!30776 change Dynamicshape To TensorShape

Merge pull request !30776 from lianliguang/master
This commit is contained in:
i-robot 2022-03-07 10:57:29 +00:00 committed by Gitee
commit d1eec933ae
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GPG Key ID: 173E9B9CA92EEF8F
53 changed files with 418 additions and 416 deletions

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@ -794,7 +794,7 @@ def rowtensor_deduplicate_indices_slices(grad):
values = grad.values
unique_indices, index_position = P.Unique()(indices)
summed_values = P.UnsortedSegmentSum()(values, index_position, P.DynamicShape()(unique_indices)[0])
summed_values = P.UnsortedSegmentSum()(values, index_position, P.TensorShape()(unique_indices)[0])
return RowTensor(unique_indices, summed_values, grad.dense_shape)

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@ -35,7 +35,7 @@ reduce_sum = P.ReduceSum()
unsorted_segment_sum = P.UnsortedSegmentSum()
transpose = P.Transpose()
shape_op = P.Shape()
dyn_shape_op = P.DynamicShape()
dyn_shape_op = P.TensorShape()
reshape = P.Reshape()
size_op = P.Size()
invert_permutation = P.InvertPermutation()

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@ -33,7 +33,7 @@ from .._utils.utils import is_shape_unknown
from ...common import dtype as mstype
shape_op = P.Shape()
dyn_shape_op = P.DynamicShape()
dyn_shape_op = P.TensorShape()
reduce_prod = P.ReduceProd()
reduce_sum = P.ReduceSum()
reshape = P.Reshape()

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@ -53,7 +53,7 @@ def get_bprop_conv2d(self):
dilation=self.dilation, stride=self.stride, group=self.group, data_format=self.format
)
get_shape = P.Shape()
get_dyn_shape = P.DynamicShape()
get_dyn_shape = P.TensorShape()
def bprop(x, w, out, dout):
x_shape = get_shape(x)
@ -1139,7 +1139,7 @@ def get_bprop_conv2d_backprop_input(self):
dilation=self.dilation, stride=self.stride, group=self.group, data_format=self.format
)
get_shape = P.Shape()
get_dyn_shape = P.DynamicShape()
get_dyn_shape = P.TensorShape()
def bprop(x, w, f_sizes, out, dout):
w_shape = get_shape(w)

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@ -1,20 +1,20 @@
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@ -1,12 +1,14 @@
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@ -1,12 +1,14 @@
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@ -1,20 +1,20 @@
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@ -1,20 +1,20 @@
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@ -1,10 +1,12 @@
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@ -1,5 +1,5 @@
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@ -21,7 +21,7 @@ bprop.45:y*
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@ -1,5 +1,5 @@
0.1.1 MindSpore*1.7.0.20220305
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@ -2,19 +2,19 @@
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@ -2,19 +2,19 @@
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bprop.62:ybprop.62:[CNode]64:3bprop.62:[CNode]64:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op28
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bprop.62:[CNode]63:1
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bprop.91:[CNode]92:1
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bprop.39:xbprop.39:[CNode]40:1bprop.39:[CNode]40:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op19
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bprop.39:[CNode]40:1bprop.39:[CNode]41:3bprop.39:[CNode]41:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op20bprop.39*
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0.1.1 MindSpore*1.7.0:

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bprop.198:[CNode]199:1bprop.198:[CNode]200:3bprop.198:[CNode]200:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op113 bprop.198*
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0.1.1 MindSpore*1.7.0:

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bprop.133:[CNode]134:1bprop.133:[CNode]135:3bprop.133:[CNode]135:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op66 bprop.133*
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S-Prim-MakeTuple:4S-Prim-MakeTuple
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bprop.194:xbprop.194:[CNode]195:1bprop.194:[CNode]195:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:/Default/S-Prim-hyper_map[zeros_like_leaf]-op110
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bprop.77:ybprop.77:[CNode]79:3bprop.77:[CNode]79:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op37
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bprop.77:[CNode]78:1
bprop.77:[CNode]79:3bprop.77:[CNode]80:4bprop.77:[CNode]80:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op38bprop.77*
bprop.77:x*
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0.1.1 MindSpore*1.7.0:÷

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bprop.106:ybprop.106:[CNode]108:3bprop.106:[CNode]108:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op58
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bprop.106:[CNode]107:1
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bprop.72:ybprop.72:[CNode]74:3bprop.72:[CNode]74:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op34
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bprop.72:[CNode]73:1
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S-Prim-MakeTuple:5S-Prim-MakeTuple
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bprop.101:ybprop.101:[CNode]103:3bprop.101:[CNode]103:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op55
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bprop.101:[CNode]102:1
bprop.101:[CNode]103:3bprop.101:[CNode]104:4bprop.101:[CNode]104:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op56 bprop.101*
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bprop.41:stopbprop.41:[CNode]43:3bprop.41:[CNode]43:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op18
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bprop.41:numbprop.41:[CNode]44:4bprop.41:[CNode]44:4"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op19
bprop.60:numbprop.60:[CNode]63:4bprop.60:[CNode]63:4"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op32
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0.1.1 MindSpore*1.7.0.20220305
0.1.1 MindSpore*1.7.0

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bprop.16:[CNode]17:1bprop.16:[CNode]18:3bprop.16:[CNode]18:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op8bprop.16*
bprop.11:x*
bprop.11:out*
bprop.11:dout2
bprop.11:[CNode]13:2:@f89f9c80e2c70d984c3cce2cd04a572f30d2346157b99460e82f82a2468d8abfP
bprop.16:x*
bprop.16:out*
bprop.16:dout2
bprop.16:[CNode]18:3:@3e6b4b1515849c402b76774467f7d4737b4a5c7f70c42f3deb212cc538028f2bPbH
#S-Prim-hyper_map[zeros_like_leaf]:2!S-Prim-hyper_map[zeros_like_leaf]b&
S-Prim-MakeTuple:4S-Prim-MakeTuple

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@ -49,7 +49,7 @@ tile = P.Tile()
size = P.Size()
ones_like = P.OnesLike()
shape = P.Shape()
dyn_shape = P.DynamicShape()
dyn_shape = P.TensorShape()
rank = P.Rank()
reshape = P.Reshape()

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@ -9,7 +9,7 @@ context.set_context(enable_graph_kernel=False, save_graphs=False, mode=context.P
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.host = P.DynamicShape()
self.host = P.TensorShape()
self.t1 = Tensor(np.random.randn(16).astype(np.int32))
def construct(self):

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@ -31,7 +31,7 @@ class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.unique = P.Unique()
self.shape = P.DynamicShape()
self.shape = P.TensorShape()
self.reshape = P.Reshape()
self.add = P.Add()

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@ -28,7 +28,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.d_shape = ops.DynamicShape()
self.d_shape = ops.TensorShape()
self.d_broadcastto = inner.DynamicBroadcastTo()
def construct(self, data, shape):

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@ -191,7 +191,7 @@ def test_dynamic_bachnorm():
class ReshapeNet(nn.Cell):
def construct(self, x, y):
shape_of_y = ops.DynamicShape()(y)
shape_of_y = ops.TensorShape()(y)
return ops.Reshape()(x, shape_of_y)

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@ -26,7 +26,7 @@ class DynamicShapeNet(nn.Cell):
def __init__(self):
super(DynamicShapeNet, self).__init__()
self.convert_to_dynamic_shape_op = inner.GpuConvertToDynamicShape()
self.dynamic_shape_op = P.DynamicShape()
self.dynamic_shape_op = P.TensorShape()
def construct(self, x):
x_dynamic_shape = self.convert_to_dynamic_shape_op(x)

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@ -104,7 +104,7 @@ def test_gatherv2():
shp = P.Shape()(self.yy)
y = P.Reshape()(y, shp)
u, _ = self.unq(y)
u_shp = P.DynamicShape()(u)
u_shp = P.TensorShape()(u)
z = self.gather(x, u, 0)
return z, u_shp
@ -123,7 +123,7 @@ def test_segmentsum():
self.sum = P.UnsortedSegmentSum()
def construct(self, x):
u, _ = self.unq(x)
shp = P.DynamicShape()(u)
shp = P.TensorShape()(u)
z = self.sum(x, self.segment_ids, shp[0])
return z, shp[0]