117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
|
# Copyright 2021 The JAX Authors.
|
||
|
|
#
|
||
|
|
# 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
|
||
|
|
#
|
||
|
|
# https://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.
|
||
|
|
|
||
|
|
"""Sparse utilities."""
|
||
|
|
|
||
|
|
import functools
|
||
|
|
from typing import Any, NamedTuple, Tuple, Union
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import jax
|
||
|
|
from jax import lax
|
||
|
|
from jax import tree_util
|
||
|
|
from jax import vmap
|
||
|
|
from jax._src import core
|
||
|
|
from jax._src import dtypes
|
||
|
|
from jax._src import stages
|
||
|
|
from jax._src.api_util import flatten_axes
|
||
|
|
import jax.numpy as jnp
|
||
|
|
from jax.util import safe_zip
|
||
|
|
from jax._src.lax.lax import _dot_general_shape_rule, DotDimensionNumbers
|
||
|
|
from jax._src.typing import Array
|
||
|
|
|
||
|
|
class SparseEfficiencyError(ValueError):
|
||
|
|
pass
|
||
|
|
|
||
|
|
class SparseEfficiencyWarning(UserWarning):
|
||
|
|
pass
|
||
|
|
|
||
|
|
class CuSparseEfficiencyWarning(SparseEfficiencyWarning):
|
||
|
|
pass
|
||
|
|
|
||
|
|
Shape = Tuple[int, ...]
|
||
|
|
|
||
|
|
class SparseInfo(NamedTuple):
|
||
|
|
shape: Shape
|
||
|
|
indices_sorted: bool = False
|
||
|
|
unique_indices: bool = False
|
||
|
|
|
||
|
|
#--------------------------------------------------------------------
|
||
|
|
# utilities
|
||
|
|
# TODO: possibly make these primitives, targeting cusparse rountines
|
||
|
|
# csr2coo/coo2csr/SPDDMM
|
||
|
|
|
||
|
|
def nfold_vmap(fun, N, *, broadcasted=True, in_axes=0):
|
||
|
|
"""Convenience function to apply (broadcasted) vmap N times."""
|
||
|
|
_vmap = broadcasting_vmap if broadcasted else vmap
|
||
|
|
for _ in range(N):
|
||
|
|
fun = _vmap(fun, in_axes=in_axes)
|
||
|
|
return fun
|
||
|
|
|
||
|
|
def broadcasting_vmap(fun, in_axes=0, out_axes=0):
|
||
|
|
@functools.wraps(fun)
|
||
|
|
def batched_fun(*args):
|
||
|
|
args_flat, in_tree = tree_util.tree_flatten(args)
|
||
|
|
in_axes_flat = flatten_axes("vmap in_axes", in_tree, in_axes, kws=False)
|
||
|
|
size = max(arg.shape[i] for arg, i in safe_zip(args_flat, in_axes_flat) if i is not None)
|
||
|
|
if size > 1:
|
||
|
|
if any(i is not None and arg.shape[i] not in (1, size)
|
||
|
|
for arg, i in safe_zip(args_flat, in_axes_flat)):
|
||
|
|
raise ValueError("broadcasting_vmap: mismatched input shapes")
|
||
|
|
args_flat, in_axes_flat = zip(*(
|
||
|
|
(arg, None) if i is None else (lax.squeeze(arg, (i,)), None) if arg.shape[i] == 1 else (arg, i)
|
||
|
|
for arg, i in zip(args_flat, in_axes_flat)
|
||
|
|
))
|
||
|
|
new_args = tree_util.tree_unflatten(in_tree, args_flat)
|
||
|
|
new_in_axes = tree_util.tree_unflatten(in_tree, in_axes_flat)
|
||
|
|
return vmap(fun, in_axes=new_in_axes, out_axes=out_axes)(*new_args)
|
||
|
|
return batched_fun
|
||
|
|
|
||
|
|
@jax.jit
|
||
|
|
def _csr_to_coo(indices: Array, indptr: Array) -> Tuple[Array, Array]:
|
||
|
|
"""Given CSR (indices, indptr) return COO (row, col)"""
|
||
|
|
return jnp.cumsum(jnp.zeros_like(indices).at[indptr].add(1)) - 1, indices
|
||
|
|
|
||
|
|
def _csr_extract(indices: Array, indptr: Array, mat: Array) -> Array:
|
||
|
|
"""Extract values of dense matrix mat at given CSR indices."""
|
||
|
|
row, col = _csr_to_coo(indices, indptr)
|
||
|
|
return _coo_extract(row, col, mat)
|
||
|
|
|
||
|
|
def _coo_extract(row: Array, col: Array, mat: Array) -> Array:
|
||
|
|
"""Extract values of dense matrix mat at given COO indices."""
|
||
|
|
return mat[row, col]
|
||
|
|
|
||
|
|
def _count_stored_elements_per_batch(mat: Array, n_batch: int = 0, n_dense: int = 0) -> Array:
|
||
|
|
"""Return per-batch number of stored elements (nse) of a dense matrix."""
|
||
|
|
mat = jnp.asarray(mat)
|
||
|
|
mask = (mat != 0)
|
||
|
|
if n_dense > 0:
|
||
|
|
mask = mask.any(tuple(-(i + 1) for i in range(n_dense)))
|
||
|
|
mask = mask.sum(tuple(range(n_batch, mask.ndim)))
|
||
|
|
return mask
|
||
|
|
|
||
|
|
def _count_stored_elements(mat: Array, n_batch: int = 0, n_dense: int = 0) -> int:
|
||
|
|
"""Return the number of stored elements (nse) of the given dense matrix."""
|
||
|
|
return int(_count_stored_elements_per_batch(mat, n_batch, n_dense).max(initial=0))
|
||
|
|
|
||
|
|
def _dot_general_validated_shape(
|
||
|
|
lhs_shape: Tuple[int, ...], rhs_shape: Tuple[int, ...],
|
||
|
|
dimension_numbers: DotDimensionNumbers) -> Tuple[int, ...]:
|
||
|
|
"""Validate the inputs and return the output shape."""
|
||
|
|
lhs = core.ShapedArray(lhs_shape, np.float32)
|
||
|
|
rhs = core.ShapedArray(rhs_shape, np.float32)
|
||
|
|
return _dot_general_shape_rule(
|
||
|
|
lhs, rhs, dimension_numbers=dimension_numbers,
|
||
|
|
precision=None, preferred_element_type=None)
|