Scalar support in minimum/maximum. (#832)

* Scalar support in minimum/maximum.

* Add a clamp method to tensors.

candle-core/src/tensor.rs

@@ -105,6 +105,28 @@ macro_rules! binary_op {
     };
 }
 
+macro_rules! binary_op_scalar {
+    ($fn_name:ident, $op_name:ident) => {
+        pub fn $fn_name<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
+            let rhs = match rhs.to_tensor_scalar()? {
+                crate::scalar::TensorScalar::Tensor(rhs) => rhs,
+                crate::scalar::TensorScalar::Scalar(rhs) => rhs
+                    .to_dtype(self.dtype())?
+                    .to_device(self.device())?
+                    .broadcast_as(self.shape())?,
+            };
+            let shape = self.same_shape_binary_op(&rhs, stringify!($fn_name))?;
+            let storage = self.storage().binary_impl::<crate::op::$op_name>(
+                &*rhs.storage(),
+                self.layout(),
+                rhs.layout(),
+            )?;
+            let op = BackpropOp::new2(self, &rhs, |t1, t2| Op::Binary(t1, t2, BinaryOp::$op_name));
+            Ok(from_storage(storage, shape.clone(), op, false))
+        }
+    };
+}
+
 macro_rules! broadcast_binary_op {
     ($fn_name:ident, $inner_fn_name:ident) => {
         pub fn $fn_name(&self, rhs: &Self) -> Result<Self> {
@@ -447,8 +469,8 @@ impl Tensor {
     binary_op!(mul, Mul);
     binary_op!(sub, Sub);
     binary_op!(div, Div);
-    binary_op!(maximum, Maximum);
+    binary_op_scalar!(maximum, Maximum);
-    binary_op!(minimum, Minimum);
+    binary_op_scalar!(minimum, Minimum);
     broadcast_binary_op!(broadcast_add, add);
     broadcast_binary_op!(broadcast_mul, mul);
     broadcast_binary_op!(broadcast_sub, sub);
@@ -827,6 +849,11 @@ impl Tensor {
         self.cmp(rhs, CmpOp::Le)
     }
 
+    /// Clamp the tensor values to be between `min` and `max`.
+    pub fn clamp<T1: TensorOrScalar, T2: TensorOrScalar>(&self, min: T1, max: T2) -> Result<Self> {
+        self.maximum(min)?.minimum(max)
+    }
+
     /// Upsample the input tensor to the `(target_h, target_w)` size, taking the value of the
     /// nearest element.
     ///

candle-core/tests/tensor_tests.rs

@@ -33,6 +33,17 @@ fn tensor_2d(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn clamp(device: &Device) -> Result<()> {
+    let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
+    let tensor = Tensor::new(data, device)?;
+    let tensor = tensor.clamp(1.5, 6.2)?;
+    assert_eq!(
+        tensor.to_vec2::<f32>()?,
+        [[3.0, 1.5, 4.0, 1.5, 5.0], [2.0, 1.5, 6.2, 6.2, 2.0]],
+    );
+    Ok(())
+}
+
 fn binary_op(device: &Device) -> Result<()> {
     let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
     let tensor1 = Tensor::new(data, device)?;
@@ -908,6 +919,7 @@ test_device!(index_add, index_add_cpu, index_add_gpu);
 test_device!(gather, gather_cpu, gather_gpu);
 test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
 test_device!(randn, randn_cpu, randn_gpu);
+test_device!(clamp, clamp_cpu, clamp_gpu);
 
 // There was originally a bug on the CPU implementation for randn
 // https://github.com/huggingface/candle/issues/381