mindspore2022/mindspore/ops/_grad/grad_implementations.py

# Copyright 2020 Huawei Technologies Co., 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.
# ============================================================================

"""bprop primitives"""
from .. import functional as F
from ..composite import multitype_ops as C
from .grad_base import bprops

# Unused parameters are placeholders.


@bprops.register("scalar_add")
def bprop_scalar_add(x, y, out, dout):
    """Backpropagator for primitive `scalar_add`."""
    return dout, dout


@bprops.register("scalar_mul")
def bprop_scalar_mul(x, y, out, dout):
    """Backpropagator for primitive `scalar_mul`."""
    return dout*y, dout*x


@bprops.register("scalar_sub")
def bprop_scalar_sub(x, y, out, dout):
    """Backpropagator for primitive `scalar_sub`."""
    return dout, -dout


@bprops.register("scalar_div")
def bprop_scalar_div(x, y, out, dout):
    """Backpropagator for primitive `scalar_div`."""
    return dout/y, (-dout) * (out/y)


@bprops.register("scalar_pow")
def bprop_scalar_pow(x, y, out, dout):
    """Backpropagator for primitive `scalar_pow`."""
    return dout * (y * (x ** (y-1))), dout * (F.scalar_log(x) * out)


@bprops.register("scalar_exp")
def bprop_scalar_exp(x, out, dout):
    """Backpropagator for primitive `scalar_exp`."""
    return (dout * out,)


@bprops.register("scalar_uadd")
def bprop_scalar_uadd(x, out, dout):
    """Backpropagator for primitive `scalar_uadd`."""
    return (dout,)


@bprops.register("scalar_usub")
def bprop_scalar_usub(x, out, dout):
    """Backpropagator for primitive `scalar_usub`."""
    return (-dout,)


@bprops.register("scalar_gt")
def bprop_scalar_gt(x, y, out, dout):
    """Backpropagator for primitive `scalar_gt`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("scalar_lt")
def bprop_scalar_lt(x, y, out, dout):
    """Backpropagator for primitive `scalar_lt`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("scalar_ge")
def bprop_scalar_ge(x, y, out, dout):
    """Backpropagator for primitive `scalar_ge`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("scalar_le")
def bprop_scalar_le(x, y, out, dout):
    """Backpropagator for primitive `scalar_le`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("scalar_eq")
def bprop_scalar_eq(x, y, out, dout):
    """Backpropagator for primitive `scalar_eq`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("scalar_ne")
def bprop_scalar_ne(x, y, out, dout):
    """Backpropagator for primitive `scalar_eq`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("scalar_cast")
def bprop_scalar_cast(x, t, out, dout):
    """Backpropagator for primitive `scalar_cast`."""
    return F.scalar_cast(dout, F.typeof(x)), t


@bprops.register("tuple_getitem")
def bprop_tuple_getitem(data, idx, out, dout):
    """Backpropagator for primitive `tuple_getitem`."""
    return F.tuple_setitem(C.zeros_like(data), idx, dout), C.zeros_like(idx)


@bprops.register("identity")
def bprop_identity(x, out, dout):
    """Backpropagator for primitive `identity`."""
    return (dout,)


@bprops.register("make_ref")
def bprop_make_ref(key, x, y, out, dout):
    """Backpropagator for primitive `make_ref`."""
    return (C.zeros_like(key), dout, C.zeros_like(y))


@bprops.register("get_ref_value")
def bprop_get_ref_value(x, out, dout):
    """Backpropagator for primitive `get_ref_value`."""
    return (dout,)


@bprops.register("get_ref_key")
def bprop_get_ref_key(x, out, dout):
    """Backpropagator for primitive `get_ref_key`."""
    return (C.zeros_like(x),)


@bprops.register("scalar_to_array")
def bprop_scalar_to_array(x, out, dout):
    """Backpropagator for primitive `scalar_to_array`."""
    return (F.array_to_scalar(dout),)


@bprops.register("array_to_scalar")
def bprop_array_to_scalar(x, out, dout):
    """Backpropagator for primitive `array_to_scalar`."""
    return (F.scalar_to_array(dout),)


@bprops.register("dot")
def bprop_dot(x, y, out, dout):
    """Backpropagator for primitive `dot`."""
    return F.dot(dout, F.transpose(y, (1, 0))), F.dot(F.transpose(x, (1, 0)), dout)


@bprops.register("reshape")
def bprop_reshape(xs, shp, out, dout):
    """Backpropagator for primitive `reshape`."""
    return F.reshape(dout, F.shape(xs)), C.zeros_like(shp)


@bprops.register("distribute")
def bprop_distribute(arr, shp, out, dout):
    """Backpropagator for primitive `distribute`."""
    return F.array_reduce(F.scalar_add, dout, F.shape(arr)), C.zeros_like(shp)


@bprops.register("shape")
def bprop_shape(arr, out, dout):
    """Backpropagator for primitive `shape`."""
    return (C.zeros_like(arr),)


@bprops.register("broadcast_shape")
def bprop_broadcast_shape(shp1, shp2, out, dout):
    """Backpropagator for primitive `broadcast_shape`."""
    return C.zeros_like(shp1), C.zeros_like(shp2)


@bprops.register("J")
def bprop_j(x, out, dout):
    """Backpropagator for primitive `J`."""
    return (F.jinv(dout),)


@bprops.register("array_reduce")
def bprop_array_reduce(fn, x, shp, out, dout):
    """Backpropagator for primitive `array_reduce`."""
    return F.distribute(dout, F.shape(x)), C.zeros_like(shp)


@bprops.register("depend")
def bprop_depend(x, y, out, dout):
    """Backpropagator for primitive `depend`."""
    return dout, C.zeros_like(y)


@bprops.register("embed")
def bprop_embed(x, out, dout):
    """Backpropagator for primitive `embed`."""
    return (C.zeros_like(x),)


@bprops.register("bool_not")
def bprop_bool_not(x, out, dout):
    """Backpropagator for primitive `bool_not`."""
    return (C.zeros_like(x),)


@bprops.register("bool_or")
def bprop_bool_or(x, y, out, dout):
    """Backpropagator for primitive `bool_or`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("stop_gradient")
def bprop_stop_gradient(x, out, dout):
    """Backpropagator for primitive `stop_gradient`."""
    return (C.zeros_like(x),)


@bprops.register("bool_and")
def bprop_bool_and(x, y, out, dout):
    """Backpropagator for primitive `bool_and`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("ControlDepend")
def bprop_control_depend(x, y, out, dout):
    """Backpropagator for primitive `Control_depend`."""
    return C.zeros_like(x), C.zeros_like(y)


@bprops.register("switch")
def bprop_switch(cond, tb, fb, out, dout):
    """Backpropagator for primitive `switch`."""
    return C.zeros_like(cond), F.switch(cond, dout, C.zeros_like(tb)), \
            F.switch(cond, C.zeros_like(fb), dout)