Add rsqrt

Author: andrewdalpino

CHANGELOG.md

@@ -3,6 +3,7 @@
 - 4.1.0
     - Added `softplus()` and `erf()` special functions
     - Added `sinh()` and `cosh()` trigonometric functions
+    - Added reciprocal square root `rsqrt()` function
 
 - 4.0.0
     - Data are now backed by a contiguous C buffer instead of PHP array

docs/Matrix.md

@@ -419,6 +419,7 @@ See [Unary](interfaces/unary.md). Each method returns a new `Matrix`.
 - `abs() : self` — absolute value of each element
 - `square() : self` — square of the matrix element-wise
 - `sqrt() : self` — square root of the matrix
+- `rsqrt() : self` — reciprocal square root of the matrix
 - `reciprocal() : self` — element-wise reciprocal of the matrix
 - `exp() : self` — exponential of the matrix
 - `expm1() : self` — exponential of each element minus 1

docs/Vector.md

@@ -302,6 +302,7 @@ See [Unary](interfaces/unary.md). Each method returns a new `Vector`.
 - `abs() : self` — absolute value of the vector
 - `square() : self` — square the vector
 - `sqrt() : self` — square root of the vector
+- `rsqrt() : self` — reciprocal square root of the vector
 - `reciprocal() : self` — element-wise reciprocal of the vector
 - `exp() : self` — exponentiate each element
 - `expm1() : self` — exponential of each element minus 1

docs/interfaces/tensor.md

@@ -20,7 +20,7 @@ interface Tensor extends ArrayLike, Arithmetic, Comparable,
 | [ArrayLike](arraylike.md) | `shape`, `shapeString`, `size`, `map`, `reduce`, `asArray` |
 | [Arithmetic](arithmetic.md) | `multiply`, `divide`, `add`, `subtract`, `pow`, `mod` |
 | [Comparable](comparable.md) | `equal`, `notEqual`, `greater`, `greaterEqual`, `less`, `lessEqual` |
-| [Unary](unary.md) | `abs`, `square`, `sqrt`, `exp`, `expm1`, `log`, `log1p`, `sigmoid`, `softplus`, `round`, `floor`, `ceil`, `sign`, `negate`, `clip`, `clipLower`, `clipUpper` |
+| [Unary](unary.md) | `abs`, `square`, `sqrt`, `rsqrt`, `exp`, `expm1`, `log`, `log1p`, `sigmoid`, `softplus`, `round`, `floor`, `ceil`, `sign`, `negate`, `clip`, `clipLower`, `clipUpper` |
 | [Trigonometric](trigonometric.md) | `sin`, `asin`, `cos`, `acos`, `tan`, `atan`, `tanh`, `sinh`, `cosh`, `rad2deg`, `deg2rad` |
 | [Reductions](reductions.md) | `sum`, `product`, `min`, `max`, `argmin`, `argmax`, `mean`, `variance`, `median`, `quantile` |
 | [Special](special.md) | `softmax`, `erf` |

docs/interfaces/unary.md

@@ -26,6 +26,10 @@ Square the tensor.
 
 Return the square root of the tensor.
 
+### `rsqrt() : mixed`
+
+Return the reciprocal square root of the tensor.
+
 ### `reciprocal() : mixed`
 
 Return the element-wise reciprocal of the tensor.

ext/include/unary.c

@@ -86,6 +86,7 @@
 
 TENSOR_UNARY_DISPATCH(abs, fabs(va[i]))
 TENSOR_UNARY_DISPATCH(sqrt, sqrt(va[i]))
+TENSOR_UNARY_DISPATCH(rsqrt, 1.0 / sqrt(va[i]))
 TENSOR_UNARY_DISPATCH(floor, floor(va[i]))
 TENSOR_UNARY_DISPATCH(ceil, ceil(va[i]))
 TENSOR_UNARY_DISPATCH(negate, -va[i])
@@ -466,6 +467,7 @@ void tensor_unary_dispatch_sse_init(void)
 {
 	tensor_abs_route = tensor_abs_sse;
 	tensor_sqrt_route = tensor_sqrt_sse;
+	tensor_rsqrt_route = tensor_rsqrt_sse;
 	tensor_floor_route = tensor_floor_sse;
 	tensor_ceil_route = tensor_ceil_sse;
 	tensor_negate_route = tensor_negate_sse;
@@ -484,6 +486,7 @@ void tensor_unary_dispatch_avx_init(void)
 {
 	tensor_abs_route = tensor_abs_avx;
 	tensor_sqrt_route = tensor_sqrt_avx;
+	tensor_rsqrt_route = tensor_rsqrt_avx;
 	tensor_floor_route = tensor_floor_avx;
 	tensor_ceil_route = tensor_ceil_avx;
 	tensor_negate_route = tensor_negate_avx;
@@ -502,6 +505,7 @@ void tensor_unary_dispatch_avx512_init(void)
 {
 	tensor_abs_route = tensor_abs_avx512;
 	tensor_sqrt_route = tensor_sqrt_avx512;
+	tensor_rsqrt_route = tensor_rsqrt_avx512;
 	tensor_floor_route = tensor_floor_avx512;
 	tensor_ceil_route = tensor_ceil_avx512;
 	tensor_negate_route = tensor_negate_avx512;

ext/include/unary.h

@@ -5,6 +5,7 @@
 
 void tensor_abs(zval * return_value, zval * a);
 void tensor_sqrt(zval * return_value, zval * a);
+void tensor_rsqrt(zval * return_value, zval * a);
 void tensor_exp(zval * return_value, zval * a);
 void tensor_expm1(zval * return_value, zval * a);
 void tensor_log(zval * return_value, zval * a);