_vec1::<f64>()?),
+            2 => assert_eq!(z.to_vec2::<f64>()?, expected.to_vec2::<f64>()?),
+            3 => assert_eq!(z.to_vec3::<f64>()?, expected.to_vec3::<f64>()?),
+            _ => unreachable!(),
+        };
+
+        Ok(())
+    }
+
+    Ok(())
+}