Extend cat op for complex dtype (pytorch#12894)

Summary:

Need complex cat op for live translation

Differential Revision: D78934592

Author: pssrawat

kernels/portable/cpu/op_cat.cpp

@@ -56,27 +56,57 @@ Tensor& cat_out(
   const size_t ninputs = tensors.size();
 
   const auto out_type = out.scalar_type();
-  ET_SWITCH_REALHBBF16_TYPES(out_type, ctx, "cat.out", CTYPE_OUT, [&] {
-    CTYPE_OUT* out_ptr = out.mutable_data_ptr<CTYPE_OUT>();
-    for (size_t i = 0; i < outer; ++i) {
-      for (size_t j = 0; j < ninputs; ++j) {
-        const auto in_type = tensors[j].scalar_type();
-        ET_SWITCH_REALHBBF16_TYPES(in_type, ctx, "cat.out", CTYPE_IN, [&] {
+  const bool out_is_complex =
+      executorch::runtime::isComplexType(out.scalar_type());
+
+  if (out_is_complex) {
+    // All the input tensors and output must have same dtype
+    for (size_t i = 0; i < ninputs; ++i) {
+      const auto in_type = tensors[i].scalar_type();
+      ET_KERNEL_CHECK(ctx, out_type == in_type, InvalidArgument, out);
+    }
+    ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, "cat.out", CTYPE, [&] {
+      CTYPE* out_ptr = out.mutable_data_ptr<CTYPE>();
+      for (size_t i = 0; i < outer; ++i) {
+        for (size_t j = 0; j < ninputs; ++j) {
           if (tensors[j].numel() == 0) {
             return;
           }
           size_t inner = tensors[j].size(dim) * dim_stride;
-          const CTYPE_IN* const in_ptr =
-              tensors[j].const_data_ptr<CTYPE_IN>() + i * inner;
-
-          for (size_t k = 0; k < inner; ++k) {
-            out_ptr[k] = static_cast<CTYPE_OUT>(in_ptr[k]);
-          }
+          const CTYPE* const in_ptr =
+              tensors[j].const_data_ptr<CTYPE>() + i * inner;
+          memcpy(out_ptr, in_ptr, inner * sizeof(CTYPE));
           out_ptr += inner;
-        });
+        }
       }
-    }
-  });
+    });
+  } else {
+    ET_SWITCH_REALHBBF16_TYPES(out_type, ctx, "cat.out", CTYPE_OUT, [&] {
+      CTYPE_OUT* out_ptr = out.mutable_data_ptr<CTYPE_OUT>();
+      for (size_t i = 0; i < outer; ++i) {
+        for (size_t j = 0; j < ninputs; ++j) {
+          const auto in_type = tensors[j].scalar_type();
+          ET_SWITCH_REALHBBF16_TYPES(in_type, ctx, "cat.out", CTYPE_IN, [&] {
+            if (tensors[j].numel() == 0) {
+              return;
+            }
+            size_t inner = tensors[j].size(dim) * dim_stride;
+            const CTYPE_IN* const in_ptr =
+                tensors[j].const_data_ptr<CTYPE_IN>() + i * inner;
+
+            if (sizeof(CTYPE_IN) == sizeof(CTYPE_OUT)) {
+              memcpy(out_ptr, in_ptr, inner * sizeof(CTYPE_IN));
+            } else {
+              for (size_t k = 0; k < inner; ++k) {
+                out_ptr[k] = static_cast<CTYPE_OUT>(in_ptr[k]);
+              }
+            }
+            out_ptr += inner;
+          });
+        }
+      }
+    });
+  }
 
   return out;
 }

kernels/test/op_cat_test.cpp

@@ -73,6 +73,58 @@ class OpCatOutTest : public OperatorTest {
         tf.make({2, 4}, {1.5, -2.0, 3.25, 10.0, 4.0, -5.5, 6.5, 20.0});
     EXPECT_TENSOR_EQ(out, expected);
   }
+
+  template <typename CTYPE, ScalarType DTYPE>
+  void test_complex_dtype() {
+    TensorFactory<DTYPE> tf;
+    Tensor x = tf.make(
+        {2, 2},
+        {CTYPE(0.01, 2.03),
+         CTYPE(4.05, 6.07),
+         CTYPE(0.11, 2.13),
+         CTYPE(4.15, 6.17)});
+    Tensor y = tf.make(
+        {2, 2},
+        {CTYPE(0.21, 2.23),
+         CTYPE(4.25, 6.27),
+         CTYPE(0.31, 2.33),
+         CTYPE(4.35, 6.37)});
+
+    std::vector<Tensor> inputs = {x, y};
+
+    // Concatenate along dim[0].
+    Tensor out_0 = tf.full({4, 2}, CTYPE{0, 0});
+    Tensor ret_0 = op_cat_out(
+        ArrayRef<Tensor>(inputs.data(), inputs.size()), /*dim=*/0, out_0);
+    Tensor expected_0 = tf.make(
+        {4, 2},
+        {CTYPE(0.01, 2.03),
+         CTYPE(4.05, 6.07),
+         CTYPE(0.11, 2.13),
+         CTYPE(4.15, 6.17),
+         CTYPE(0.21, 2.23),
+         CTYPE(4.25, 6.27),
+         CTYPE(0.31, 2.33),
+         CTYPE(4.35, 6.37)});
+
+    EXPECT_TENSOR_EQ(out_0, expected_0);
+
+    // Concatenate along dim[1].
+    Tensor out_1 = tf.full({2, 4}, CTYPE{0, 0});
+    Tensor ret_1 = op_cat_out(
+        ArrayRef<Tensor>(inputs.data(), inputs.size()), /*dim=*/1, out_1);
+    Tensor expected_1 = tf.make(
+        {2, 4},
+        {CTYPE(0.01, 2.03),
+         CTYPE(4.05, 6.07),
+         CTYPE(0.21, 2.23),
+         CTYPE(4.25, 6.27),
+         CTYPE(0.11, 2.13),
+         CTYPE(4.15, 6.17),
+         CTYPE(0.31, 2.33),
+         CTYPE(4.35, 6.37)});
+    EXPECT_TENSOR_EQ(out_1, expected_1);
+  }
 };
 
 TEST_F(OpCatOutTest, SmokeDim1) {
@@ -133,6 +185,13 @@ TEST_F(OpCatOutTest, SixteenBitFloatSupport) {
   test_16bit_dtype<ScalarType::BFloat16>();
 }
 
+TEST_F(OpCatOutTest, ComplexSupport) {
+#define RUN_COMPLEX_TEST(ctype, dtype) \
+  test_complex_dtype<ctype, ScalarType::dtype>();
+  ET_FORALL_COMPLEXH_TYPES(RUN_COMPLEX_TEST);
+#undef RUN_COMPLEX_TEST
+}
+
 TEST_F(OpCatOutTest, NegativeDims) {
   TensorFactory<ScalarType::Int> tf;