Modify the XLA Uniform sampler to use cast instead of bitcasts. We didn't strictly need a bitcast because we are ignoring the exponent bits anyway.

Before and after logic is equivalent. However, performance could have an impact.

PiperOrigin-RevId: 304057589
Change-Id: I2ad9e923b1c966f46eba91ae47e0e632b74cff72

tensorflow/compiler/tests/stateless_random_ops_test.py

@@ -26,6 +26,7 @@ from tensorflow.python.kernel_tests.random import util as \
 random_test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import stateless_random_ops as stateless
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 
@@ -132,5 +133,38 @@ class StatelessRandomOpsTest(xla_test.XLATestCase):
             variance_rtol=6e-3 if dtype == dtypes.bfloat16 else 1e-3)
 
 
+class StatelessRandomOpsBenchmark(test.Benchmark):
+  """Microbenchmarks for the stateless random ops."""
+
+  def _benchmarkUniform(self, name, dtype, use_xla_jit):
+
+    def BuilderFn():
+      shape = (10, 1000, 1000)
+      seed_var = variables.Variable((312, 456),
+                                    dtype=dtypes.int32,
+                                    name='input')
+      random_t = stateless.stateless_random_uniform(
+          shape, seed=seed_var, dtype=dtype)
+      return '%s.shape%s' % (name, shape), [random_t]
+
+    xla_test.Benchmark(self, BuilderFn, use_xla_jit=use_xla_jit, device='cpu')
+
+  def benchmarkUniformF32(self):
+    self._benchmarkUniform(
+        'uniform_f32', dtype=dtypes.float32, use_xla_jit=False)
+
+  def benchmarkUniformF64(self):
+    self._benchmarkUniform(
+        'uniform_f64', dtype=dtypes.float64, use_xla_jit=False)
+
+  def benchmarkUniformF32XLA(self):
+    self._benchmarkUniform(
+        'uniform_f32', dtype=dtypes.float32, use_xla_jit=True)
+
+  def benchmarkUniformF64XLA(self):
+    self._benchmarkUniform(
+        'uniform_f64', dtype=dtypes.float64, use_xla_jit=True)
+
+
 if __name__ == '__main__':
   test.main()

tensorflow/compiler/xla/client/lib/prng.cc

@@ -434,17 +434,21 @@ XlaOp ConvertRandomBitsToUniformFloatingPoint(XlaOp bits, XlaOp minval,
         (value_type == F64 && bit_type == U64));
 
   // Form random mantissa bits for float/double, with a leading 1 bit.
-  int float_bits = primitive_util::BitWidth(value_type);
+  int num_float_bits = primitive_util::BitWidth(value_type);
   // Subtract one as SignificandWidth includes the leading 1 bit.
-  int mantissa_bits = primitive_util::SignificandWidth(value_type) - 1;
+  int num_mantissa_bits = primitive_util::SignificandWidth(value_type) - 1;
 
-  bits = ShiftRightLogical(bits, ScalarLike(bits, float_bits - mantissa_bits)) |
-         BitcastConvertType(ScalarLike(minval, 1.0), bit_type);
-  XlaOp values = BitcastConvertType(bits, value_type);
+  // Ignore the exponent bits and convert the mantissa bits to the floating
+  // point type.
+  bits = ShiftRightLogical(
+      bits, ScalarLike(bits, num_float_bits - num_mantissa_bits));
 
-  // We have a floating point number in the range [1.0, 2.0).
-  // Subtract 1.0f to shift to the range [0.0, 1.0)
-  values = values - ScalarLike(values, 1.0);
+  // We have an integer-valued floating point number in the range
+  // [0, 2**{num_mantissa_bits}).
+  XlaOp values = ConvertElementType(bits, value_type);
+
+  // Divide by 2**{-num_mantissa_bits} to get a number in the range [0.0, 1.0).
+  values = values * ScalarLike(values, std::ldexp(1., -num_mantissa_bits));
 
   // Multiply and add to shift to the range [minval, maxval).
   return values * (maxval - minval) + minval;