Jaxlinop merge (#196)

Merge jaxlinop into main

gpjax/gaussian_distribution.py

@@ -14,7 +14,7 @@
 # ==============================================================================
 
 import jax.numpy as jnp
-from jaxlinop import LinearOperator, IdentityLinearOperator
+from .linops import LinearOperator, IdentityLinearOperator
 
 from jaxtyping import Array, Float
 from jax import vmap

gpjax/gps.py

@@ -21,8 +21,8 @@
 from jaxtyping import Array, Float
 from jax.random import KeyArray
 
-from jaxlinop import identity
-from jaxkern.base import AbstractKernel
+from .linops import identity
+from .kernels.base import AbstractKernel
 from jaxutils import PyTree
 
 from .config import get_global_config

gpjax/kernels/computations/base.py

@@ -17,13 +17,14 @@
 from typing import Callable, Dict
 
 from jax import vmap
-from jaxlinop import (
+from jaxtyping import Array, Float
+from jaxutils import PyTree
+
+from ...linops import (
     DenseLinearOperator,
     DiagonalLinearOperator,
     LinearOperator,
 )
-from jaxtyping import Array, Float
-from jaxutils import PyTree
 
 
 class AbstractKernelComputation(PyTree):

gpjax/kernels/computations/basis_functions.py

@@ -3,7 +3,7 @@
 import jax.numpy as jnp
 from jaxtyping import Array, Float
 from .base import AbstractKernelComputation
-from jaxlinop import DenseLinearOperator
+from ...linops import DenseLinearOperator
 
 
 class BasisFunctionComputation(AbstractKernelComputation):

gpjax/kernels/computations/constant_diagonal.py

@@ -17,12 +17,13 @@
 import jax.numpy as jnp
 
 from jax import vmap
-from jaxlinop import (
+from jaxtyping import Array, Float
+from .base import AbstractKernelComputation
+
+from ...linops import (
     ConstantDiagonalLinearOperator,
     DiagonalLinearOperator,
 )
-from jaxtyping import Array, Float
-from .base import AbstractKernelComputation
 
 
 class ConstantDiagonalKernelComputation(AbstractKernelComputation):

gpjax/kernels/computations/diagonal.py

@@ -13,15 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-from typing import Callable, Dict
-
 from jax import vmap
-from jaxlinop import (
-    DiagonalLinearOperator,
-)
+from typing import Callable, Dict
 from jaxtyping import Array, Float
-from .base import AbstractKernelComputation
 
+from .base import AbstractKernelComputation
+from ...linops import DiagonalLinearOperator
 
 class DiagonalKernelComputation(AbstractKernelComputation):
     def __init__(

gpjax/likelihoods.py

@@ -15,7 +15,7 @@
 
 import abc
 from typing import Any, Callable, Dict, Optional
-from jaxlinop.utils import to_dense
+from .linops.utils import to_dense
 from jaxutils import PyTree
 
 import distrax as dx

gpjax/linops/README.md

@@ -0,0 +1,53 @@
+# LinOps
+
+The `linops` submodule is a lightweight linear operator library written in [`jax`](https://github.com/google/jax).
+
+# Overview
+Consider solving a diagonal matrix $A$ against a vector $b$.
+
+```python
+import jax.numpy as jnp
+
+n = 1000
+diag = jnp.linspace(1.0, 2.0, n)
+
+A = jnp.diag(diag)
+b = jnp.linspace(3.0, 4.0, n)
+
+# A⁻¹ b
+jnp.solve(A, b)
+```
+Doing so is costly in large problems. Storing the matrix gives rise to memory costs of $O(n^2)$, and inverting the matrix costs $O(n^3)$ in the number of data points $n$.
+
+But hold on a second. Notice:
+
+- We only have to store the diagonal entries to determine the matrix $A$. Doing so, would reduce memory costs from $O(n^2)$ to $O(n)$. 
+- To invert $A$, we only need to take the reciprocal of the diagonal, reducing inversion costs from $O(n^3)$, to $O(n)$. 
+
+`JaxLinOp` is designed to exploit stucture of this kind. 
+```python
+from gpjax import linops
+
+A = linops.DiagonalLinearOperator(diag = diag)
+
+# A⁻¹ b
+A.solve(b)
+```
+`linops` is designed to automatically reduce cost savings in matrix addition, multiplication, computing log-determinants and more, for other matrix stuctures too!
+
+# Custom Linear Operator (details to come soon)
+
+The flexible design of `linops` will allow users to impliment their own custom linear operators.
+
+```python
+from gpjax.linops import LinearOperator
+
+class MyLinearOperator(LinearOperator):
+
+  def __init__(self, ...)
+    ...
+
+# There will be a minimal number methods that users need to impliment for their custom operator. 
+# For optimal efficiency, we'll make it easy for the user to add optional methods to their operator, 
+# if they give better performance than the defaults.
+```

gpjax/linops/__init__.py

@@ -0,0 +1,42 @@
+# Copyright 2022 The JaxLinOp Contributors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+# ==============================================================================
+
+from .linear_operator import LinearOperator
+from .dense_linear_operator import DenseLinearOperator
+from .diagonal_linear_operator import DiagonalLinearOperator
+from .constant_diagonal_linear_operator import ConstantDiagonalLinearOperator
+from .identity_linear_operator import IdentityLinearOperator
+from .zero_linear_operator import ZeroLinearOperator
+from .triangular_linear_operator import (
+    LowerTriangularLinearOperator,
+    UpperTriangularLinearOperator,
+)
+from .utils import (
+    identity,
+    to_dense,
+)
+
+__all__ = [
+    "LinearOperator",
+    "DenseLinearOperator",
+    "DiagonalLinearOperator",
+    "ConstantDiagonalLinearOperator",
+    "IdentityLinearOperator",
+    "ZeroLinearOperator",
+    "LowerTriangularLinearOperator",
+    "UpperTriangularLinearOperator",
+    "identity",
+    "to_dense",
+]

gpjax/linops/constant_diagonal_linear_operator.py

@@ -0,0 +1,190 @@
+# Copyright 2022 The JaxLinOp Contributors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+# ==============================================================================
+
+from __future__ import annotations
+
+from typing import Any, Union
+
+import jax.numpy as jnp
+from jaxtyping import Array, Float
+from simple_pytree import static_field
+from dataclasses import dataclass
+
+from .linear_operator import LinearOperator
+from .diagonal_linear_operator import DiagonalLinearOperator
+
+
+def _check_args(value: Any, size: Any) -> None:
+
+    if not isinstance(size, int):
+        raise ValueError(f"`length` must be an integer, but `length = {size}`.")
+
+    if value.ndim != 1:
+        raise ValueError(
+            f"`value` must be one dimensional scalar, but `value.shape = {value.shape}`."
+        )
+
+
+@dataclass
+class ConstantDiagonalLinearOperator(DiagonalLinearOperator):
+    value: Float[Array, "1"]
+    size: int = static_field()
+
+    def __init__(
+        self, value: Float[Array, "1"], size: int, dtype: jnp.dtype = None
+    ) -> None:
+        """Initialize the constant diagonal linear operator.
+
+        Args:
+            value (Float[Array, "1"]): Constant value of the diagonal.
+            size (int): Size of the diagonal.
+        """
+
+        _check_args(value, size)
+
+        if dtype is not None:
+            value = value.astype(dtype)
+
+        self.value = value
+        self.size = size
+        self.shape = (size, size)
+        self.dtype = value.dtype
+
+    def __add__(
+        self, other: Union[Float[Array, "N N"], LinearOperator]
+    ) -> DiagonalLinearOperator:
+        if isinstance(other, ConstantDiagonalLinearOperator):
+            if other.size == self.size:
+                return ConstantDiagonalLinearOperator(
+                    value=self.value + other.value, size=self.size
+                )
+
+            raise ValueError(
+                f"`length` must be the same, but `length = {self.size}` and `length = {other.size}`."
+            )
+
+        else:
+            return super().__add__(other)
+
+    def __mul__(self, other: float) -> LinearOperator:
+        """Multiply covariance operator by scalar.
+
+        Args:
+            other (LinearOperator): Scalar.
+
+        Returns:
+            LinearOperator: Covariance operator multiplied by a scalar.
+        """
+
+        return ConstantDiagonalLinearOperator(value=self.value * other, size=self.size)
+
+    def _add_diagonal(self, other: DiagonalLinearOperator) -> LinearOperator:
+        """Add diagonal to the covariance operator,  useful for computing, Kxx + Iσ².
+
+        Args:
+            other (DiagonalLinearOperator): Diagonal covariance operator to add to the covariance operator.
+
+        Returns:
+            LinearOperator: Covariance operator with the diagonal added.
+        """
+
+        if isinstance(other, ConstantDiagonalLinearOperator):
+            if other.size == self.size:
+                return ConstantDiagonalLinearOperator(
+                    value=self.value + other.value, size=self.size
+                )
+
+            raise ValueError(
+                f"`length` must be the same, but `length = {self.size}` and `length = {other.size}`."
+            )
+
+        else:
+            return super()._add_diagonal(other)
+
+    def diagonal(self) -> Float[Array, "N"]:
+        """Diagonal of the covariance operator."""
+        return self.value * jnp.ones(self.size)
+
+    def to_root(self) -> ConstantDiagonalLinearOperator:
+        """
+        Lower triangular.
+
+        Returns:
+            Float[Array, "N N"]: Lower triangular matrix.
+        """
+        return ConstantDiagonalLinearOperator(
+            value=jnp.sqrt(self.value), size=self.size
+        )
+
+    def log_det(self) -> Float[Array, "1"]:
+        """Log determinant.
+
+        Returns:
+            Float[Array, "1"]: Log determinant of the covariance matrix.
+        """
+        return 2.0 * self.size * jnp.log(self.value)
+
+    def inverse(self) -> ConstantDiagonalLinearOperator:
+        """Inverse of the covariance operator.
+
+        Returns:
+            DiagonalLinearOperator: Inverse of the covariance operator.
+        """
+        return ConstantDiagonalLinearOperator(value=1.0 / self.value, size=self.size)
+
+    def solve(self, rhs: Float[Array, "N M"]) -> Float[Array, "N M"]:
+        """Solve linear system.
+
+        Args:
+            rhs (Float[Array, "N M"]): Right hand side of the linear system.
+
+        Returns:
+            Float[Array, "N M"]: Solution of the linear system.
+        """
+
+        return rhs / self.value
+
+    @classmethod
+    def from_dense(cls, dense: Float[Array, "N N"]) -> ConstantDiagonalLinearOperator:
+        """Construct covariance operator from dense matrix.
+
+        Args:
+            dense (Float[Array, "N N"]): Dense matrix.
+
+        Returns:
+            DiagonalLinearOperator: Covariance operator.
+        """
+        return ConstantDiagonalLinearOperator(
+            value=jnp.atleast_1d(dense[0, 0]), size=dense.shape[0]
+        )
+
+    @classmethod
+    def from_root(
+        cls, root: ConstantDiagonalLinearOperator
+    ) -> ConstantDiagonalLinearOperator:
+        """Construct covariance operator from root.
+
+        Args:
+            root (ConstantDiagonalLinearOperator): Root of the covariance operator.
+
+        Returns:
+            ConstantDiagonalLinearOperator: Covariance operator.
+        """
+        return ConstantDiagonalLinearOperator(value=root.value**2, size=root.size)
+
+
+__all__ = [
+    "ConstantDiagonalLinearOperator",
+]