[XLA] Improve numerical stability of Logistic.
PiperOrigin-RevId: 315320526 Change-Id: Iedfd22d0fb657cb31dda537786ce001f1dab168b
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@ -510,6 +510,16 @@ class UnaryOpsTest(xla_test.XLATestCase):
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],
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dtype=dtype))
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@test_util.disable_mlir_bridge(
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"TODO(b/155501444): Handle _UnaryOpsComposition ops from Grappler")
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def testFloatOpsDisabledOnMlirBridge(self):
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for dtype in self.float_types:
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if dtype != np.float16:
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self._assertOpOutputMatchesExpected(
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lambda x: math_ops.sigmoid(x) / math_ops.log1p(math_ops.exp(x)),
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np.array([-40, 40], dtype=dtype),
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expected=np.array([1.0, 0.025], dtype=dtype))
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@test_util.disable_mlir_bridge(
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"TODO(b/153812660): Handle tf.QuantizeAndDequantize compilation")
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def testQuantizeAndDequantize(self):
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@ -1394,8 +1394,8 @@ XlaOp NextAfter(XlaOp from, XlaOp to) {
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}
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XlaOp Logistic(XlaOp x) {
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auto half = xla::ScalarLike(x, 0.5);
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return half + half * xla::Tanh(half * x);
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auto one = xla::ScalarLike(x, 1);
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return xla::Div(one, (one + xla::Exp(xla::Neg(x))));
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}
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// Computes an approximation to the modified Bessel function of the first kind,
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@ -35,7 +35,6 @@ from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import control_flow_v2_toggles
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import math_ops
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@ -1016,8 +1015,7 @@ class LSTMTest(test.TestCase):
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})
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comparison_fn = self.assertAllEqual
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if (test_util.is_xla_enabled() and
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control_flow_v2_toggles.control_flow_v2_enabled()):
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if test_util.is_xla_enabled():
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comparison_fn = self.assertAllClose
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if in_graph_mode:
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comparison_fn(outputs_static, outputs_dynamic)
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@ -1107,8 +1105,7 @@ class LSTMTest(test.TestCase):
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})
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comparison_fn = self.assertAllEqual
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if (test_util.is_xla_enabled() and
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control_flow_v2_toggles.control_flow_v2_enabled()):
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if test_util.is_xla_enabled():
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comparison_fn = self.assertAllClose
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if in_graph_mode:
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comparison_fn(outputs_static, outputs_dynamic)
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