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remove control_depend from py file

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pull/13769/head
huangbingjian 4 years ago
parent b5af96af76
commit 72ae1799f3
12 changed files with 39 additions and 149 deletions
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1
mindspore/_extends/graph_kernel/model/model.py
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@ -177,7 +177,6 @@ class PrimLib:
'ReduceMax': Prim(REDUCE),
'ReduceMin': Prim(REDUCE),
'MakeTuple': Prim(CONTROL),
'ControlDepend': Prim(CONTROL),
'Assign': Prim(ELEMWISE),
'Tanh': Prim(ELEMWISE),
'ExpandDims': Prim(RESHAPE),

6
mindspore/ops/_grad/grad_implementations.py
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@ -261,12 +261,6 @@ def bprop_bool_and(x, y, out, dout):
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`."""

5
mindspore/ops/functional.py
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@ -42,11 +42,6 @@ shape = P.Shape()
rank = P.Rank()
reshape = P.Reshape()
# control_depend: represent dependency between two operators
def control_depend(src, dst):
control_depend_op = P.ControlDepend()
return control_depend_op(src, dst)
merge = P.Merge()
geswitch = P.GeSwitch()
addn = P.AddN()

3
mindspore/ops/operations/__init__.py
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@ -40,7 +40,7 @@ from .comm_ops import (AllGather, AllReduce, _AlltoAll, AllSwap, ReduceScatter,
_HostAllGather, _HostReduceScatter)
from .debug_ops import (ImageSummary, InsertGradientOf, HookBackward, ScalarSummary,
TensorSummary, HistogramSummary, Print, Assert)
from .control_ops import ControlDepend, GeSwitch, Merge
from .control_ops import GeSwitch, Merge
from .inner_ops import (ScalarCast, Randperm, NoRepeatNGram, LambApplyOptimizerAssign, LambApplyWeightAssign, MakeRefKey,
FusedWeightScaleApplyMomentum)
@ -277,7 +277,6 @@ __all__ = [
'ScalarToArray',
'ScalarToTensor',
'TupleToArray',
'ControlDepend',
'GeSwitch',
'Merge',
'SameTypeShape',

69
mindspore/ops/operations/control_ops.py
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@ -14,76 +14,9 @@
# ============================================================================
"""control_ops"""
from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register
from ..._checkparam import Rel
from ..primitive import PrimitiveWithInfer, prim_attr_register
from ..._checkparam import Validator as validator
from ...common import dtype as mstype
from ...common._decorator import deprecated
class ControlDepend(Primitive):
"""
Adds control dependency relation between source and destination operations.
In many cases, we need to control the execution order of operations. ControlDepend is designed for this.
ControlDepend will instruct the execution engine to run the operations in a specific order. ControlDepend
tells the engine that the destination operations must depend on the source operation which means the source
operations must be executed before the destination.
Note:
This operation does not work in `PYNATIVE_MODE`.
`ControlDepend` is deprecated from version 1.1 and will be removed in a future version, use `Depend` instead.
Args:
depend_mode (int): Use 0 for a normal dependency relation and 1 for a user-defined dependency relation.
Default: 0.
Inputs:
- **src** (Any) - The source input. It can be a tuple of operations output or a single operation output. We do
not concern about the input data, but concern about the operation that generates the input data.
If `depend_mode` is 1 and the source input is Parameter, we will try to find the operations that
used the parameter as input.
- **dst** (Any) - The destination input. It can be a tuple of operations output or a single operation output.
We do not concern about the input data, but concern about the operation that generates the input data.
If `depend_mode` is 1 and the source input is Parameter, we will try to find the operations that
used the parameter as input.
Outputs:
This operation has no actual data output, it will be used to setup the order of relative operations.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.control_depend = P.ControlDepend()
... self.softmax = ops.Softmax()
...
... def construct(self, x, y):
... mul = x * y
... softmax = self.softmax(x)
... ret = self.control_depend(mul, softmax)
... return ret
...
>>> x = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
>>> y = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
>>> net = Net()
>>> output = net(x, y)
>>> print(output)
[[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]]
"""
@deprecated("1.1", "Depend")
@prim_attr_register
def __init__(self, depend_mode=0):
"""init"""
validator.check_int_range(depend_mode, 0, 1, Rel.INC_BOTH, "depend_mode", self.name)
def __call__(self, src, dst):
return src
class GeSwitch(PrimitiveWithInfer):

14
mindspore/ops/operations/other_ops.py
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@ -420,16 +420,12 @@ class Depend(Primitive):
Depend is used for processing dependency operations.
In some side-effect scenarios, we need to ensure the execution order of operators.
In order to ensure that operator A is executed before operator B, it is recommended
to insert the Depend operator between operators A and B.
In order to ensure that operator A is executed before operator B, it is recommended to
insert the Depend operator between operators A and B. The usage method is as follows::
Previously, the ControlDepend operator was used to control the execution order.
Since the ControlDepend operator is deprecated from version 1.1, it is recommended
to use the Depend operator instead. The replacement method is as follows::
a = A(x) ---> a = A(x)
b = B(y) ---> y = Depend(y, a)
ControlDepend(a, b) ---> b = B(y)
out_a = A(in_a)
in_b = Depend(in_b, out_a)
out_b = B(in_b)
Inputs:
- **value** (Tensor) - the real value to return for depend operator.

17
model_zoo/official/nlp/bert/src/adam.py
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@ -115,8 +115,8 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
op_sqrt = P.Sqrt()
scatter_add = P.ScatterAdd(use_locking)
assign_m = F.assign(m, op_mul(beta1, m))
assign_v = F.assign(v, op_mul(beta2, v))
success = F.depend(success, F.assign(m, op_mul(beta1, m)))
success = F.depend(success, F.assign(v, op_mul(beta2, v)))
grad_indices = gradient.indices
grad_value = gradient.values
@ -131,27 +131,18 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
if use_nesterov:
m_temp = next_m * _scaler_ten
assign_m_nesterov = F.assign(m, op_mul(beta1, next_m))
F.assign(m, op_mul(beta1, next_m))
div_value = scatter_add(m,
op_mul(grad_indices, _scaler_one),
op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
param_update = div_value / (op_sqrt(next_v) + eps)
m_recover = F.assign(m, m_temp / _scaler_ten)
F.control_depend(m_temp, assign_m_nesterov)
F.control_depend(assign_m_nesterov, div_value)
F.control_depend(param_update, m_recover)
F.assign(m, m_temp / _scaler_ten)
else:
param_update = next_m / (op_sqrt(next_v) + eps)
lr_t = lr * op_sqrt(1 - beta2_power) / (1 - beta1_power)
next_param = param - lr_t * param_update
F.control_depend(assign_m, next_m)
F.control_depend(assign_v, next_v)
success = F.depend(success, F.assign(param, next_param))
success = F.depend(success, F.assign(m, next_m))
success = F.depend(success, F.assign(v, next_v))

1
model_zoo/official/nlp/gru/src/gru_for_train.py
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@ -172,7 +172,6 @@ class GRUTrainOneStepWithLossScaleCell(nn.Cell):
self.get_status = P.NPUGetFloatStatus()
self.clear_before_grad = P.NPUClearFloatStatus()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.depend_parameter_use = P.ControlDepend(depend_mode=1)
self.base = Tensor(1, mstype.float32)
self.less_equal = P.LessEqual()
self.hyper_map = C.HyperMap()

5
model_zoo/research/cv/FaceDetection/src/network_define.py
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@ -17,7 +17,7 @@ import numpy as np
import mindspore.nn as nn
from mindspore.ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, \
LessEqual, ControlDepend
LessEqual
from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_gradients_mean
from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
from mindspore import Tensor
@ -25,7 +25,7 @@ from mindspore.context import ParallelMode
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.common.parameter import ParameterTuple
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common import dtype as mstype
@ -69,7 +69,6 @@ class TrainOneStepWithLossScaleCell(nn.Cell):
self.base = Tensor(1, mstype.float32)
self.reducer_flag = False
self.less_equal = LessEqual()
self.depend_parameter_use = ControlDepend(depend_mode=1)
self.allreduce = P.AllReduce()
self.parallel_mode = _get_parallel_mode()
self.grad_reducer = None

54
model_zoo/research/nlp/ternarybert/src/cell_wrapper.py
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@ -341,7 +341,6 @@ class BertTrainWithLossScaleCell(nn.Cell):
self.get_status = P.NPUGetFloatStatus()
self.clear_before_grad = P.NPUClearFloatStatus()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.depend_parameter_use = P.ControlDepend(depend_mode=1)
self.base = Tensor(1, mstype.float32)
self.less_equal = P.LessEqual()
self.hyper_map = C.HyperMap()
@ -381,24 +380,24 @@ class BertTrainWithLossScaleCell(nn.Cell):
saved = ()
for i in range(self.length):
saved = saved + (F.assign(self.saved_params[i], weights[i]),)
assign_embedding = ()
for i in range(self.quant_embedding_list_length):
quant_embedding = self.quantize_embedding(weights[self.quant_embedding_list[i]])
assign_embedding = assign_embedding + (F.assign(weights[self.quant_embedding_list[i]], quant_embedding),)
F.control_depend(saved, assign_embedding[i])
assign_weight = ()
quant_embedding = F.depend(quant_embedding, saved)
assign_embedding = F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
input_ids = F.depend(input_ids, assign_embedding)
for i in range(self.quant_weight_list_length):
quant_weight = self.quantize_weight(weights[self.quant_weight_list[i]])
assign_weight = assign_weight + (F.assign(weights[self.quant_weight_list[i]], quant_weight),)
F.control_depend(saved, assign_weight[i])
for i in range(self.quant_embedding_list_length):
F.control_depend(assign_embedding[i], input_ids)
for i in range(self.quant_weight_list_length):
F.control_depend(assign_weight[i], input_ids)
quant_weight = F.depend(quant_weight, saved)
assign_weight = F.assign(weights[self.quant_weight_list[i]], quant_weight)
input_ids = F.depend(input_ids, assign_weight)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
# alloc status and clear should be right before grad operation
init = self.alloc_status()
self.clear_before_grad(init)
@ -408,15 +407,15 @@ class BertTrainWithLossScaleCell(nn.Cell):
label_ids,
self.cast(scaling_sens,
mstype.float32))
F.control_depend(input_ids, grads)
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
grads = self.hyper_map(F.partial(clip_grad, self.clip_type, self.clip_value), grads)
restore = ()
for i in range(self.length):
weights[i] = F.depend(weights[i], grads)
restore = restore + (F.assign(weights[i], self.saved_params[i]),)
F.control_depend(grads, restore[i])
self.get_status(init)
flag_sum = self.reduce_sum(init, (0,))
if self.is_distributed:
@ -432,8 +431,7 @@ class BertTrainWithLossScaleCell(nn.Cell):
succ = False
else:
succ = self.optimizer(grads)
for i in range(self.length):
F.control_depend(restore[i], succ)
succ = F.depend(succ, restore)
return succ
@ -495,35 +493,33 @@ class BertTrainCell(nn.Cell):
saved = ()
for i in range(self.length):
saved = saved + (F.assign(self.saved_params[i], weights[i]),)
assign_embedding = ()
for i in range(self.quant_embedding_list_length):
quant_embedding = self.quantize_embedding(weights[self.quant_embedding_list[i]])
assign_embedding = assign_embedding + (F.assign(weights[self.quant_embedding_list[i]], quant_embedding),)
F.control_depend(saved, assign_embedding[i])
assign_weight = ()
quant_embedding = F.depend(quant_embedding, saved)
assign_embedding = F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
input_ids = F.depend(input_ids, assign_embedding)
for i in range(self.quant_weight_list_length):
quant_weight = self.quantize_weight(weights[self.quant_weight_list[i]])
assign_weight = assign_weight + (F.assign(weights[self.quant_weight_list[i]], quant_weight),)
F.control_depend(saved, assign_weight[i])
for i in range(self.quant_embedding_list_length):
F.control_depend(assign_embedding[i], input_ids)
for i in range(self.quant_weight_list_length):
F.control_depend(assign_weight[i], input_ids)
quant_weight = F.depend(quant_weight, saved)
assign_weight = F.assign(weights[self.quant_weight_list[i]], quant_weight)
input_ids = F.depend(input_ids, assign_weight)
grads = self.grad(self.network, weights)(input_ids,
input_mask,
token_type_id,
label_ids,
self.cast(F.tuple_to_array((self.sens,)),
mstype.float32))
F.control_depend(input_ids, grads)
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(F.partial(clip_grad, self.clip_type, self.clip_value), grads)
restore = ()
for i in range(self.length):
weights[i] = F.depend(weights[i], grads)
restore = restore + (F.assign(weights[i], self.saved_params[i]),)
F.control_depend(grads, restore[i])
succ = self.optimizer(grads)
for i in range(self.length):
F.control_depend(restore[i], succ)
succ = F.depend(succ, restore)
return succ

4
tests/st/auto_monad/test_auto_monad_gpu.py
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@ -399,7 +399,6 @@ class MixControlNet(Cell):
kernel_size=1, stride=1, has_bias=False,
weight_init='ones', pad_mode='same')
self.bn = BatchNorm2d(num_features=in_channel)
self.controldepend = P.ControlDepend()
self.assignadd = P.AssignAdd()
self.assign = P.Assign()
self.relu = ReLU()
@ -428,9 +427,8 @@ class MixControlNet(Cell):
if x < 20:
out = self.biasadd(out, self.bias)
if x % 2 == 0:
self.assignadd(self.bias, self.value)
out = self.biasadd(out, self.bias)
assign = self.assignadd(self.bias, self.value)
self.controldepend(assign, out)
out = self.bn(out)
else:
out = self.conv(out)

9
tests/st/graph_kernel/model/test_graph_parallel.py
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@ -33,14 +33,6 @@ def reduce_graph(shape, reduce_axis):
gb.emit('ReduceSum', a3, 'C', attrs={'reduce_axis': reduce_axis})
return gb.get()[0]
def control_graph(shape):
gb = model.GraphBuilder()
with gb.graph_scope('control') as _:
a1 = gb.tensor(shape, 'float32')
a2 = gb.emit('Abs', a1)
gb.emit('ControlDepend', a2)
return gb.get()[0]
def block_fusion(graphs):
gain = model.parallel_estimate(graphs)
print("fusion = {}, bottleneck = {}, gain = {}".format(gain.fusion_type, gain.bottleneck, gain.gain))
@ -51,4 +43,3 @@ if __name__ == "__main__":
assert block_fusion([reduce_graph([1024, 1024], [1]), injective_graph([24, 1024])])
assert not block_fusion([reduce_graph([1024, 1024], [1]), injective_graph([50, 1024])])
assert not block_fusion([reduce_graph([1024, 1024], [0, 1]), injective_graph([1024, 1024])])
assert block_fusion([control_graph([20, 128]), injective_graph([40, 1024])])

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