forked from huawei/mindspore2022
274 lines
7.9 KiB
Python
274 lines
7.9 KiB
Python
# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""bprop primitives"""
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from mindspore.ops import _constants
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from ..operations import _grad_ops as G
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from .. import functional as F
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from .. import operations as P
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from ..composite import multitype_ops as C
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from .grad_base import bprops
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get_dtype = P.DType()
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# Unused parameters are placeholders.
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@bprops.register("MaximumGrad")
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@bprops.register("MinimumGrad")
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def bprop_max_and_minimum_grad_grad(x, y, z, out, dout):
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"""Backpropagator for primitive `MaximumGrad` and `MinimumGrad`."""
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out0 = F.cast(out[0] != 0, get_dtype(dout[0]))
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out1 = F.cast(out[1] != 0, get_dtype(dout[1]))
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dz = out0 * dout[0] + out1 * dout[1]
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return F.zeros_like(x), F.zeros_like(y), dz
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@bprops.register("ReluGrad")
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def bprop_relu_grad_grad(x, y, out, dout):
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"""Backpropagator for primitive `ReluGrad`."""
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input_grad = G.ReluGrad()
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dy = input_grad(dout, y)
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return dy, F.zeros_like(y)
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@bprops.register(_constants.kScalarAdd)
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def bprop_scalar_add(x, y, out, dout):
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"""Backpropagator for primitive `scalar_add`."""
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return dout, dout
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@bprops.register(_constants.kScalarMul)
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def bprop_scalar_mul(x, y, out, dout):
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"""Backpropagator for primitive `scalar_mul`."""
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return dout*y, dout*x
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@bprops.register(_constants.kScalarSub)
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def bprop_scalar_sub(x, y, out, dout):
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"""Backpropagator for primitive `scalar_sub`."""
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return dout, -dout
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@bprops.register(_constants.kScalarDiv)
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def bprop_scalar_div(x, y, out, dout):
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"""Backpropagator for primitive `scalar_div`."""
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return dout/y, (-dout) * (out/y)
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@bprops.register(_constants.kScalarPow)
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def bprop_scalar_pow(x, y, out, dout):
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"""Backpropagator for primitive `scalar_pow`."""
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return dout * (y * (x ** (y-1))), dout * (F.scalar_log(x) * out)
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@bprops.register("scalar_exp")
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def bprop_scalar_exp(x, out, dout):
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"""Backpropagator for primitive `scalar_exp`."""
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return (dout * out,)
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@bprops.register(_constants.kScalarUadd)
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def bprop_scalar_uadd(x, out, dout):
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"""Backpropagator for primitive `scalar_uadd`."""
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return (dout,)
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@bprops.register(_constants.kScalarUsub)
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def bprop_scalar_usub(x, out, dout):
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"""Backpropagator for primitive `scalar_usub`."""
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return (-dout,)
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@bprops.register("scalar_gt")
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def bprop_scalar_gt(x, y, out, dout):
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"""Backpropagator for primitive `scalar_gt`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("scalar_lt")
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def bprop_scalar_lt(x, y, out, dout):
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"""Backpropagator for primitive `scalar_lt`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("scalar_ge")
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def bprop_scalar_ge(x, y, out, dout):
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"""Backpropagator for primitive `scalar_ge`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("scalar_le")
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def bprop_scalar_le(x, y, out, dout):
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"""Backpropagator for primitive `scalar_le`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("scalar_eq")
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def bprop_scalar_eq(x, y, out, dout):
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"""Backpropagator for primitive `scalar_eq`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("scalar_ne")
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def bprop_scalar_ne(x, y, out, dout):
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"""Backpropagator for primitive `scalar_eq`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("scalar_cast")
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def bprop_scalar_cast(x, t, out, dout):
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"""Backpropagator for primitive `scalar_cast`."""
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return F.scalar_cast(dout, F.typeof(x)), t
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@bprops.register(_constants.kTupleGetItem)
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def bprop_tuple_getitem(data, idx, out, dout):
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"""Backpropagator for primitive `tuple_getitem`."""
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return F.tuple_setitem(C.zeros_like(data), idx, dout), C.zeros_like(idx)
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@bprops.register("list_getitem")
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def bprop_list_getitem(data, idx, out, dout):
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"""Backpropagator for primitive `list_getitem`."""
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return F.list_setitem(C.zeros_like(data), idx, dout), C.zeros_like(idx)
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@bprops.register("identity")
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def bprop_identity(x, out, dout):
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"""Backpropagator for primitive `identity`."""
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return (dout,)
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@bprops.register("make_ref")
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def bprop_make_ref(key, x, y, out, dout):
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"""Backpropagator for primitive `make_ref`."""
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return (C.zeros_like(key), dout, C.zeros_like(y))
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@bprops.register("get_ref_value")
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def bprop_get_ref_value(x, out, dout):
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"""Backpropagator for primitive `get_ref_value`."""
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return (dout,)
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@bprops.register("get_ref_key")
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def bprop_get_ref_key(x, out, dout):
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"""Backpropagator for primitive `get_ref_key`."""
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return (C.zeros_like(x),)
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@bprops.register("scalar_to_array")
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def bprop_scalar_to_array(x, out, dout):
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"""Backpropagator for primitive `scalar_to_array`."""
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return (F.array_to_scalar(dout),)
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@bprops.register("array_to_scalar")
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def bprop_array_to_scalar(x, out, dout):
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"""Backpropagator for primitive `array_to_scalar`."""
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return (F.scalar_to_array(dout),)
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@bprops.register("reshape")
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def bprop_reshape(xs, shp, out, dout):
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"""Backpropagator for primitive `reshape`."""
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return F.reshape(dout, F.shape(xs)), C.zeros_like(shp)
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@bprops.register("distribute")
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def bprop_distribute(arr, shp, out, dout):
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"""Backpropagator for primitive `distribute`."""
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return F.array_reduce(F.scalar_add, dout, F.shape(arr)), C.zeros_like(shp)
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@bprops.register("shape")
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def bprop_shape(arr, out, dout):
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"""Backpropagator for primitive `shape`."""
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return (C.zeros_like(arr),)
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@bprops.register("broadcast_shape")
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def bprop_broadcast_shape(shp1, shp2, out, dout):
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"""Backpropagator for primitive `broadcast_shape`."""
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return C.zeros_like(shp1), C.zeros_like(shp2)
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@bprops.register("array_reduce")
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def bprop_array_reduce(fn, x, shp, out, dout):
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"""Backpropagator for primitive `array_reduce`."""
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return F.distribute(dout, F.shape(x)), C.zeros_like(shp)
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@bprops.register("Depend")
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def bprop_depend(x, y, out, dout):
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"""Backpropagator for primitive `depend`."""
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return dout, C.zeros_like(y)
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@bprops.register("embed")
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def bprop_embed(x, out, dout):
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"""Backpropagator for primitive `embed`."""
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return (C.zeros_like(x),)
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@bprops.register("bool_not")
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def bprop_bool_not(x, out, dout):
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"""Backpropagator for primitive `bool_not`."""
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return (C.zeros_like(x),)
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@bprops.register("bool_or")
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def bprop_bool_or(x, y, out, dout):
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"""Backpropagator for primitive `bool_or`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("stop_gradient")
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def bprop_stop_gradient(x, out, dout):
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"""Backpropagator for primitive `stop_gradient`."""
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return (C.zeros_like(x),)
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@bprops.register("bool_and")
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def bprop_bool_and(x, y, out, dout):
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"""Backpropagator for primitive `bool_and`."""
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return C.zeros_like(x), C.zeros_like(y)
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@bprops.register("Switch")
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def bprop_switch(cond, tb, fb, out, dout):
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"""Backpropagator for primitive `switch`."""
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return C.zeros_like(cond), F.switch(cond, dout, C.zeros_like(tb)), \
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F.switch(cond, C.zeros_like(fb), dout)
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def _fprop_switch_layer(index, layers):
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"""Backpropagator for primitive `switch_layer`."""
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def _bprop_switch_layer(dout):
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return dout, C.zeros_like(index), ()
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return F.switch_layer(index, layers), _bprop_switch_layer
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@bprops.register("UpdateState")
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def bprop_update_state(u_monad, x, out, dout):
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"""Backpropagator for primitive `UpdateState`."""
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return C.zeros_like(u_monad), C.zeros_like(x)
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@bprops.register("Load")
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def bprop_load(param, u_monad, out, dout):
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"""Backpropagator for primitive `load`."""
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return dout, C.zeros_like(u_monad)
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