# Copyright 2024 BDP Ecosystem Limited. 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
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# ==============================================================================
from typing import Union, Tuple
import brainunit as u
import jax
import jax.numpy as jnp
import numpy as np
from .csrmm import raw_event_csrmm_taichi
from .csrmv import event_csrmv_taichi
__all__ = [
'event_csrmv',
'event_csrmm',
]
[docs]
def event_csrmm(
data: Union[jax.typing.ArrayLike, u.Quantity],
indices: jax.typing.ArrayLike,
indptr: jax.typing.ArrayLike,
matrix: jax.typing.ArrayLike,
*,
shape: Tuple[int, int],
transpose: bool = False,
):
"""Product of CSR sparse matrix and a dense event matrix.
Args:
data : array of shape ``(nse,)``, float.
indices : array of shape ``(nse,)``
indptr : array of shape ``(shape[0] + 1,)`` and dtype ``indices.dtype``
matrix : array of shape ``(shape[0] if transpose else shape[1], cols)`` and
dtype ``data.dtype``
shape : length-2 tuple representing the matrix shape
transpose : boolean specifying whether to transpose the sparse matrix
before computing.
Returns:
C : array of shape ``(shape[1] if transpose else shape[0], cols)``
representing the matrix-matrix product product.
"""
return raw_event_csrmm_taichi(data, indices, indptr, matrix, shape=shape, transpose=transpose)[0]
[docs]
def event_csrmv(
data: Union[jax.typing.ArrayLike, u.Quantity],
indices: jax.Array,
indptr: jax.Array,
events: jax.Array,
*,
shape: Tuple[int, int],
transpose: bool = False,
) -> jax.Array:
"""Product of a sparse CSR matrix and a dense event vector.
This function supports JAX transformations, including `jit()`, `grad()`,
`vmap()` and `pmap()`.
Parameters
----------
data: ndarray, float
An array of shape ``(nse,)``.
indices: ndarray
An array of shape ``(nse,)``.
indptr: ndarray
An array of shape ``(shape[0] + 1,)`` and dtype ``indices.dtype``.
events: ndarray
An array of shape ``(shape[0] if transpose else shape[1],)``
and dtype ``data.dtype``.
shape: tuple
A length-2 tuple representing the matrix shape.
transpose: bool
A boolean specifying whether to transpose the sparse matrix
before computing.
If ``transpose=True``, the operator will compute based on the
event-driven property of the ``events`` vector.
Returns
-------
y : Array
The array of shape ``(shape[1] if transpose else shape[0],)`` representing
the matrix vector product.
"""
# checking
data = jnp.atleast_1d(data)
if np.ndim(data) == 1:
if data.shape[0] not in [1, indices.shape[0]]:
raise ValueError('The size of data should be 1 or be consistent with indices.'
f'But we got {data.shape} != {indices.shape}, {data.shape} != 1.')
else:
raise ValueError('data should be a scalar or 1D vector. '
f'But we got {np.ndim(data)}-D array.')
if np.ndim(indices) != 1:
raise ValueError('indices should be a 1D vector with integer type.')
if np.ndim(indptr) != 1:
raise ValueError('indptr should be a 1D vector with integer type.')
if indices.dtype not in [jnp.int8, jnp.int16, jnp.int32, jnp.int64, jnp.uint8, jnp.uint16, jnp.uint32, jnp.uint64]:
raise ValueError(
'indices should be a 1D vector with int8, int16, int32, int64, uint8, uint16, uint32 or uint64 type.')
if indptr.dtype not in [jnp.int8, jnp.int16, jnp.int32, jnp.int64, jnp.uint8, jnp.uint16, jnp.uint32, jnp.uint64]:
raise ValueError(
'indptr should be a 1D vector with int8, int16, int32, int64, uint8, uint16, uint32 or uint64 type.')
if np.ndim(events) != 1:
raise ValueError('events should be a 1D vector.')
if len(shape) != 2:
raise ValueError('shape should be a length-2 tuple.')
if transpose:
if events.shape[0] != shape[0]:
raise ValueError(f'Shape mismatch, vec ({events.shape[0]},) @ mat {shape}.')
else:
if events.shape[0] != shape[1]:
raise ValueError(f'Shape mismatch, mat {shape} @ vec ({events.shape[0]},).')
# if the shape of indices is (0,), then we return a zero vector
if indices.shape[0] == 0:
return jnp.zeros(shape[1] if transpose else shape[0], dtype=data.dtype)
return event_csrmv_taichi(data, indices, indptr, events, shape=shape, transpose=transpose)[0]
