Upgraded eigen to the latest version

Change: 129887348

tensorflow/core/kernels/cwise_ops.h

@@ -386,61 +386,20 @@ struct logical_not : base<bool, Eigen::internal::scalar_boolean_not_op<bool> > {
 // NOTE: std::isinf, std::isnan, std::isfinite are plain function.
 // Therefore we need to wrap them in functors to be used with Eigen's
 // type system.
+template <typename T>
+struct isinf : base<T, Eigen::internal::scalar_isinf_op<T>, bool> {};
 
 template <typename T>
-struct isinf_func {
+struct isnan : base<T, Eigen::internal::scalar_isnan_op<T>, bool> {};
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(T x) const {
-    return Eigen::numext::isinf(x);
-  }
-};
 
 template <typename T>
-struct isinf : base<T, isinf_func<T>, bool> {};
+struct isfinite : base<T, Eigen::internal::scalar_isfinite_op<T>, bool> {};
 
 template <typename T>
-struct isnan_func {
+struct floor : base<T, Eigen::internal::scalar_floor_op<T>> {};
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(T x) const {
-    return Eigen::numext::isnan(x);
-  }
-};
 
 template <typename T>
-struct isnan : base<T, isnan_func<T>, bool> {};
+struct ceil : base<T, Eigen::internal::scalar_ceil_op<T>> {};
-
-template <typename T>
-struct isfinite_func {
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(T x) const {
-    return Eigen::numext::isfinite(x);
-  }
-};
-
-template <typename T>
-struct isfinite : base<T, isfinite_func<T>, bool> {};
-
-template <typename T>
-struct floor_func {
-  typedef T result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(T x) const {
-    return Eigen::numext::floor(x);
-  }
-};
-
-template <typename T>
-struct floor : base<T, floor_func<T> > {};
-
-template <typename T>
-struct ceil_func {
-  typedef T result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(T x) const {
-    return Eigen::numext::ceil(x);
-  }
-};
-
-template <typename T>
-struct ceil : base<T, ceil_func<T> > {};
 
 ////////////////////////////////////////////////////////////////////////////////
 // Binary functors

tensorflow/core/util/tensor_slice_util.h

@@ -110,7 +110,7 @@ struct CopyThatWorksWithStringPointer<Eigen::half> {
                     d(d_start[0] + i0, d_start[1] + i1, d_start[2] + i2,
                       d_start[3] + i3, d_start[4] + i4, d_start[5] + i5,
                       d_start[6] + i6, d_start[7] + i7) =
-                        Eigen::internal::raw_uint16_to_half(
+                        Eigen::half_impl::raw_uint16_to_half(
                             s(s_start[0] + i0, s_start[1] + i1, s_start[2] + i2,
                               s_start[3] + i3, s_start[4] + i4, s_start[5] + i5,
                               s_start[6] + i6, s_start[7] + i7));

tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py

@@ -55,13 +55,13 @@ def SortEigenDecomposition(e, v):
 
 def _GetSelfAdjointEigTest(dtype_, shape_):
 
-  def CompareEigenVectors(self, x, y, atol):
+  def CompareEigenVectors(self, x, y, tol):
     # Eigenvectors are only unique up to sign so we normalize the signs first.
     signs = np.sign(np.sum(np.divide(x, y), -2, keepdims=True))
     x *= signs
-    self.assertAllClose(x, y, atol)
+    self.assertAllClose(x, y, atol=tol, rtol=tol)
 
-  def CompareEigenDecompositions(self, x_e, x_v, y_e, y_v, atol):
+  def CompareEigenDecompositions(self, x_e, x_v, y_e, y_v, tol):
     num_batches = int(np.prod(x_e.shape[:-1]))
     n = x_e.shape[-1]
     x_e = np.reshape(x_e, [num_batches] + [n])
@@ -71,8 +71,8 @@ def _GetSelfAdjointEigTest(dtype_, shape_):
     for i in range(num_batches):
       x_ei, x_vi = SortEigenDecomposition(x_e[i, :], x_v[i, :, :])
       y_ei, y_vi = SortEigenDecomposition(y_e[i, :], y_v[i, :, :])
-      self.assertAllClose(x_ei, y_ei, atol=atol)
+      self.assertAllClose(x_ei, y_ei, atol=tol, rtol=tol)
-      CompareEigenVectors(self, x_vi, y_vi, atol)
+      CompareEigenVectors(self, x_vi, y_vi, tol)
 
   def Test(self):
     np.random.seed(1)
@@ -85,7 +85,7 @@ def _GetSelfAdjointEigTest(dtype_, shape_):
     if dtype_ == np.float32:
       atol = 1e-4
     else:
-      atol = 1e-14
+      atol = 1e-12
     for compute_v in False, True:
       np_e, np_v = np.linalg.eig(a)
       with self.test_session():

tensorflow/workspace.bzl

@@ -7,8 +7,8 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
 
   # These lines need to be changed when updating Eigen. They are parsed from
   # this file by the cmake and make builds to determine the eigen version and hash.
-  eigen_version = "b4fa9622b809"
+  eigen_version = "6f952374ef2b"
-  eigen_sha256 = "2862840c2de9c0473a4ef20f8678949ae89ab25965352ee53329e63ba46cec62"
+  eigen_sha256 = "56d658324b09de3f418ae42ca0646dd1e6e0b897dd58b164ec0d21315764afd9"
 
   native.new_http_archive(
     name = "eigen_archive",