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!3098 IndexedSlices adapter of sparse optimizer 

Merge pull request !3098 from wangnan39/sparse_optimizer_adapter_indexedslice
tags/v0.6.0-beta
mindspore-ci-bot Gitee 5 years ago
parent
8105f6e33d 86889c59cb
commit
10053f3d72
10 changed files with 73 additions and 233 deletions
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  1. +11
    -7
      mindspore/nn/optim/adam.py
  2. +11
    -7
      mindspore/nn/optim/ftrl.py
  3. +3
    -3
      mindspore/nn/optim/lazyadam.py
  4. +8
    -6
      mindspore/nn/optim/optimizer.py
  5. +3
    -2
      mindspore/nn/optim/proximal_ada_grad.py
  6. +16
    -15
      mindspore/nn/wrap/grad_reducer.py
  7. +3
    -2
      mindspore/ops/_grad/grad_array_ops.py
  8. +13
    -12
      mindspore/ops/_grad/grad_comm_ops.py
  9. +0
    -174
      tests/ut/python/nn/optim/test_adam_with_tuple_grad.py
  10. +5
    -5
      tests/ut/python/parallel/test_sparse_feature_bprop.py

+ 11
- 7
mindspore/nn/optim/adam.py View File

@@ -108,24 +108,26 @@ def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, po
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)


@_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tuple",
@_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "IndexedSlices",
"Tensor", "Tensor", "Tensor", "Bool")
def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2, ps_parameter):
"""Apply sparse adam optimizer to the weight parameter when the gradient is sparse."""
success = True
indices = gradient.indices()
values = gradient.values()
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("Adam", [0, 1, 2])
shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),
op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
op_shape(beta2), op_shape(eps), op_shape(gradient[1]), op_shape(gradient[0]))
op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
success = F.depend(success, _ps_pull(_ps_push((beta1_power, beta2_power, lr, beta1, beta2,
eps, gradient[1], gradient[0]), shapes), params))
eps, values, indices), shapes), params))
else:
success = F.depend(success, sparse_opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
eps, gradient[1], gradient[0]))
eps, values, indices))
return success


@@ -149,17 +151,19 @@ def _run_opt_with_one_number(opt, sparse_opt, beta1_power, beta2_power, beta1, b


@_adam_push_pull_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tuple", "Tensor", "Tensor", "Tensor")
"Tensor", "IndexedSlices", "Tensor", "Tensor", "Tensor")
def _run_push_pull_opt_with_sparse(push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2):
"""Apply sparse adam optimizer by push and pull to the weight parameter when the gradient is sparse."""
success = True
op_shape = P.Shape()
values = gradient.values()
indices = gradient.indices()
shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),
op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
op_shape(beta2), op_shape(eps), op_shape(gradient[1]), op_shape(gradient[0]))
op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
eps, gradient[1], gradient[0]), shapes), params))
eps, values, indices), shapes), params))
return success




+ 11
- 7
mindspore/nn/optim/ftrl.py View File

@@ -25,20 +25,22 @@ _ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
_ftrl_push_pull_opt = C.MultitypeFuncGraph("ftrl_opt")


@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tuple", "Tensor",
@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "IndexedSlices", "Tensor",
"Tensor", "Bool")
def _tensor_run_opt_with_sparse(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment,
ps_parameter):
"""Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
success = True
indices = gradient.indices()
values = gradient.values()
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("Ftrl", [0, 1, 2])
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(gradient[1]), op_shape(gradient[0]))
success = F.depend(success, _ps_pull(_ps_push((gradient[1], gradient[0]), shapes), weight))
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
success = F.depend(success, _ps_pull(_ps_push((values, indices), shapes), weight))
else:
success = F.depend(success, spars_opt(weight, moment, linear, gradient[1], gradient[0]))
success = F.depend(success, spars_opt(weight, moment, linear, values, indices))
return success


@@ -58,14 +60,16 @@ def _tensor_run_opt(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gra
return success


@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tuple",
@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "IndexedSlices",
"Tensor", "Tensor")
def _tensor_run_push_pull_opt_with_sparse(push, pull, learning_rate, l1, l2, lr_power, linear, gradient,
weight, moment):
success = True
op_shape = P.Shape()
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(gradient[1]), op_shape(gradient[0]))
success = F.depend(success, pull(push((gradient[1], gradient[0]), shapes), weight))
values = gradient.values()
indices = gradient.indices()
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
success = F.depend(success, pull(push((values, indices), shapes), weight))
return success




+ 3
- 3
mindspore/nn/optim/lazyadam.py View File

@@ -27,14 +27,14 @@ from .optimizer import Optimizer
_lazy_adam_opt = C.MultitypeFuncGraph("lazy_adam_opt")


@_lazy_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tuple",
"Tensor", "Tensor", "Tensor")
@_lazy_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor",
"IndexedSlices", "Tensor", "Tensor", "Tensor")
def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2):
"""Apply sparse lazy adam optimizer to the weight parameter when the gradient is sparse."""
success = True
success = F.depend(success, sparse_opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
eps, gradient[1], gradient[0]))
eps, gradient.values(), gradient.indices()))
return success




+ 8
- 6
mindspore/nn/optim/optimizer.py View File

@@ -22,7 +22,7 @@ from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.nn.cell import Cell
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor
from mindspore.common.tensor import Tensor, IndexedSlices
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
@@ -490,12 +490,14 @@ op_gather = P.GatherV2()
_apply_decay = C.MultitypeFuncGraph("apply_decay")


@_apply_decay.register("Number", "Bool", "Tensor", "Tuple")
@_apply_decay.register("Number", "Bool", "Tensor", "IndexedSlices")
def _tensor_apply_decay_with_sparse(weight_decay, if_apply, weight, gradient):
"""Get grad with weight_decay."""
if if_apply:
weight = op_gather(weight, gradient[0], 0)
return gradient[0], op_add((weight * weight_decay, gradient[1])), gradient[2]
indices = gradient.indices()
values = op_add((op_gather(weight, indices, 0) * weight_decay, gradient.values()))
shape = gradient.dense_shape()
return IndexedSlices(indices, values, shape)
return gradient


@@ -518,9 +520,9 @@ def tensor_grad_scale(scale, grad):
return grad * scale


@_grad_scale.register("Number", "Tuple")
@_grad_scale.register("Number", "IndexedSlices")
def tensor_grad_scale_with_sparse(scale, grad):
"""Get grad with scale."""
if scale == 1.0:
return grad
return grad[0], grad[1] * scale, grad[2]
return IndexedSlices(grad.indices(), grad.values() * scale, grad.dense_shape())

+ 3
- 2
mindspore/nn/optim/proximal_ada_grad.py View File

@@ -23,11 +23,12 @@ from .optimizer import Optimizer
_proximal_ada_grad_opt = C.MultitypeFuncGraph("proximal_ada_grad_opt")
@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tuple", "Tensor", "Tensor")
@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "IndexedSlices", "Tensor",
"Tensor")
def _tensor_run_opt_with_sparse(opt, sparse_opt, learning_rate, l1, l2, gradient, weight, accum):
"""Apply sparse proximal_ada_grad optimizer to the weight parameter."""
success = True
success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient[1], gradient[0]))
success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient.values(), gradient.indices()))
return success


+ 16
- 15
mindspore/nn/wrap/grad_reducer.py View File

@@ -16,6 +16,7 @@
from mindspore import context
from mindspore.nn.cell import Cell
from mindspore.communication.management import GlobalComm, get_group_size
from mindspore.common.tensor import IndexedSlices
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.ops.operations.comm_ops import AllReduce, AllGather
from mindspore.parallel._auto_parallel_context import auto_parallel_context
@@ -77,7 +78,7 @@ def _tensors_allreduce(degree, mean, allgather, allreduce_filter, grad, allreduc
return grad


@reduce_opt.register("Number", "Bool", "Function", "Bool", "Tuple", "Function")
@reduce_opt.register("Number", "Bool", "Function", "Bool", "IndexedSlices", "Function")
def _tensors_allreduce_with_sparse(degree, mean, allgather, allreduce_filter, grad, allreduce):
"""
Apply allgather on gradient instead of allreduce for sparse feature.
@@ -88,21 +89,21 @@ def _tensors_allreduce_with_sparse(degree, mean, allgather, allreduce_filter, gr
mean (bool): When mean is true, the mean coefficient (degree) would apply on gradients.
allgather (Primitive): The communication operator for sparse gradients.
allreduce_filter (bool): When it is true, allgather would apply.
grad (tuple): The indices, gradient tensor and tensor_shape before operation.
grad (IndexedSlices): The gradient before operation.
allreduce (Primitive): The communication operator for gradients.

Returns:
Tuple, include indices, the gradient tensor and tensor_shape after operation.
IndexedSlices, the gradient after operation.
"""
if allreduce_filter:
indices = allgather(grad[0])
dout = allgather(grad[1])
indices = allgather(grad.indices())
dout = allgather(grad.values())
if mean:
degree = F.scalar_cast(degree, F.dtype(grad[1]))
degree = F.scalar_cast(degree, F.dtype(grad.values()))
cast_op = P.Cast()
mul_op = P.Mul()
dout = mul_op(dout, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(dout)))
grad = (indices, dout, grad[2])
grad = IndexedSlices(indices, dout, grad.dense_shape())
return grad


@@ -123,18 +124,18 @@ def _tensors_get_datatype(grad):
return F.dtype(grad)


@_get_datatype.register("Tuple")
@_get_datatype.register("IndexedSlices")
def _tensors_get_datatype_with_sparse(grad):
"""
Acquire gradient datatype.

Args:
grad (Tuple): The gradient tensor before operation.
grad (IndexedSlices): The gradient before operation.

Returns:
mstype, the datatype of gradient.
"""
return F.dtype(grad[1])
return F.dtype(grad.values())


_cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@@ -155,20 +156,20 @@ def _tensors_cast_datatype(datatype, grad):
return F.cast(grad, datatype)


@_cast_datatype.register("TypeType", "Tuple")
@_cast_datatype.register("TypeType", "IndexedSlices")
def _tensors_cast_datatype_with_sparse(datatype, grad):
"""
Cast gradient to datatype.

Args:
datatype (mstype): the destination datatype of gradient.
grad (Tuple): The gradient tensor before operation.
grad (IndexedSlices): The gradient before operation.

Returns:
Tuple, the gradient tuple after operation.
IndexedSlices, the gradient after operation.
"""
dout = F.cast(grad[1], datatype)
return (grad[0], dout, grad[2])
dout = F.cast(grad.values(), datatype)
return IndexedSlices(grad.indices(), dout, grad.dense_shape())


class DistributedGradReducer(Cell):


+ 3
- 2
mindspore/ops/_grad/grad_array_ops.py View File

@@ -25,6 +25,7 @@ from .grad_base import bprop_getters
from ..primitive import constexpr
from ... import context
from ...common import dtype as mstype
from ...common.tensor import IndexedSlices

reduce_sum = P.ReduceSum()
unsorted_segment_sum = P.UnsortedSegmentSum()
@@ -206,7 +207,7 @@ def get_bprop_embedding_lookup(self):
actual_dout_shape_changed = new_indices_shape_changed + x_shp_tail
# Reshape the 'actual_dout' on device
actual_dout = reshape_op(dout, actual_dout_shape_changed)
return (new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset)
return IndexedSlices(new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset)
return bprop_sparse


@@ -335,7 +336,7 @@ def get_bprop_sparse_gather_v2(self):
values_shape = indices_size + x_tail_shp
values = reshape(dout, values_shape)
indices = reshape(indices, indices_size)
return (indices, values, x_shp), zeros_like(indices), zeros_like(axis)
return IndexedSlices(indices, values, x_shp), zeros_like(indices), zeros_like(axis)
if F.rank(dout) == 0:
dout = P.ExpandDims()(dout, -1)
if F.rank(indices) == 0:


+ 13
- 12
mindspore/ops/_grad/grad_comm_ops.py View File

@@ -17,6 +17,7 @@
import mindspore.common.dtype as mstype
from mindspore.ops import functional as F
from .. import operations as P
from ...common.tensor import IndexedSlices
from ..composite.multitype_ops.zeros_like_impl import zeros_like
from ..operations.comm_ops import (AllGather, _HostAllGather, AllReduce, _AlltoAll, Broadcast,
_GetTensorSlice, _MirrorOperator, ReduceOp,
@@ -46,9 +47,9 @@ def get_bprop_all_reduce(self):
if F.issubclass_(F.typeof(dout), mstype.tensor):
dx = all_reduce_grad(dout)
else:
indices = all_gather(dout[0])
grad = all_gather(dout[1])
dx = (indices, grad, dout[2])
indices = all_gather(dout.indices())
grad = all_gather(dout.values())
dx = IndexedSlices(indices, grad, dout.dense_shape())
return (dx,)
else:

@@ -59,12 +60,12 @@ def get_bprop_all_reduce(self):
z = cast(z, dtype(dx))
dx = mul(dx, z)
else:
indices = all_gather(dout[0])
grad = all_gather(dout[1])
indices = all_gather(dout.indices())
grad = all_gather(dout.values())
z = equal(x, out)
z = cast(z, dtype(grad))
grad = mul(grad, z)
dx = (indices, grad, dout[2])
dx = IndexedSlices(indices, grad, dout.dense_shape())
return (dx,)
return bprop

@@ -194,19 +195,19 @@ def get_bprop_mirror_operator(self):
num = F.scalar_cast(dev_num, F.dtype(dx))
dx = mul(dx, cast(F.scalar_to_array(float_one/num), F.dtype(dx)))
else:
indices = all_gather(dout[0])
grad = all_gather(dout[1])
indices = all_gather(dout.indices())
grad = all_gather(dout.values())
float_one = F.scalar_cast(1.0, F.dtype(grad))
num = F.scalar_cast(dev_num, F.dtype(grad))
grad = mul(grad, cast(F.scalar_to_array(float_one/num), F.dtype(grad)))
dx = (indices, grad, dout[2])
dx = (indices, grad, dout.dense_shape())
else:
if F.issubclass_(F.typeof(dout), mstype.tensor):
dx = all_reduce(dout)
else:
indices = all_gather(dout[0])
grad = all_gather(dout[1])
dx = (indices, grad, dout[2])
indices = all_gather(dout.indices())
grad = all_gather(dout.values())
dx = (indices, grad, dout.dense_shape())

return (dx,)
return bprop


+ 0
- 174
tests/ut/python/nn/optim/test_adam_with_tuple_grad.py View File

@@ -1,174 +0,0 @@
# 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.
# ============================================================================
""" test adam """
import numpy as np

import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.common.api import _executor
from mindspore.common import dtype as mstype
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Optimizer
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel

context.set_context(enable_sparse=True)

adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor", "Tensor", "Bool")
def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
op_mul = P.Mul()
op_square = P.Square()
op_sqrt = P.Sqrt()
op_cast = P.Cast()
op_reshape = P.Reshape()
op_shape = P.Shape()

param_fp32 = op_cast(param, mstype.float32)
m_fp32 = op_cast(m, mstype.float32)
v_fp32 = op_cast(v, mstype.float32)
gradient_fp32 = op_cast(gradient, mstype.float32)

next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)

next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
- beta2, op_square(gradient_fp32))

update = next_m / (op_sqrt(next_v) + eps)
if decay_flag:
update = update + op_mul(weight_decay_tensor, param_fp32)

update_with_lr = op_mul(lr, update)
next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))

next_v = F.depend(next_v, F.assign(param, next_param))
next_v = F.depend(next_v, F.assign(m, next_m))
next_v = F.depend(next_v, F.assign(v, next_v))
return next_v


@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor", "Tuple", "Bool")
def _update_run_op_sparse_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
return gradient[2][2]

def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
"""Check the type of inputs."""
validator.check_value_type("beta1", beta1, [float], prim_name)
validator.check_value_type("beta2", beta2, [float], prim_name)
validator.check_value_type("eps", eps, [float], prim_name)
validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)


class AdamWeightDecaySparse(Optimizer):
"""
Implements Adam algorithm weight decay fix.

Args:
params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
should be class mindspore.Parameter.
learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
Iterable or a Tensor and the dims of the Tensor is 1,
use dynamic learning rate, then the i-th step will
take the i-th value as the learning rate.
When the learning_rate is float or learning_rate is a Tensor
but the dims of the Tensor is 0, use fixed learning rate.
Other cases are not supported. Default: 1e-3.
beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
Should be in range (0.0, 1.0).
beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
Should be in range (0.0, 1.0).
eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
Should be greater than 0.
weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.

Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`,
and might be in sparse format.

Outputs:
tuple[Parameter], the updated velocity value, the shape is the same as `params`.

Examples:
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> optim = nn.AdamWeightDecay(params=net.trainable_params())
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
"""
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
super(AdamWeightDecaySparse, self).__init__(learning_rate, params)
if self.is_group:
raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
_check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
self.eps = Tensor(np.array([eps]).astype(np.float32))
self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))

self.params = self.parameters
self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
self.decay_flag = tuple(decay_filter(x) for x in self.params)

self.map = C.Map()

def construct(self, gradients):
lr = self.get_lr()
updated_velocity = self.map(F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
self.weight_decay_tensor),
self.params, self.moments1, self.moments2, gradients, self.decay_flag)

return updated_velocity


def test_AdamWeightDecaySparse():
""" test_AdamWeightDecaySparse """
context.set_context(mode=context.GRAPH_MODE)
class Loss(nn.Cell):
def __init__(self):
super(Loss, self).__init__()
def construct(self, base, target):
return base
class NetWithSparseGatherV2(nn.Cell):
def __init__(self):
super(NetWithSparseGatherV2, self).__init__()
self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1")
self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
self.gatherv2 = P.SparseGatherV2()
self.axis = 0
def construct(self, indices):
return self.gatherv2(self.w1, indices, self.axis) * self.w2

inputs = Tensor(np.array([0, 1]).astype(np.int32))
label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
net = NetWithSparseGatherV2()
net.set_train()
loss = Loss()
optimizer = AdamWeightDecaySparse(net.trainable_params())

net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)

+ 5
- 5
tests/ut/python/parallel/test_sparse_feature_bprop.py View File

@@ -19,8 +19,8 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import context
from mindspore.common import dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import composite as C
from mindspore.common.tensor import Tensor, IndexedSlices
from mindspore.ops import composite as C, operations as P
from mindspore.ops.operations.comm_ops import AllReduce, _MirrorOperator
from mindspore.ops._grad.grad_base import bprop_getters
from mindspore._checkparam import Validator as validator
@@ -65,7 +65,7 @@ def get_bprop_gather_v2(self):
"""Generate bprop for GatherV2"""

def bprop(x, indices, axis, out, dout):
return (indices, dout, x), axis, out
return IndexedSlices(indices, dout, x), axis, out

return bprop

@@ -78,7 +78,7 @@ def test_bprop_with_sparse_feature_allreduce():
if shape is None:
shape = [8, 8]
self.all_reduce = AllReduce()
self.gatherv2 = VirtualGatherV2()
self.gatherv2 = P.GatherV2()
self.index = Tensor(np.ones(shape), dtype=ms.int32)
self.axis = axis

@@ -102,7 +102,7 @@ def test_bprop_with_sparse_feature_mirror():
if shape is None:
shape = [8, 8]
self.mirror = _MirrorOperator(group=HCCL_WORLD_COMM_GROUP)
self.gatherv2 = VirtualGatherV2()
self.gatherv2 = P.GatherV2()
self.index = Tensor(np.ones(shape), dtype=ms.int32)
self.axis = axis



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