[TF:XLA] Add tf.cross support.

See #11788

PiperOrigin-RevId: 163287731

tensorflow/compiler/tests/binary_ops_test.py

@@ -765,6 +765,24 @@ class BinaryOpsTest(XLATestCase):
           np.array([1, 0], dtype=np.int32),
           expected=np.array([[1, 3], [2, 4]], dtype=dtype))
 
+  def testCross(self):
+    for dtype in self.float_types:
+      self._testBinary(
+          gen_math_ops.cross,
+          np.zeros((4, 3), dtype=dtype),
+          np.zeros((4, 3), dtype=dtype),
+          expected=np.zeros((4, 3), dtype=dtype))
+      self._testBinary(
+          gen_math_ops.cross,
+          np.array([1, 2, 3], dtype=dtype),
+          np.array([4, 5, 6], dtype=dtype),
+          expected=np.array([-3, 6, -3], dtype=dtype))
+      self._testBinary(
+          gen_math_ops.cross,
+          np.array([[1, 2, 3], [10, 11, 12]], dtype=dtype),
+          np.array([[4, 5, 6], [40, 50, 60]], dtype=dtype),
+          expected=np.array([[-3, 6, -3], [60, -120, 60]], dtype=dtype))
+
 
 if __name__ == "__main__":
   googletest.main()

tensorflow/compiler/tf2xla/kernels/BUILD

@@ -24,6 +24,7 @@ tf_kernel_library(
         "concat_op.cc",
         "const_op.cc",
         "conv_ops.cc",
+        "cross_op.cc",
         "cwise_ops.cc",
         "depthwise_conv_ops.cc",
         "diag_op.cc",

tensorflow/compiler/tf2xla/kernels/cross_op.cc

@@ -0,0 +1,87 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+
+namespace tensorflow {
+namespace {
+
+class CrossOp : public XlaOpKernel {
+ public:
+  explicit CrossOp(OpKernelConstruction* context) : XlaOpKernel(context) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    TensorShape in0_shape = ctx->InputShape(0);
+    TensorShape in1_shape = ctx->InputShape(1);
+    OP_REQUIRES(ctx, in0_shape == in1_shape,
+                errors::InvalidArgument("Both inputs must be of same shape: ",
+                                        in0_shape.DebugString(), " vs. ",
+                                        in1_shape.DebugString()));
+    OP_REQUIRES(ctx, in0_shape.dims() >= 1,
+                errors::InvalidArgument("Input must be at least 1D",
+                                        in0_shape.DebugString()));
+
+    auto inner_dim = in0_shape.dim_size(in0_shape.dims() - 1);
+    OP_REQUIRES(ctx, inner_dim == 3,
+                errors::FailedPrecondition(
+                    "Cross-products are only defined for 3-element vectors."));
+
+    // in0 is a [...,X,Y,Z,3]
+    // in1 is the same shape as in0
+    // So slice 0 is: in0[...,:,:,:,0:1]
+    // So slice 1 is: in0[...,:,:,:,1:2]
+    // So slice 2 is: in0[...,:,:,:,2:3]
+
+    std::vector<int64> starts(in0_shape.dims(), 0);
+    std::vector<int64> limits;
+    for (auto dim_size : in0_shape.dim_sizes()) {
+      limits.push_back(dim_size);
+    }
+    std::vector<int64> strides(in0_shape.dims(), 1);
+
+    xla::ComputationBuilder* b = ctx->builder();
+    auto in0 = ctx->Input(0);
+    auto in1 = ctx->Input(1);
+    starts.back() = 0;
+    limits.back() = 1;
+    auto u1 = b->Slice(in0, starts, limits, strides);
+    auto v1 = b->Slice(in1, starts, limits, strides);
+    starts.back() = 1;
+    limits.back() = 2;
+    auto u2 = b->Slice(in0, starts, limits, strides);
+    auto v2 = b->Slice(in1, starts, limits, strides);
+    starts.back() = 2;
+    limits.back() = 3;
+    auto u3 = b->Slice(in0, starts, limits, strides);
+    auto v3 = b->Slice(in1, starts, limits, strides);
+
+    auto s1 = b->Sub(b->Mul(u2, v3), b->Mul(u3, v2));
+    auto s2 = b->Sub(b->Mul(u3, v1), b->Mul(u1, v3));
+    auto s3 = b->Sub(b->Mul(u1, v2), b->Mul(u2, v1));
+    auto output = b->ConcatInDim({s1, s2, s3}, in0_shape.dims() - 1);
+
+    ctx->SetOutput(0, output);
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(CrossOp);
+};
+
+REGISTER_XLA_OP(Name("Cross"), CrossOp);
+
+}  // namespace
+}  // namespace tensorflow