weights_zerofilling.py

# -*- coding: utf-8 -*-
"""
.. invisible:
     _   _ _____ _     _____ _____
    | | | |  ___| |   |  ___/  ___|
    | | | | |__ | |   | |__ \ `--.
    | | | |  __|| |   |  __| `--. \
    \ \_/ / |___| |___| |___/\__/ /
     \___/\____/\_____|____/\____/

Created on Febr 2, 2015

███████████████████████████████████████████████████████████████████████████████

Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.  See the NOTICE file
distributed with this work for additional information
regarding copyright ownership.  The ASF licenses this file
to you 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.

███████████████████████████████████████████████████████████████████████████████
"""


import numpy
from zope.interface import implementer

from veles.accelerated_units import IOpenCLUnit, ICUDAUnit, INumpyUnit
from veles.memory import Array
from veles.distributable import TriviallyDistributable
from veles.znicz.nn_units import ForwardBase


@implementer(IOpenCLUnit, ICUDAUnit, INumpyUnit)
class ZeroFiller(ForwardBase, TriviallyDistributable):
    """Fills weights of given unit with zero on every step"""

    MAPPING = {"zero_filter"}

    def __init__(self, workflow, **kwargs):
        super(ZeroFiller, self).__init__(workflow, **kwargs)

        self.mask = Array()
        self.grouping = kwargs.get("grouping", 1)
        self.demand("weights")

    def init_unpickled(self):
        super(ZeroFiller, self).init_unpickled()
        self.sources_["weights_zerofilling"] = {}

    @property
    def effective_shape(self):
        return (self.weights.shape[0],
                self.weights.size // self.weights.shape[0])

    @property
    def grouping(self):
        return self._grouping

    @grouping.setter
    def grouping(self, value):
        if not isinstance(value, int):
            raise TypeError(
                "grouping value must be an integer (got %s)" % type(value))
        if value < 2:
            raise ValueError("grouping value %d is invalid" % value)
        self._grouping = value

    def initialize(self, device=None, **kwargs):
        super(ZeroFiller, self).initialize(device, **kwargs)
        if not self.weights:
            return True

        if not self.mask:
            if self.effective_shape[1] % self.grouping != 0:
                raise ValueError(
                    "Non-multiple of grouping weights shape detected: "
                    "%s, grouping=%d" %
                    (self.weights.shape, self.grouping))
            self.mask.reset(numpy.zeros(self.effective_shape,
                                        dtype=self.weights.dtype))
            self.mask.map_invalidate()
            # TODO(a.kazantsev): add check for transposed weights.
            for kernel in range(self.effective_shape[0]):
                for chan in range(self.effective_shape[1]):
                    self.mask[kernel, chan] = not (
                        kernel % self.grouping == chan % self.grouping)
        else:
            assert self.mask.shape == self.effective_shape

        for vec in self.mask, self.weights:
            vec.initialize(device)

    def _gpu_init(self):
        self.build_program(cache_file_name="zero_filling_%d" % self.grouping,
                           dtype=self.weights.dtype)

        self.assign_kernel("multiply_by_mask")
        self.set_args(self.mask, self.weights)

    def ocl_init(self):
        self._gpu_init()
        self._global_size = [self.weights.size]
        self._local_size = None

    def cuda_init(self):
        self._gpu_init()
        self._global_size = (self.weights.size, 1, 1)
        self._local_size = (1, 1, 1)

    def numpy_run(self):
        self.mask.map_read()
        self.weights.map_write()

        self.weights.mem *= self.mask.mem

    def _gpu_run(self):
        self.weights.unmap()
        self.mask.unmap()
        self.execute_kernel(self._global_size, self._local_size)

    def ocl_run(self):
        self._gpu_run()

    def cuda_run(self):
        self._gpu_run()