# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 functools import reduce
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import BoolField
from ..array_utils import as_same_device, device
from ..datasource import scalar
from ..utils import infer_dtype
from .core import TensorBinOp, TensorMultiOp
from .utils import TreeReductionBuilder, arithmetic_operand, tree_op_estimate_size
@arithmetic_operand(sparse_mode="binary_or")
class TensorMultiply(TensorBinOp):
_op_type_ = OperandDef.MUL
_func_name = "multiply"
[docs]@infer_dtype(np.multiply)
def multiply(x1, x2, out=None, where=None, **kwargs):
"""
Multiply arguments element-wise.
Parameters
----------
x1, x2 : array_like
Input arrays to be multiplied.
out : Tensor, None, or tuple of Tensor and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or `None`,
a freshly-allocated tensor is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values
of False indicate to leave the value in the output alone.
**kwargs
Returns
-------
y : Tensor
The product of `x1` and `x2`, element-wise. Returns a scalar if
both `x1` and `x2` are scalars.
Notes
-----
Equivalent to `x1` * `x2` in terms of array broadcasting.
Examples
--------
>>> import mars.tensor as mt
>>> mt.multiply(2.0, 4.0).execute()
8.0
>>> x1 = mt.arange(9.0).reshape((3, 3))
>>> x2 = mt.arange(3.0)
>>> mt.multiply(x1, x2).execute()
array([[ 0., 1., 4.],
[ 0., 4., 10.],
[ 0., 7., 16.]])
"""
op = TensorMultiply(**kwargs)
return op(x1, x2, out=out, where=where)
@infer_dtype(np.multiply, reverse=True)
def rmultiply(x1, x2, **kwargs):
op = TensorMultiply(**kwargs)
return op.rcall(x1, x2)
class TensorTreeMultiply(TensorMultiOp):
_op_type_ = OperandDef.TREE_MULTIPLY
_func_name = "multiply"
ignore_empty_input = BoolField("ignore_empty_input", default=False)
def __init__(self, sparse=False, **kw):
super().__init__(sparse=sparse, **kw)
@classmethod
def _is_sparse(cls, *args):
if not args or all(np.isscalar(x) for x in args):
return False
if all(
np.isscalar(x) or (hasattr(x, "issparse") and x.issparse()) for x in args
):
return True
return False
@classmethod
def execute(cls, ctx, op):
inputs, device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
if op.ignore_empty_input:
inputs = [inp for inp in inputs if not hasattr(inp, "size") or inp.size > 0]
with device(device_id):
ctx[op.outputs[0].key] = reduce(xp.multiply, inputs)
@classmethod
def estimate_size(cls, ctx, op):
tree_op_estimate_size(ctx, op)
@infer_dtype(lambda *args: reduce(np.multiply, args))
def tree_multiply(*args, combine_size=None, **kwargs):
class MultiplyBuilder(TreeReductionBuilder):
def _build_reduction(self, inputs, final=False):
op = TensorTreeMultiply(args=inputs, **kwargs)
return op(*inputs)
args = [scalar(a) if np.isscalar(a) else a for a in args]
return MultiplyBuilder(combine_size).build(args)