Add RandomNormal ONNX operator (#2200)

candle-onnx/src/eval.rs

@@ -971,7 +971,7 @@ pub fn simple_eval(
                 };
                 values.insert(node.output[0].clone(), output);
             }
-            "RandomUniform" => {
+            random_type @ ("RandomUniform" | "RandomNormal") => {
                 let dt: i64 = get_attr_opt(node, "dtype")?.copied().unwrap_or(1); // 1 is float
                                                                                   // type by
                                                                                   // default
@@ -979,36 +979,42 @@ pub fn simple_eval(
                     Ok(dt) => match dtype(dt) {
                         Some(DType::U8 | DType::U32 | DType::I64) => {
                             bail!(
-                                "unsupported 'dtype' value {dt:?}, only floats are allowed, for RandomUnifrom {}",
+                                "unsupported 'dtype' value {dt:?}, only floats are allowed, for {random_type} {}",
                                 node.name
                             )
                         }
                         Some(dt) => dt,
                         None => {
                             bail!(
-                                "unsupported 'dtype' value {dt:?} for RandomUnifrom {}",
+                                "unsupported 'dtype' value {dt:?} for {random_type} {}",
                                 node.name
                             )
                         }
                     },
                     Err(_) => {
                         bail!(
-                            "unsupported 'dtype' value {dt:?} for RandomUniform {}",
+                            "unsupported 'dtype' value {dt:?} for {random_type} {}",
                             node.name
                         )
                     }
                 };
-                let low: f32 = get_attr_opt(node, "low")?.copied().unwrap_or(0.0);
-                let high: f32 = get_attr_opt(node, "high")?.copied().unwrap_or(1.0);
                 let seed: Option<f32> = get_attr_opt(node, "seed")?.copied();
                 if seed.is_some() {
-                    bail!("seed for RandomUniform is currently not supported")
+                    bail!("seed for {random_type} is currently not supported")
                 };
                 let shape: Vec<usize> = get_attr::<[i64]>(node, "shape")?
                     .iter()
                     .map(|x| *x as usize)
                     .collect();
-                let output = Tensor::rand(low, high, shape, &Device::Cpu)?.to_dtype(dtype)?;
+                let output = if random_type == "RandomUniform" {
+                    let low: f32 = get_attr_opt(node, "low")?.copied().unwrap_or(0.0);
+                    let high: f32 = get_attr_opt(node, "high")?.copied().unwrap_or(1.0);
+                    Tensor::rand(low, high, shape, &Device::Cpu)?.to_dtype(dtype)?
+                } else {
+                    let mean: f32 = get_attr_opt(node, "mean")?.copied().unwrap_or(0.0);
+                    let scale: f32 = get_attr_opt(node, "scale")?.copied().unwrap_or(1.0);
+                    Tensor::randn(mean, scale, shape, &Device::Cpu)?.to_dtype(dtype)?
+                };
                 values.insert(node.output[0].clone(), output);
             }
             op_type => bail!("unsupported op_type {op_type} for op {node:?}"),

candle-onnx/tests/ops.rs

@@ -2020,6 +2020,150 @@ fn test_random_uniform() -> Result<()> {
     Ok(())
 }
 
+// "RandomNormal"
+#[test]
+fn test_random_normal() -> Result<()> {
+    test(vec![3, 2, 1, 4], None, None)?;
+    test(vec![2, 2, 2, 2], Some(-10.0), None)?;
+    test(vec![2, 2, 2, 2], None, Some(10.0))?;
+    test(vec![1, 2, 3, 4], Some(-10.0), Some(10.0))?;
+
+    fn test(shape: Vec<i64>, mean: Option<f32>, scale: Option<f32>) -> Result<()> {
+        let att_mean = AttributeProto {
+            name: "mean".to_string(),
+            ref_attr_name: "mean".to_string(),
+            i: 0,
+            doc_string: "mean".to_string(),
+            r#type: 1, // FLOAT
+            f: mean.unwrap_or(0.0),
+            s: vec![],
+            t: None,
+            g: None,
+            sparse_tensor: None,
+            tp: None,
+            floats: vec![],
+            ints: vec![],
+            strings: vec![],
+            tensors: vec![],
+            graphs: vec![],
+            sparse_tensors: vec![],
+            type_protos: vec![],
+        };
+        let att_scale = AttributeProto {
+            name: "scale".to_string(),
+            ref_attr_name: "scale".to_string(),
+            i: 0,
+            doc_string: "scale".to_string(),
+            r#type: 1, // FLOAT
+            f: scale.unwrap_or(1.0),
+            s: vec![],
+            t: None,
+            g: None,
+            sparse_tensor: None,
+            tp: None,
+            floats: vec![],
+            ints: vec![],
+            strings: vec![],
+            tensors: vec![],
+            graphs: vec![],
+            sparse_tensors: vec![],
+            type_protos: vec![],
+        };
+        let att_shape = AttributeProto {
+            name: "shape".to_string(),
+            ref_attr_name: "shape".to_string(),
+            i: 0,
+            doc_string: "shape".to_string(),
+            r#type: 7, // INTS
+            f: 0.0,
+            s: vec![],
+            t: None,
+            g: None,
+            sparse_tensor: None,
+            tp: None,
+            floats: vec![],
+            ints: shape,
+            strings: vec![],
+            tensors: vec![],
+            graphs: vec![],
+            sparse_tensors: vec![],
+            type_protos: vec![],
+        };
+        let att_dtype = AttributeProto {
+            name: "dtype".to_string(),
+            ref_attr_name: "dtype".to_string(),
+            i: 11, // DOUBLE
+            doc_string: "dtype".to_string(),
+            r#type: 2, // INT
+            f: 0.0,
+            s: vec![],
+            t: None,
+            g: None,
+            sparse_tensor: None,
+            tp: None,
+            floats: vec![],
+            ints: vec![],
+            strings: vec![],
+            tensors: vec![],
+            graphs: vec![],
+            sparse_tensors: vec![],
+            type_protos: vec![],
+        };
+        let attrs = {
+            let mut mut_attrs = vec![att_shape, att_dtype];
+            if mean.is_some() {
+                mut_attrs.push(att_mean);
+            }
+            if scale.is_some() {
+                mut_attrs.push(att_scale);
+            }
+            mut_attrs
+        };
+        let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+            node: vec![NodeProto {
+                op_type: "RandomNormal".to_string(),
+                domain: "".to_string(),
+                attribute: attrs,
+                input: vec![],
+                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 eval = candle_onnx::simple_eval(&manual_graph, HashMap::new())?;
+        assert_eq!(eval.len(), 1);
+
+        let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
+        let data = z.flatten_all()?.to_vec1::<f64>()?;
+
+        // test if values are unique
+        for (i, a) in data.iter().enumerate() {
+            for (j, b) in data.iter().enumerate() {
+                if i == j {
+                    continue;
+                };
+                assert_ne!(a, b);
+            }
+        }
+
+        Ok(())
+    }
+
+    Ok(())
+}
+
 // "Range"
 #[test]
 fn test_range() -> Result<()> {