ONNX: Add Floor and Ceil (huggingface#2235)

wonkyDD · Jun 2, 2024 · 03344d3 · 03344d3
1 parent 1ec3b2c
commit 03344d3
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Showing 2 changed files with 162 additions and 0 deletions.
diff --git a/candle-onnx/src/eval.rs b/candle-onnx/src/eval.rs
@@ -877,6 +877,16 @@ pub fn simple_eval(
                 let output = input.relu()?;
                 values.insert(node.output[0].clone(), output);
             }
+            "Ceil" => {
+                let input = get(&node.input[0])?;
+                let output = input.ceil()?;
+                values.insert(node.output[0].clone(), output);
+            }
+            "Floor" => {
+                let input = get(&node.input[0])?;
+                let output = input.floor()?;
+                values.insert(node.output[0].clone(), output);
+            }
             // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Constant
             "Constant" => {
                 let value = match node.attribute.iter().find(|attr| attr.name == "value") {

diff --git a/candle-onnx/tests/ops.rs b/candle-onnx/tests/ops.rs
@@ -2556,3 +2556,155 @@ fn test_where() -> Result<()> {
 
     Ok(())
 }
+
+#[test]
+fn test_floor() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Floor".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![ValueInfoProto {
+            name: INPUT_X.to_string(),
+            doc_string: "".to_string(),
+            r#type: None,
+        }],
+        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 x = Tensor::from_vec(
+        // some values taken from https://numpy.org/doc/stable/reference/generated/numpy.floor.html
+        vec![
+            f64::NAN,
+            f64::INFINITY,
+            f64::NEG_INFINITY,
+            -1.7,
+            -1.5,
+            -0.2,
+            0.2,
+            1.5,
+            1.7,
+            2.0,
+        ],
+        &[10],
+        &Device::Cpu,
+    )?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+
+    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 results = z.to_vec1::<f64>()?;
+
+    assert!(results[0].is_nan());
+    assert_eq!(
+        results[1..],
+        vec![
+            f64::INFINITY,
+            f64::NEG_INFINITY,
+            -2.,
+            -2.,
+            -1.,
+            0.,
+            1.,
+            1.,
+            2.
+        ]
+    );
+
+    Ok(())
+}
+
+#[test]
+fn test_ceil() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Ceil".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![ValueInfoProto {
+            name: INPUT_X.to_string(),
+            doc_string: "".to_string(),
+            r#type: None,
+        }],
+        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 x = Tensor::from_vec(
+        // some values taken from https://numpy.org/doc/stable/reference/generated/numpy.ceil.html
+        vec![
+            f64::NAN,
+            f64::INFINITY,
+            f64::NEG_INFINITY,
+            -1.7,
+            -1.5,
+            -0.2,
+            0.2,
+            1.5,
+            1.7,
+            2.0,
+        ],
+        &[10],
+        &Device::Cpu,
+    )?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+
+    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 results = z.to_vec1::<f64>()?;
+
+    assert!(results[0].is_nan());
+    assert_eq!(
+        results[1..],
+        vec![
+            f64::INFINITY,
+            f64::NEG_INFINITY,
+            -1.,
+            -1.,
+            -0.,
+            1.,
+            2.,
+            2.,
+            2.
+        ]
+    );
+
+    Ok(())
+}