forked from mindspore-Ecosystem/mindspore
!45735 Add bprop mindir for some array ops
Merge pull request !45735 from YuJianfeng/master
This commit is contained in:
Gitee
commit
025add5df1
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@ -38,6 +38,7 @@ class GetMetaFg : public AnfVisitor {
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{"logical_not", {"logical_not", "mindspore.ops.composite.multitype_ops.logic_not_impl"}},
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{"in", {"in_", "mindspore.ops.composite.multitype_ops.in_impl"}},
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{"less", {"less", "mindspore.ops.composite.multitype_ops.less_impl"}},
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{"less_equal", {"less_equal", "mindspore.ops.composite.multitype_ops.less_equal_impl"}},
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{"greater", {"greater", "mindspore.ops.composite.multitype_ops.greater_impl"}},
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{"add", {"add", "mindspore.ops.composite.multitype_ops.add_impl"}},
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{"sub", {"sub", "mindspore.ops.composite.multitype_ops.sub_impl"}},
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@ -159,12 +159,6 @@ def bprop_embed(x, out, dout):
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return (C.zeros_like(x),)
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@bprops.register("StopGradient")
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def bprop_stop_gradient(x, out, dout):
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"""Backpropagator for primitive `StopGradient`."""
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return (C.zeros_like(x),)
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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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@ -0,0 +1,28 @@
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0.1.1 MindSpore*2.0.0:×
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"get_bprop_batch_to_space_nd.1:self*!
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get_bprop_batch_to_space_nd.1:x*#
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!get_bprop_batch_to_space_nd.1:out*$
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"get_bprop_batch_to_space_nd.1:dout2*
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m
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bprop.3:dx:6bprop.3:[CNode]8:7bprop.3:[CNode]8:7"REF::S-Prim-MakeTuple:8: Default/S-Prim-MakeTuple-op5bprop.32
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bprop.3:[CNode]8:7Pb
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getattr:3getattrb&
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S-Prim-MakeTuple:8S-Prim-MakeTupleh
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@ -0,0 +1,192 @@
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0.1.1 MindSpore*2.0.0:‹
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m#get_bprop_broadcast_to.1:[CNode]2:1#get_bprop_broadcast_to.1:[CNode]2:1" REF::bprop.3:Default/bprop.3-op0
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get_bprop_broadcast_to.1:x*
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get_bprop_broadcast_to.1:out*
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get_bprop_broadcast_to.1:dout2%
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bprop.3:[CNode]5:6
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↱bprop.8bprop.3:[CNode]9:7bprop.3:[CNode]9:7"
REF::Switch:8:Default/Switch-op4
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bprop.3:[CNode]11:11
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↰bprop.12
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↱bprop.13bprop.3:[CNode]14:12bprop.3:[CNode]14:12"
REF::Switch:8:Default/Switch-op8
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✓bprop.17
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REF::Switch:8:Default/Switch-op11
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}
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get_bprop_broadcast_to.1:doutbprop.3:dout_shape:66bprop.3:dout_shape:66"REF::S-Prim-Shape:3:Default/S-Prim-Shape-op49bprop.32
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↓bprop.22:[CNode]28:22
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✗↓bprop.30:reduction_axes:37✗↓bprop.30:[CNode]40:38✗↓bprop.30:[CNode]40:38"REF::S-Prim-MakeTuple:39: Default/S-Prim-MakeTuple-op30
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↰bprop.12:[CNode]62:70Pb
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Switch:8Switchb
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getattr:63getattrb
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resolve:31resolveb
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S-Prim-Shape:3 S-Prim-Shapeb'
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S-Prim-MakeTuple:69S-Prim-MakeTupleb'
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S-Prim-MakeTuple:27S-Prim-MakeTupleb'
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S-Prim-MakeTuple:52S-Prim-MakeTuplebŽ
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S-Prim-is_shape_unknown:5S-Prim-is_shape_unknown1
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constexpr_name*is_shape_unknown€b'
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S-Prim-MakeTuple:39S-Prim-MakeTupleb'
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S-Prim-make_dict:45S-Prim-make_dictb'
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S-Prim-make_dict:54S-Prim-make_dictbm
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S-Prim-TensorShape:34S-Prim-TensorShape
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output_names€Š Zoutput€
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input_names€Š Zinput_x€b?
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S-Prim-BroadcastGradientArgs:58 S-Prim-BroadcastGradientArgsb'
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S-Prim-MakeTuple:41S-Prim-MakeTupleb'
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S-Prim-MakeTuple:43S-Prim-MakeTupleb'
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S-Prim-MakeTuple:50S-Prim-MakeTupleh
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@ -0,0 +1,23 @@
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0.1.1 MindSpore*2.0.0:í
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q%get_bprop_depth_to_space.1:[CNode]2:1%get_bprop_depth_to_space.1:[CNode]2:1" REF::bprop.3:Default/bprop.3-op0
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%get_bprop_depth_to_space.1:[CNode]5:2 get_bprop_depth_to_space.1:op:4 get_bprop_depth_to_space.1:op:4"?REF::ClassType::mindspore.ops.operations.array_ops.SpaceToDepth:CDefault/class 'mindspore.ops.operations.array_ops.SpaceToDepth'-op2get_bprop_depth_to_space.1*!
|
||||
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||||
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|
||||
get_bprop_trans_shape.1:x*
|
||||
get_bprop_trans_shape.1:shape*
|
||||
get_bprop_trans_shape.1:out*
|
||||
get_bprop_trans_shape.1:dout2$
|
||||
"get_bprop_trans_shape.1:[CNode]2:1:@adaf31d907574c3f63f9202dc6a36f2f70f0e7caf15e7294fa4a4f2427bc8d02J/grad_array_ops.pyB¶
|
||||
r
|
||||
get_bprop_trans_shape.1:xbprop.3:[CNode]4:3bprop.3:[CNode]4:3"REF::S-Prim-Shape:4:Default/S-Prim-Shape-op2
|
||||
t
|
||||
get_bprop_trans_shape.1:dout
|
||||
bprop.3:[CNode]4:3 bprop.3:dx:5 bprop.3:dx:5"!REF::get_bprop_trans_shape.1:op:2:3
|
||||
¦
|
||||
get_bprop_trans_shape.1:shapebprop.3:[CNode]5:6bprop.3:[CNode]5:6".REF::MetaFuncGraph::hyper_map[zeros_like_leaf]:-Default/S-Prim-hyper_map[zeros_like_leaf]-op4
|
||||
<EFBFBD>
|
||||
bprop.3:dx:5
|
||||
bprop.3:[CNode]5:6bprop.3:[CNode]6:7bprop.3:[CNode]6:7"REF::S-Prim-MakeTuple:8: Default/S-Prim-MakeTuple-op5bprop.32
|
||||
bprop.3:[CNode]6:7Pb
|
||||
S-Prim-Shape:4 S-Prim-Shapeb&
|
||||
S-Prim-MakeTuple:8S-Prim-MakeTupleh
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
|
||||
0.1.1 MindSpore*2.0.0:<3A>
|
||||
a get_bprop_unique.1:[CNode]2:1 get_bprop_unique.1:[CNode]2:1" REF::bprop.3:Default/bprop.3-op0
|
||||
´get_bprop_unique.1:op:2get_bprop_unique.1:op:2"=REF::ClassType::mindspore.ops.operations._grad_ops.UniqueGrad:ADefault/class 'mindspore.ops.operations._grad_ops.UniqueGrad'-op1get_bprop_unique.1*
|
||||
get_bprop_unique.1:self*
|
||||
get_bprop_unique.1:x*
|
||||
get_bprop_unique.1:out*
|
||||
get_bprop_unique.1:dout2
|
||||
get_bprop_unique.1:[CNode]2:1:@250b2c34ad30cb5948758d14a57231a58534cf4e271d6445ab7a48ae067de84dJ/grad_array_ops.pyBþ
|
||||
n
|
||||
get_bprop_unique.1:dout
|
||||
get_bprop_unique.1:out bprop.3:dx:3 bprop.3:dx:3" REF::get_bprop_unique.1:op:2:2
|
||||
m
|
||||
bprop.3:dx:3bprop.3:[CNode]4:4bprop.3:[CNode]4:4"REF::S-Prim-MakeTuple:5: Default/S-Prim-MakeTuple-op3bprop.32
|
||||
bprop.3:[CNode]4:4Pb&
|
||||
S-Prim-MakeTuple:5S-Prim-MakeTupleh
|
||||
|
|
@ -20,7 +20,6 @@ import numpy as np
|
|||
import mindspore.nn as nn
|
||||
from mindspore import Tensor, Parameter
|
||||
from mindspore.ops import operations as P
|
||||
import mindspore.ops.functional as F
|
||||
import mindspore.ops as ops
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.common.initializer import initializer
|
||||
|
|
@ -480,26 +479,6 @@ def test_depend():
|
|||
grad.compile(x, y)
|
||||
|
||||
|
||||
def test_stop_gradient():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the stop_gradient op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
|
||||
class StopGradientNet(nn.Cell):
|
||||
def construct(self, x, y):
|
||||
c = x * y
|
||||
c_s = F.stop_gradient(c)
|
||||
return c_s
|
||||
|
||||
x = Tensor(np.ones([4, 5]), mstype.float32)
|
||||
y = Tensor(np.ones([4, 5]), mstype.float32)
|
||||
stop_gradient = StopGradientNet()
|
||||
grad = GradNet(stop_gradient)
|
||||
grad.compile(x, y)
|
||||
|
||||
|
||||
def test_switch():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
|
|
@ -890,3 +869,260 @@ def test_strided_slice_grad():
|
|||
grad = GradNet(strided_slice)
|
||||
second_grad = GradNet(grad)
|
||||
second_grad.compile(x, (1, 0, 2), (3, 1, 3), (1, 1, 1))
|
||||
|
||||
|
||||
def test_sparse_gather_v2():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the sparse_gather_v2 op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mstype.float32)
|
||||
input_indices = Tensor(np.array([1, 2]), mstype.int32)
|
||||
axis = 1
|
||||
sparse_gather_v2 = Net(P.SparseGatherV2())
|
||||
grad = GradNet(sparse_gather_v2)
|
||||
grad.compile(input_params, input_indices, axis)
|
||||
|
||||
|
||||
def test_resize_nearest_neighbor():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the resize_nearest_neighbor op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.array([[[[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]]]), mstype.float32)
|
||||
resize_nearest_neighbor = Net(P.ResizeNearestNeighbor((2, 2)))
|
||||
grad = GradNet(resize_nearest_neighbor)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_gather_nd():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the gather_nd op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
gather_nd = Net(P.GatherNd())
|
||||
grad = GradNet(gather_nd)
|
||||
grad.compile(x, indices)
|
||||
|
||||
|
||||
def test_scatter_nd():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the scatter_nd op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
indices = Tensor(np.array([[0], [2]]), mstype.int32)
|
||||
updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2],
|
||||
[3, 3, 3, 3], [4, 4, 4, 4]],
|
||||
[[1, 1, 1, 1], [2, 2, 2, 2],
|
||||
[3, 3, 3, 3], [4, 4, 4, 4]]]), mstype.float32)
|
||||
shape = (4, 4, 4)
|
||||
scatter_nd = Net(P.ScatterNd())
|
||||
grad = GradNet(scatter_nd)
|
||||
grad.compile(indices, updates, shape)
|
||||
|
||||
|
||||
def test_scatter_nd_update():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the scatter_nd_update op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
np_x = np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]])
|
||||
input_x = Parameter(Tensor(np_x, mstype.float32), name="x")
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
updates = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
scatter_nd_update = Net(P.ScatterNdUpdate())
|
||||
grad = GradNet(scatter_nd_update)
|
||||
grad.compile(input_x, indices, updates)
|
||||
|
||||
|
||||
def test_scatter_non_aliasing_add():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the scatter_non_aliasing_add op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mstype.float32), name="x")
|
||||
indices = Tensor(np.array([[2], [4], [1], [7]]), mstype.int32)
|
||||
updates = Tensor(np.array([6, 7, 8, 9]), mstype.float32)
|
||||
scatter_non_aliasing_add = Net(P.ScatterNonAliasingAdd())
|
||||
grad = GradNet(scatter_non_aliasing_add)
|
||||
grad.compile(input_x, indices, updates)
|
||||
|
||||
|
||||
def test_tensor_scatter_update():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the tensor_scatter_update op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
update = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
tensor_scatter_update = Net(P.TensorScatterUpdate())
|
||||
grad = GradNet(tensor_scatter_update)
|
||||
grad.compile(input_x, indices, update)
|
||||
|
||||
|
||||
def test_tensor_scatter_add():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the tensor_scatter_add op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 0], [0, 0]]), mstype.int32)
|
||||
updates = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
tensor_scatter_add = Net(P.TensorScatterAdd())
|
||||
grad = GradNet(tensor_scatter_add)
|
||||
grad.compile(input_x, indices, updates)
|
||||
|
||||
|
||||
def test_space_to_depth():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the space_to_depth op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.random.rand(1, 3, 2, 2), mstype.float32)
|
||||
block_size = 2
|
||||
space_to_depth = Net(P.SpaceToDepth(block_size))
|
||||
grad = GradNet(space_to_depth)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_depth_to_space():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the depth_to_space op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.random.rand(1, 12, 1, 1), mstype.float32)
|
||||
block_size = 2
|
||||
depth_to_space = Net(P.DepthToSpace(block_size))
|
||||
grad = GradNet(depth_to_space)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_diag_part():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the diag_part op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor([[1, 0, 0, 0],
|
||||
[0, 2, 0, 0],
|
||||
[0, 0, 3, 0],
|
||||
[0, 0, 0, 4]])
|
||||
diag_part = Net(P.DiagPart())
|
||||
grad = GradNet(diag_part)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_space_to_batch_nd():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the space_to_batch_nd op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
block_size = 2
|
||||
paddings = [[0, 0], [0, 0]]
|
||||
x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32)
|
||||
space_to_batch_nd = Net(P.SpaceToBatchND(block_size, paddings))
|
||||
grad = GradNet(space_to_batch_nd)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_batch_to_space_nd():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the batch_to_space_nd op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
block_size = 2
|
||||
crops = [[0, 0], [0, 0]]
|
||||
x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32)
|
||||
batch_to_space_nd = Net(P.BatchToSpaceND(block_size, crops))
|
||||
grad = GradNet(batch_to_space_nd)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_broadcast_to():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the broadcast_to op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
shape = (2, 3)
|
||||
x = Tensor(np.array([1, 2, 3]).astype(np.float32))
|
||||
broadcast_to = Net(P.BroadcastTo(shape))
|
||||
grad = GradNet(broadcast_to)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_reverse_sequence():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the reverse_sequence op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mstype.float32)
|
||||
seq_lengths = Tensor(np.array([1, 2, 3]))
|
||||
reverse_sequence = Net(P.ReverseSequence(seq_dim=1))
|
||||
grad = GradNet(reverse_sequence)
|
||||
grad.compile(x, seq_lengths)
|
||||
|
||||
|
||||
def test_trans_shape():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the trans_shape op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
shape = (3, 1)
|
||||
x = Tensor(np.array([1, 2, 3]).astype(np.float32))
|
||||
trans_shape = Net(P.TransShape())
|
||||
grad = GradNet(trans_shape)
|
||||
grad.compile(x, shape)
|
||||
|
||||
|
||||
def test_trans_unique():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the unique op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.array([1, 2, 5, 2]), mstype.int32)
|
||||
unique = Net(P.Unique())
|
||||
grad = GradNet(unique)
|
||||
grad.compile(x)
|
||||
|
||||
|
||||
def test_masked_select():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the masked_select op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.array([1, 2, 3, 4]), mstype.int32)
|
||||
mask = Tensor(np.array([1, 0, 1, 0]), mstype.bool_)
|
||||
masked_select = Net(P.MaskedSelect())
|
||||
grad = GradNet(masked_select)
|
||||
grad.compile(x, mask)
|
||||
|
||||
|
||||
def test_non_zero():
|
||||
"""
|
||||
Feature: Bprop pre-compilation.
|
||||
Description: Compile the backward graph for the non_zero op.
|
||||
Expectation: Load the bprop mindir successfully.
|
||||
"""
|
||||
x = Tensor(np.array([[[1, 0], [-5, 0]]]), mstype.int32)
|
||||
grad = GradNet(ops.nonzero)
|
||||
grad.compile(x)
|
||||
|
|
|
|||
Loading…
Reference in New Issue