Add support for the hermitian option on jnp.linalg.pinv.

Improve the pinv implementation to avoid computing an unnecessary reduction: svd sorts its singular values so we don't need to use amax() to find the largest one.
Avoid explicitly forming the identity matrix in the pinv JVP.

CHANGELOG.md

@@ -7,6 +7,8 @@ Remember to align the itemized text with the first line of an item within a list
 -->
 
 ## jax 0.3.25
+* Changes
+  * {func}`jax.numpy.linalg.pinv` now supports the `hermitian` option.
 
 ## jaxlib 0.3.25
 

jax/_src/numpy/linalg.py

@@ -41,6 +41,9 @@ def _H(x: ArrayLike) -> Array:
   return jnp.conjugate(jnp.swapaxes(x, -1, -2))
 
 
+def _symmetrize(x: Array) -> Array: return (x + _H(x)) / 2
+
+
 @_wraps(np.linalg.cholesky)
 @jit
 def cholesky(a: ArrayLike) -> Array:
@@ -406,27 +409,32 @@ def eigvalsh(a: ArrayLike, UPLO: Optional[str] = 'L') -> Array:
   return w
 
 
-@partial(custom_jvp, nondiff_argnums=(1,))
+@partial(custom_jvp, nondiff_argnums=(1, 2))
 @_wraps(np.linalg.pinv, lax_description=textwrap.dedent("""\
     It differs only in default value of `rcond`. In `numpy.linalg.pinv`, the
     default `rcond` is `1e-15`. Here the default is
     `10. * max(num_rows, num_cols) * jnp.finfo(dtype).eps`.
     """))
-@jit
+@partial(jit, static_argnames=('hermitian',))
-def pinv(a: ArrayLike, rcond: Optional[ArrayLike] = None) -> Array:
+def pinv(a: ArrayLike, rcond: Optional[ArrayLike] = None,
+         hermitian: bool = False) -> Array:
   # Uses same algorithm as
   # https://github.com/numpy/numpy/blob/v1.17.0/numpy/linalg/linalg.py#L1890-L1979
   _check_arraylike("jnp.linalg.pinv", a)
-  arr = jnp.conj(a)
+  arr = jnp.asarray(a)
+  m, n = arr.shape[-2:]
+  if m == 0 or n == 0:
+    return jnp.empty(arr.shape[:-2] + (n, m), arr.dtype)
+  arr = jnp.conj(arr)
   if rcond is None:
     max_rows_cols = max(arr.shape[-2:])
     rcond = 10. * max_rows_cols * jnp.array(jnp.finfo(arr.dtype).eps)
   rcond = jnp.asarray(rcond)
-  u, s, vh = svd(arr, full_matrices=False)
+  u, s, vh = svd(arr, full_matrices=False, hermitian=hermitian)
   # Singular values less than or equal to ``rcond * largest_singular_value``
   # are set to zero.
   rcond = lax.expand_dims(rcond[..., jnp.newaxis], range(s.ndim - rcond.ndim - 1))
-  cutoff = rcond * jnp.amax(s, axis=-1, keepdims=True, initial=-jnp.inf)
+  cutoff = rcond * s[..., 0:1]
   s = jnp.where(s > cutoff, s, jnp.inf).astype(u.dtype)
   res = jnp.matmul(_T(vh), jnp.divide(_T(u), s[..., jnp.newaxis]),
                    precision=lax.Precision.HIGHEST)
@@ -435,22 +443,24 @@ def pinv(a: ArrayLike, rcond: Optional[ArrayLike] = None) -> Array:
 
 @pinv.defjvp
 @jax.default_matmul_precision("float32")
-def _pinv_jvp(rcond, primals, tangents):
+def _pinv_jvp(rcond, hermitian, primals, tangents):
   # The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems
   # Whose Variables Separate. Author(s): G. H. Golub and V. Pereyra. SIAM
   # Journal on Numerical Analysis, Vol. 10, No. 2 (Apr., 1973), pp. 413-432.
   # (via https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Derivative)
   a, = primals
   a_dot, = tangents
-  p = pinv(a, rcond=rcond)
+  p = pinv(a, rcond=rcond, hermitian=hermitian)
-  m, n = a.shape[-2:]
+  if hermitian:
-  # TODO(phawkins): on TPU, we would need to opt into high precision here.
+    # svd(..., hermitian=True) symmetrizes its input, and the JVP must match.
-  # TODO(phawkins): consider if this can be simplified in the Hermitian case.
+    a = _symmetrize(a)
-  p_dot = -p @ a_dot @ p
+    a_dot = _symmetrize(a_dot)
-  I_n = lax.expand_dims(jnp.eye(m, dtype=a.dtype), range(a.ndim - 2))
+
-  p_dot = p_dot + p @ _H(p) @ _H(a_dot) @ (I_n - a @ p)
+  # TODO(phawkins): this could be simplified in the Hermitian case if we
-  I_m = lax.expand_dims(jnp.eye(n, dtype=a.dtype), range(a.ndim - 2))
+  # supported triangular matrix multiplication.
-  p_dot = p_dot + (I_m - p @ a) @ _H(a_dot) @ _H(p) @ p
+  s = p @ _H(p) @ _H(a_dot)
+  t = _H(a_dot) @ _H(p) @ p
+  p_dot = -p @ a_dot @ p + s - s @ a @ p + t - p @ a @ t
   return p, p_dot
 
 

tests/linalg_test.py

@@ -798,21 +798,28 @@ class NumpyLinalgTest(jtu.JaxTestCase):
     self._CompileAndCheck(jnp.linalg.inv, args_maker)
 
   @jtu.sample_product(
-    shape=[(1, 1), (4, 4), (2, 70, 7), (2000, 7), (7, 1000), (70, 7, 2),
+    [dict(shape=shape, hermitian=hermitian)
-           (2, 0, 0), (3, 0, 2), (1, 0)],
+     for shape in [(1, 1), (4, 4), (3, 10, 10), (2, 70, 7), (2000, 7),
+                   (7, 1000), (70, 7, 2), (2, 0, 0), (3, 0, 2), (1, 0)]
+     for hermitian in ([False, True] if shape[-1] == shape[-2] else [False])],
     dtype=float_types + complex_types,
   )
   @jtu.skip_on_devices("rocm")  # will be fixed in ROCm-5.1
-  def testPinv(self, shape, dtype):
+  def testPinv(self, shape, hermitian, dtype):
     rng = jtu.rand_default(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
 
-    self._CheckAgainstNumpy(np.linalg.pinv, jnp.linalg.pinv, args_maker,
+    jnp_fn = partial(jnp.linalg.pinv, hermitian=hermitian)
-                            tol=1e-2)
+    def np_fn(a):
-    self._CompileAndCheck(jnp.linalg.pinv, args_maker)
+      # Symmetrize the input matrix to match the jnp behavior.
+      if hermitian:
+        a = (a + T(a.conj())) / 2
+      return np.linalg.pinv(a, hermitian=hermitian)
+    self._CheckAgainstNumpy(np_fn, jnp_fn, args_maker, tol=1e-4)
+    self._CompileAndCheck(jnp_fn, args_maker)
 
     # TODO(phawkins): 1e-1 seems like a very loose tolerance.
-    jtu.check_grads(jnp.linalg.pinv, args_maker(), 2, rtol=1e-1, atol=2e-1)
+    jtu.check_grads(jnp_fn, args_maker(), 1, rtol=3e-2, atol=1e-3)
 
   def testPinvGradIssue2792(self):
     def f(p):