[TF:XLA] Handle bitcasts between different bitwidths

PiperOrigin-RevId: 251810931

tensorflow/compiler/tf2xla/kernels/cast_op.cc

@@ -12,16 +12,21 @@ 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/lib/broadcast.h"
 #include "tensorflow/compiler/tf2xla/lib/util.h"
-
+#include "tensorflow/compiler/tf2xla/shape_util.h"
 #include "tensorflow/compiler/tf2xla/type_util.h"
 #include "tensorflow/compiler/tf2xla/xla_helpers.h"
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/lib/arithmetic.h"
+#include "tensorflow/compiler/xla/client/lib/constants.h"
 #include "tensorflow/compiler/xla/client/xla_builder.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/lib/core/errors.h"
 
 namespace tensorflow {
 namespace {
@@ -112,23 +117,85 @@ class BitcastOp : public XlaOpKernel {
 
     if (src_dtype_ == dst_dtype_) {
       output = input;
-    } else {
+      ctx->SetOutput(0, output);
-      // Error out if the bitcast has a complex source or destination type and
+      return;
-      // the bitcast is not trivial.
+    }
-      OP_REQUIRES(ctx,
+    // Error out if the bitcast has a complex source or destination type and
-                  !xla::primitive_util::IsComplexType(src_type_) &&
+    // the bitcast is not trivial.
-                      !xla::primitive_util::IsComplexType(dst_type_),
+    OP_REQUIRES(ctx,
-                  errors::Unimplemented("Complex types not supported."));
+                !xla::primitive_util::IsComplexType(src_type_) &&
-      // XLA bitcast requires that the bit-width of the source and destination
+                    !xla::primitive_util::IsComplexType(dst_type_),
-      // matches, and currently only the simple lowering is performed.
+                errors::Unimplemented("Complex types not supported."));
-      OP_REQUIRES(ctx,
+    auto input_bit_width = xla::primitive_util::BitWidth(src_type_);
-                  xla::primitive_util::BitWidth(src_type_) ==
+    auto output_bit_width = xla::primitive_util::BitWidth(dst_type_);
-                      xla::primitive_util::BitWidth(dst_type_),
+
-                  errors::Unimplemented(
+    auto input_logical_type =
-                      "Only bitcasts between equally sized types supported."));
+        xla::primitive_util::UnsignedIntegralTypeForBitWidth(input_bit_width);
-      output = xla::BitcastConvertType(input, dst_type_);
+    auto output_logical_type =
+        xla::primitive_util::UnsignedIntegralTypeForBitWidth(output_bit_width);
+
+    OP_REQUIRES(ctx,
+
+                output_bit_width % input_bit_width == 0 ||
+                    input_bit_width % output_bit_width == 0,
+                errors::InvalidArgument(
+                    "Neither bit width is a multiple of the other."));
+
+    // Modify the input as needed so we only need to bitcast to create the
+    // output.
+    if (input_bit_width > output_bit_width) {
+      // Casting to a smaller bit width results in a new inner dimension.
+      auto broadcasted_input_shape = ctx->InputShape(0);
+      auto reshaped_input_shape = ctx->InputShape(0);
+      broadcasted_input_shape.AddDim(input_bit_width / output_bit_width);
+      reshaped_input_shape.AddDim(1);
+      auto output_bit_width_mask = (1 << output_bit_width) - 1;
+
+      auto status_or_input =
+          BroadcastTo(xla::Reshape(input, reshaped_input_shape.dim_sizes()),
+                      broadcasted_input_shape.dim_sizes());
+      OP_REQUIRES_OK(ctx, status_or_input.status());
+      input = xla::BitcastConvertType(status_or_input.ConsumeValueOrDie(),
+                                      input_logical_type);
+      auto xla_input_shape_status = ctx->builder()->GetShape(input);
+      OP_REQUIRES_OK(ctx, xla_input_shape_status.status());
+      auto xla_input_shape = xla_input_shape_status.ConsumeValueOrDie();
+
+      auto iota = xla::Iota(ctx->builder(), xla_input_shape,
+                            xla_input_shape.dimensions_size() - 1);
+      xla::XlaOp iota_m =
+          xla::Mul(xla::ScalarLike(input, output_bit_width), iota);
+      input = xla::And(xla::ShiftRightLogical(input, iota_m),
+                       xla::ScalarLike(input, output_bit_width_mask));
+      input = xla::ConvertElementType(input, output_logical_type);
+    } else if (input_bit_width < output_bit_width) {
+      // Casting to a larger bit width results in removing the innermost
+      // dimension.
+      auto input_shape = ctx->InputShape(0);
+      xla::Shape input_xla_shape =
+          TensorShapeToXLAShape(dst_type_, input_shape);
+      OP_REQUIRES(
+          ctx,
+          input_shape.dim_size(input_shape.dims() - 1) ==
+              output_bit_width / input_bit_width,
+          errors::InvalidArgument(
+              "Inner dimension of operand should be removed after cast."));
+
+      auto zero = XlaHelpers::Zero(ctx->builder(), dst_dtype_);
+      input = xla::ConvertElementType(input, dst_type_);
+
+      // Shift bits and OR them together to reduce the inner dimension.
+      xla::XlaOp iota_m =
+          xla::Mul(xla::ScalarLike(input, input_bit_width),
+                   xla::Iota(ctx->builder(), input_xla_shape,
+                             input_xla_shape.dimensions_size() - 1));
+      input = xla::ShiftLeft(input, iota_m);
+      input = xla::Reduce(input, zero,
+                          CreateScalarOrComputation(dst_type_, ctx->builder()),
+                          {input_xla_shape.dimensions_size() - 1});
     }
 
+    output = xla::BitcastConvertType(input, dst_type_);
     ctx->SetOutput(0, output);
   }
 

tensorflow/python/kernel_tests/bitcast_op_test.py

@@ -38,14 +38,12 @@ class BitcastTest(test.TestCase):
       self.assertEqual(tf_ans.get_shape(), shape)
       self.assertEqual(tf_ans.dtype, datatype)
 
-  @test_util.disable_xla("Different bitwidths not supported")
   def testSmaller(self):
     x = np.random.rand(3, 2)
     datatype = dtypes.int8
     shape = [3, 2, 8]
     self._testBitcast(x, datatype, shape)
 
-  @test_util.disable_xla("Different bitwidths not supported")
   def testLarger(self):
     x = np.arange(16, dtype=np.int8).reshape([4, 4])
     datatype = dtypes.int32
@@ -69,7 +67,6 @@ class BitcastTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, "Cannot bitcast due to shape"):
       array_ops.bitcast(x, datatype, None)
 
-  @test_util.disable_xla("Different bitwidths not supported")
   def testEmpty(self):
     x = np.ones([], np.int32)
     datatype = dtypes.int8