Add cross product operator

docs_input/api/linalg/other/cross.rst

@@ -0,0 +1,20 @@
+.. _cross_func:
+
+cross
+#####
+
+Cross product of two operators with last dimension 2 or 3.
+
+Inputs `A` and `B` may be higher rank than 1, in which case batching will occur
+on all dimensions besides the last dimension.
+
+.. doxygenfunction:: cross(const OpA &A, const OpB &B)
+
+Examples
+~~~~~~~~
+
+.. literalinclude:: ../../../../test/00_Operator/OperatorTests.cu
+   :language: cpp
+   :start-after: example-begin cross-test-1
+   :end-before: example-end cross-test-1
+   :dedent:

include/matx/operators/cross.h

@@ -0,0 +1,200 @@
+////////////////////////////////////////////////////////////////////////////////
+// BSD 3-Clause License
+//
+// COpBright (c) 2021, NVIDIA Corporation
+// All rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// 1. Redistributions of source code must retain the above cOpBright notice, this
+//    list of conditions and the following disclaimer.
+//
+// 2. Redistributions in binary form must reproduce the above cOpBright notice,
+//    this list of conditions and the following disclaimer in the documentation
+//    and/or other materials provided with the distribution.
+//
+// 3. Neither the name of the cOpBright holder nor the names of its
+//    contributors may be used to endorse or promote products derived from
+//    this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COpBRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+// DISCLAIMED. IN NO EVENT SHALL THE COpBRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+/////////////////////////////////////////////////////////////////////////////////
+
+#pragma once
+
+#include "matx/core/type_utils.h"
+#include "matx/operators/base_operator.h"
+
+namespace matx
+{
+
+  /**
+   * Returns cross product of two operators when the last dimensions are 2 or 3
+   */
+  namespace detail {
+    template <typename OpA, typename OpB>
+    class CrossOp : public BaseOp<CrossOp<OpA, OpB>>
+    {
+      private:
+        typename detail::base_type_t<OpA> a_;
+        typename detail::base_type_t<OpB> b_;
+
+        static constexpr int32_t out_rank = cuda::std::max(OpA::Rank(), OpB::Rank());
+        static constexpr int32_t min_rank = cuda::std::min(OpA::Rank(), OpB::Rank());
+
+        cuda::std::array<index_t, out_rank> out_dims_;
+
+        //helpers to simplify later checks
+        bool isA2D_ = a_.Size(a_.Rank() - 1) == 2 ? true : false;
+        bool isB2D_ = b_.Size(b_.Rank() - 1) == 2 ? true : false;
+
+      public:
+        using matxop = bool;
+        using value_type = typename OpA::value_type;
+
+        __MATX_INLINE__ std::string str() const { return "cross()"; }
+        __MATX_INLINE__ CrossOp(const OpA &A, const OpB &B) : a_(A), b_(B) {
+          MATX_STATIC_ASSERT_STR(OpA::Rank() >= 1 && OpB::Rank() >= 1, matxInvalidDim, "Operators to cross() must have rank GTE one.");
+
+          //dims other than the last are batched, so count R-->L, beginning one-left of the right-most dim
+          for (int32_t i = 1; i < min_rank; i++) {
+            MATX_ASSERT_STR(a_.Size(a_.Rank() - 1 - i) == b_.Size(b_.Rank() - 1 - i), matxInvalidSize, "A and B tensors must match batch sizes.");
+          }
+
+          MATX_ASSERT_STR(a_.Size(a_.Rank() - 1) == 3 || a_.Size(a_.Rank() - 1) == 2, matxInvalidSize, "Last dimension of A must have size 2 or 3.")
+          MATX_ASSERT_STR(b_.Size(b_.Rank() - 1) == 3 || b_.Size(b_.Rank() - 1) == 2, matxInvalidSize, "Last dimension of B must have size 2 or 3.")
+
+          for (int32_t i = 0; i < out_rank - 1; i++) {
+            if (i < a_.Rank()){
+              out_dims_[i] = a_.Size(i);
+            }
+            else{
+              out_dims_[i] = b_.Size(i);
+            }
+          }
+
+          //mimic NumPy cross as closely as possible
+          if(isA2D_ && isB2D_){
+            out_dims_[out_dims_.size() - 1] = 1;
+          }
+          else{
+            out_dims_[out_dims_.size() - 1] = 3;
+          }
+        };
+
+        template <typename... Is>
+        __MATX_INLINE__ __MATX_DEVICE__ __MATX_HOST__ decltype(auto) operator()(Is... indices) const
+        {
+          cuda::std::array idx{indices...};
+          auto idxOut = idx[idx.size() - 1];
+
+          //create references to individual slices for ease of notation
+          cuda::std::array idx0(idx);
+          cuda::std::array idx1(idx);
+          cuda::std::array idx2(idx);
+
+          idx0[idx0.size() - 1] = 0LL;
+          idx1[idx1.size() - 1] = 1LL;
+          idx2[idx2.size() - 1] = 2LL;
+
+          auto a0 = get_value(a_, idx0);
+          auto a1 = get_value(a_, idx1);
+
+          auto b0 = get_value(b_, idx0);
+          auto b1 = get_value(b_, idx1);
+
+          //lots of if-elses, but similar to numpy implementation
+
+          if (idxOut == 2 || (isA2D_ && isB2D_)){
+            return a0 * b1 - a1 * b0;
+          }
+
+          if (!isA2D_ && !isB2D_){
+            auto a2 = get_value(a_, idx2);
+            auto b2 = get_value(b_, idx2);
+            if (idxOut == 0){
+                return a1 * b2 - a2 * b1;
+            }
+            //idxOut == 1
+            return a2 * b0 - a0 * b2;
+
+          }
+          else if (isA2D_ && !isB2D_){
+            auto b2 = get_value(b_, idx2);
+            if (idxOut == 0){
+                return a1 * b2;
+            }
+            //idxOut == 1
+            return -a0 * b2;
+          }
+          else{// !isA2D_ && isB2D_, case of both 2D are covered in the first if statement
+            auto a2 = get_value(a_, idx2);
+            if (idxOut == 0){
+                return -a2 * b1;
+            }
+            //idxOut == 1
+            return a2 * b0;
+          }
+
+        }
+        static __MATX_INLINE__ constexpr __MATX_HOST__ __MATX_DEVICE__ int32_t Rank()
+        {
+          return out_rank;
+        }
+
+        constexpr __MATX_INLINE__ __MATX_HOST__ __MATX_DEVICE__ index_t Size([[maybe_unused]] int dim) const
+        {
+          return out_dims_[dim];
+        }
+
+        template <typename ShapeType, typename Executor>
+        __MATX_INLINE__ void PreRun(ShapeType &&shape, Executor &&ex) const noexcept
+        {
+          if constexpr (is_matx_op<OpA>()) {
+            a_.PreRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
+          }
+
+          if constexpr (is_matx_op<OpB>()) {
+            b_.PreRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
+          }          
+        }
+
+        template <typename ShapeType, typename Executor>
+        __MATX_INLINE__ void PostRun(ShapeType &&shape, Executor &&ex) const noexcept
+        {
+          if constexpr (is_matx_op<OpA>()) {
+            a_.PostRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
+          }     
+
+          if constexpr (is_matx_op<OpB>()) {
+            b_.PostRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
+          } 
+        }    
+    };
+  }
+
+
+  /**
+   * @brief Evaluate a cross product
+   *  
+   * @tparam OpA Type of input tensor 1
+   * @tparam OpB Type of input tensor 2
+   * @param OpA Input tensor 1
+   * @param OpB Input tensor 2
+   * @return cross operator 
+   */
+  template <typename OpA, typename OpB>
+  __MATX_INLINE__ auto cross(const OpA &A, const OpB &B) {
+    return detail::CrossOp(A, B);
+  }
+} // end namespace matx

include/matx/operators/operators.h

@@ -50,6 +50,7 @@
 #include "matx/operators/comma.h"
 #include "matx/operators/corr.h"
 #include "matx/operators/cov.h"
+#include "matx/operators/cross.h"
 #include "matx/operators/cumsum.h"
 #include "matx/operators/diag.h"
 #include "matx/operators/dct.h"

test/00_operators/OperatorTests.cu

@@ -2012,6 +2012,67 @@ TYPED_TEST(OperatorTestsFloatAllExecs, Toeplitz)
   MATX_EXIT_HANDLER();
 }
 
+TYPED_TEST(OperatorTestsFloatNonComplexAllExecs, Cross)
+{
+  MATX_ENTER_HANDLER();
+  using TestType = cuda::std::tuple_element_t<0, TypeParam>;
+  using ExecType = cuda::std::tuple_element_t<1, TypeParam>;
+  ExecType exec{};   
+  auto pb = std::make_unique<detail::MatXPybind>();
+  // Half precision needs a bit more tolerance when compared to fp32
+  float thresh = 0.01f;
+  if constexpr (is_matx_half_v<TestType>) {
+    thresh = 0.02f;
+  }
+
+  {//batched 4 x 3
+    pb->InitAndRunTVGenerator<TestType>("00_operators", "cross_operator", "run", {4, 3});
+
+    auto a = make_tensor<TestType>({4, 3});
+    auto b = make_tensor<TestType>({4, 3});
+    auto out = make_tensor<TestType>({4, 3});
+
+    pb->NumpyToTensorView(a, "a");
+    pb->NumpyToTensorView(b, "b");
+
+    // example-begin cross-test-1
+    (out = cross(a, b)).run(exec);
+    // example-end cross-test-1
+    exec.sync();
+    MATX_TEST_ASSERT_COMPARE(pb, out, "out", thresh);
+  }
+
+  {//non-batched 3
+    pb->InitAndRunTVGenerator<TestType>("00_operators", "cross_operator", "run", {3});
+    auto a = make_tensor<TestType>({3});
+    auto b = make_tensor<TestType>({3});
+    auto out = make_tensor<TestType>({3});
+
+    pb->NumpyToTensorView(a, "a");
+    pb->NumpyToTensorView(b, "b");
+
+    (out = cross(a, b)).run(exec);
+    exec.sync();
+    MATX_TEST_ASSERT_COMPARE(pb, out, "out", thresh);
+  }
+
+  {//non-batched 2
+    pb->InitAndRunTVGenerator<TestType>("00_operators", "cross_operator", "run", {2});
+    auto a = make_tensor<TestType>({2});
+    auto b = make_tensor<TestType>({2});
+    auto out = make_tensor<TestType>({1});
+
+    pb->NumpyToTensorView(a, "a");
+    pb->NumpyToTensorView(b, "b");
+
+    (out = cross(a, b)).run(exec);
+    exec.sync();
+    MATX_TEST_ASSERT_COMPARE(pb, out, "out", thresh);
+  }
+
+  MATX_EXIT_HANDLER();
+}
+
 TYPED_TEST(OperatorTestsNumericNonComplexAllExecs, OperatorFuncs)
 {
   MATX_ENTER_HANDLER();

test/test_vectors/generators/00_operators.py

@@ -21,7 +21,27 @@ def run(self) -> Dict[str, np.array]:
             'out': np.polyval(c, x),
         }
 
+class cross_operator:
+    def __init__(self, dtype: str, size: List[int]):
+        self.size = size
+        self.dtype = dtype        
+        pass
 
+    def run(self) -> Dict[str, np.array]:
+        a = matx_common.randn_ndarray(self.size, self.dtype)
+        b = matx_common.randn_ndarray(self.size, self.dtype)
+
+        #for the result so that the rank matches that of the MatX output
+        result = np.cross(a, b)
+        if len(result.shape) == 0:
+            result = np.expand_dims(result, axis=0)
+
+        return {
+            'a': a,
+            'b': b,
+            'out': result
+        }    
+
 class kron_operator:
     def __init__(self, dtype: str, size: List[int]):
         pass