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Add the check of the return value of Assign, AssignAdd, AssignSub.

This commit is contained in:
Margaret_wangrui 2022-09-17 10:36:03 +08:00
parent 61d2bb4b1e
commit dd2c45a829
41 changed files with 482 additions and 284 deletions
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44
mindspore/ccsrc/pipeline/jit/validator.cc
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@ -155,19 +155,59 @@ void CheckValueTuple(const AnfNodePtr &node) {
auto value_tuple = value->cast_ptr<ValueTuple>();
MS_EXCEPTION_IF_NULL(value_tuple);
const auto &tuple_values = value_tuple->value();
for (size_t i = 0; i < tuple_values.size(); ++i) {
auto input_node = NewValueNode(tuple_values[i]);
for (const auto &tuple_value : tuple_values) {
auto input_node = NewValueNode(tuple_value);
ValidateOperation(input_node);
ValidateValueNode(input_node);
}
}
void CheckAssignReturnValue(const AnfNodePtr &node) {
static const PrimitiveSet assign_prims = {prim::kPrimAssign, prim::kPrimAssignAdd, prim::kPrimAssignSub};
if (IsPrimitiveCNode(node, prim::kPrimDepend)) {
auto real_input = node->cast_ptr<CNode>()->input(1);
while (IsPrimitiveCNode(real_input, prim::kPrimDepend)) {
real_input = real_input->cast_ptr<CNode>()->input(1);
}
if (!IsOneOfPrimitiveCNode(real_input, assign_prims)) {
return;
}
} else if (!IsOneOfPrimitiveCNode(node, assign_prims)) {
return;
}
auto fg = node->func_graph();
MS_EXCEPTION_IF_NULL(fg);
auto mgr = fg->manager();
MS_EXCEPTION_IF_NULL(mgr);
auto &node_users = mgr->node_users();
auto iter = node_users.find(node);
if (iter == node_users.end()) {
return;
}
static const PrimitiveSet virtual_prims = {
prim::kPrimImageSummary, prim::kPrimScalarSummary, prim::kPrimTensorSummary, prim::kPrimHistogramSummary,
prim::kPrimMakeTuple, prim::kPrimStateSetItem, prim::kPrimTupleGetItem, prim::kPrimLoad,
prim::kPrimPartial, prim::kPrimDepend, prim::kPrimUpdateState, prim::kPrimDynamicLossScale};
auto users = iter->second;
for (const auto &user : users) {
auto user_node = user.first;
if (!IsOneOfPrimitiveCNode(user_node, virtual_prims)) {
MS_LOG(WARNING) << "Deprecated: the return value of Assign/AssignAdd/AssignSub operator will be removed "
<< "in subsequent releases.\n"
<< "You can modify the code from:\na = P.Assign()(param, value)\nb = a * 2\nto: \n"
<< "P.Assign()(param, value)\nb = param * 2\n"
<< "Please check your code:" << trace::GetDebugInfo(node->debug_info());
}
}
}
void Validate(const FuncGraphPtr &func_graph) {
FuncGraphManagerPtr mgr = Manage(func_graph, false);
MS_EXCEPTION_IF_NULL(mgr);
const AnfNodeSet &all_nodes = mgr->all_nodes();
for (auto node : all_nodes) {
TraceGuard guard(std::make_shared<TraceCopy>(node->debug_info()));
CheckAssignReturnValue(node);
while (IsPrimitiveCNode(node, prim::kPrimReturn) || IsPrimitiveCNode(node, prim::kPrimDepend)) {
node = node->cast_ptr<CNode>()->input(1);
}

5
mindspore/python/mindspore/boost/adasum.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -37,7 +37,8 @@ def _update_parameters_after_broadcast(delta_weight, update_delta_weight, parame
shape = F.shape(delta_weight)
update_delta_weight = P.Reshape()(update_delta_weight, shape)
new_parameter = old_parameter - update_delta_weight
return P.Assign()(parameter, new_parameter)
P.Assign()(parameter, new_parameter)
return parameter
def _send_before_receive(send_part, send, recv):

5
mindspore/python/mindspore/boost/boost_cell_wrapper.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -53,7 +53,8 @@ _save_weight = C.MultitypeFuncGraph("_save_weight")
@_save_weight.register("Tensor", "Tensor")
def _save_weight_process(new_parameter, old_parameter):
return P.Assign()(new_parameter, old_parameter)
P.Assign()(new_parameter, old_parameter)
return new_parameter
_grad_scale = C.MultitypeFuncGraph("grad_scale")

5
mindspore/python/mindspore/boost/dim_reduce.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -44,7 +44,8 @@ _save_weight = C.MultitypeFuncGraph("_save_weight")
@_save_weight.register("Tensor", "Tensor")
def _save_weight_process(parameter, new_parameter):
return P.Assign()(parameter, new_parameter)
P.Assign()(parameter, new_parameter)
return parameter
_pca_projection = C.MultitypeFuncGraph("_pca_projection")

3
mindspore/python/mindspore/boost/grad_accumulation.py
View File

@ -33,7 +33,8 @@ gradient_accumulation_op = C.MultitypeFuncGraph("gradient_accumulation_op")
@gradient_accumulation_op.register("Int64", "Tensor", "Tensor")
def cumulative_grad_process(accumulation_step, cumulative_grad, grad):
"""Apply gradient accumulation to cumulative grad."""
return P.AssignAdd()(cumulative_grad, grad / accumulation_step)
P.AssignAdd()(cumulative_grad, grad / accumulation_step)
return cumulative_grad
gradient_clear_op = C.MultitypeFuncGraph("gradient_clear_op")

15
mindspore/python/mindspore/nn/optim/adafactor.py
View File

@ -82,13 +82,15 @@ def _run_opt_with_one_number(eps, clip_threshold, beta1, beta2t, weight_decay, s
exp_avg_sq_row_update = P.Mul()(exp_avg_sq_row_update, beta2t)
update_mean = reduce_mean_keep_alive(update, -1) * (1.0 - beta2t)
exp_avg_sq_row_update = P.Add()(exp_avg_sq_row_update, update_mean)
exp_avg_sq_row_update = F.assign(exp_avg_sq_row, F.cast(exp_avg_sq_row_update, F.dtype(exp_avg_sq_row)))
F.assign(exp_avg_sq_row, F.cast(exp_avg_sq_row_update, F.dtype(exp_avg_sq_row)))
exp_avg_sq_row_update = exp_avg_sq_row
exp_avg_sq_col_update = F.cast(exp_avg_sq_col, grad_dtype)
exp_avg_sq_col_update = P.Mul()(exp_avg_sq_col_update, beta2t)
update_mean = reduce_mean_keep_alive(update, -2) * (1.0 - beta2t)
exp_avg_sq_col_update = P.Add()(exp_avg_sq_col_update, update_mean)
exp_avg_sq_col_update = F.assign(exp_avg_sq_col, F.cast(exp_avg_sq_col_update, F.dtype(exp_avg_sq_col)))
F.assign(exp_avg_sq_col, F.cast(exp_avg_sq_col_update, F.dtype(exp_avg_sq_col)))
exp_avg_sq_col_update = exp_avg_sq_col
update = _approx_sq_grad(exp_avg_sq_row_update, exp_avg_sq_col_update)
update = P.Mul()(update, grad)
@ -96,7 +98,8 @@ def _run_opt_with_one_number(eps, clip_threshold, beta1, beta2t, weight_decay, s
exp_avg_sq_update = F.cast(exp_avg_sq, grad_dtype)
update = update * (1.0 - beta2t)
exp_avg_sq_update = P.Add()(P.Mul()(exp_avg_sq_update, beta2t), update)
exp_avg_sq_update = F.assign(exp_avg_sq, F.cast(exp_avg_sq_update, F.dtype(exp_avg_sq)))
F.assign(exp_avg_sq, F.cast(exp_avg_sq_update, F.dtype(exp_avg_sq)))
exp_avg_sq_update = exp_avg_sq
exp_avg_sq_update = 1.0 / P.Sqrt()(exp_avg_sq_update)
update = P.Mul()(exp_avg_sq_update, grad)
@ -109,7 +112,8 @@ def _run_opt_with_one_number(eps, clip_threshold, beta1, beta2t, weight_decay, s
if compression:
exp_avg_update = F.cast(exp_avg, grad_dtype)
exp_avg_update = P.Add()(P.Mul()(exp_avg_update, beta1), update * (1 - beta1))
update = F.assign(exp_avg, F.cast(exp_avg_update, F.dtype(exp_avg)))
F.assign(exp_avg, F.cast(exp_avg_update, F.dtype(exp_avg)))
update = exp_avg
if weight_decay_flag:
p_data_fp32_coff = p_data_fp32 * -weight_decay * learning_rate_update
@ -404,7 +408,8 @@ class AdaFactor(Optimizer):
def construct(self, gradients):
gradients = self.flatten_gradients(gradients)
lr = self.get_lr()
step = F.assign_add(self.step, 1)
F.assign_add(self.step, 1)
step = self.step
if self.scale_lr and self.relative_step:
if self.warmup_init:
min_step = 1e-6 * step

6
mindspore/python/mindspore/nn/optim/adasum.py
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@ -47,7 +47,8 @@ def _update_parameters_adasum(delta_weight, update_delta_weight, parameter, old_
shape = F.shape(delta_weight)
update_delta_weight = reshape(update_delta_weight, shape)
new_parameter = old_parameter - update_delta_weight
return P.Assign()(parameter, new_parameter)
P.Assign()(parameter, new_parameter)
return parameter
@_reshape_grads.register("Tensor", "Tensor", "Function")
@ -377,7 +378,8 @@ def _get_delta_weight_process(new_parameter, old_parameter):
@_save_weight.register("Tensor", "Tensor")
def _save_weight_process(new_parameter, old_parameter):
return P.Assign()(new_parameter, old_parameter)
P.Assign()(new_parameter, old_parameter)
return new_parameter
@_clone_weight.register("Tensor", "Tensor")

26
mindspore/python/mindspore/nn/transformer/transformer.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -1678,8 +1678,10 @@ class TransformerEncoderLayer(Cell):
if self.use_past:
# reset states, init_reset True for reuse and False for reset
key_reset = self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype)))
value_reset = self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype)))
self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype)))
key_reset = self.key_past
self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype)))
value_reset = self.value_past
# add dependency for desired execution order
input_x = F.depend(input_x, key_reset)
input_x = F.depend(input_x, value_reset)
@ -1707,8 +1709,10 @@ class TransformerEncoderLayer(Cell):
# current key and value
key_present, value_present = layer_present
# update key and value calculated this step
key_update = self.assign(self.key_past, key_present)
value_update = self.assign(self.value_past, value_present)
self.assign(self.key_past, key_present)
key_update = self.key_past
self.assign(self.value_past, value_present)
value_update = self.value_past
# add dependency for desired execution order
key_update = F.depend(key_update, key_reset)
value_update = F.depend(value_update, value_reset)
@ -2115,8 +2119,10 @@ class TransformerDecoderLayer(Cell):
value_reset = None
if self.use_past:
# reset states, init_reset True for reuse and False for reset
key_reset = self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype)))
value_reset = self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype)))
self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype)))
key_reset = self.key_past
self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype)))
value_reset = self.value_past
# add dependency for desired execution order
input_x = F.depend(input_x, key_reset)
input_x = F.depend(input_x, value_reset)
@ -2159,8 +2165,10 @@ class TransformerDecoderLayer(Cell):
# current key and value
key_present, value_present = layer_present
# update key and value calculated this step
key_update = self.assign(self.key_past, key_present)
value_update = self.assign(self.value_past, value_present)
self.assign(self.key_past, key_present)
key_update = self.key_past
self.assign(self.value_past, value_present)
value_update = self.value_past
# add dependency for desired execution order
key_update = F.depend(key_update, key_reset)
value_update = F.depend(value_update, value_reset)

13
mindspore/python/mindspore/ops/_grad/grad_comm_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -151,8 +151,7 @@ def get_bprop_virtual_assign_add(self):
def bprop(x, y, out, dout):
if reduce_scatter:
dout = reduce_scatter(dout)
temp = assign_add(y, dout)
return F.depend((cast(out_tensor, dtype(x)), cast(out_tensor, dtype(y))), temp)
return F.depend((cast(out_tensor, dtype(x)), cast(out_tensor, dtype(y))), assign_add(y, dout))
return bprop
@ -208,12 +207,14 @@ def get_bprop_mirror_micro_step_operator(self):
z = F.depend(z, dout)
real_grad = all_reduce(z)
real_grad = F.tensor_mul(real_grad, scale)
assign_out = assign(z, real_grad)
assign(z, real_grad)
assign_out = z
else:
if F.issubclass_(F.typeof(dout), mstype.tensor):
z = F.depend(z, dout)
real_grad = all_reduce(z)
assign_out = assign(z, real_grad)
assign(z, real_grad)
assign_out = z
if opt_shard:
return (real_grad, cast(out_tensor, dtype(z)))
return F.depend((cast(out_tensor, dtype(x)), cast(out_tensor, dtype(z))), assign_out)
@ -284,7 +285,7 @@ def get_bprop_mini_step_all_gather(self):
if mean_flag:
dx = F.tensor_mul(dx, scale)
if add_accu:
z = assign_add(z, dx)
assign_add(z, dx)
dx = F.depend(dx, z)
else:
dx = dout

15
mindspore/python/mindspore/ops/bprop_mindir/Broadcast_bprop.mindir
View File

@ -1,10 +1,9 @@
0.1.1 MindSpore*1.9.0:ˆ
s
bprop.22:doutbprop.22:[CNode]23:1bprop.22:[CNode]23:1"REF::S-Prim-MakeTuple:2:Default/S-Prim-MakeTuple-op15bprop.22*
bprop.22:x*
bprop.22:out*
bprop.22:dout2
bprop.22:[CNode]23:1:@d656093b51d0012f58d8fd8c87351fc64fdc19fa3aa49c67d08964bd3af12156Pb&
0.1.1 MindSpore*2.0.0:ü
m
bprop.1:doutbprop.1:[CNode]2:1bprop.1:[CNode]2:1"REF::S-Prim-MakeTuple:2:Default/S-Prim-MakeTuple-op0bprop.1*
bprop.1:x*
bprop.1:out*
bprop.1:dout2
bprop.1:[CNode]2:1:@b347c311cc2bda87367b6fcbdcf96b857d6d2e109f47aed3df832027dc31a978Pb&
S-Prim-MakeTuple:2S-Prim-MakeTupleh

17
mindspore/python/mindspore/ops/function/parameter_func.py
View File

@ -18,8 +18,9 @@
from mindspore.ops import operations as P
from .._primitive_cache import _get_cache_prim
assign_ = P.Assign()
def assign(variable, value):
"""
Assigns `Parameter` with a value.
@ -48,14 +49,16 @@ def assign(variable, value):
Examples:
>>> value = Tensor([2.0], mindspore.float32)
>>> variable = mindspore.Parameter(Tensor([1.0], mindspore.float32), name="variable")
>>> output = ops.assign(variable, value)
>>> print(output)
>>> ops.assign(variable, value)
>>> print(variable)
[2.]
"""
return assign_(variable, value)
assign_sub_ = P.AssignSub()
def assign_sub(variable, value):
"""
Updates a `Parameter` by subtracting a value from it.
@ -93,7 +96,7 @@ def assign_sub(variable, value):
Examples:
>>> variable = mindspore.Parameter(initializer(1, [1], mindspore.int32), name="global_step")
>>> value = Tensor(np.ones([1]).astype(np.int32) * 100)
>>> output = ops.assign_sub(variable, value)
>>> ops.assign_sub(variable, value)
>>> print(variable.asnumpy())
[-99]
"""
@ -101,6 +104,8 @@ def assign_sub(variable, value):
assign_add_ = P.AssignAdd()
def assign_add(variable, value):
"""
Updates a `Parameter` by adding a value to it.
@ -138,7 +143,7 @@ def assign_add(variable, value):
Examples:
>>> variable = mindspore.Parameter(initializer(1, [1], mindspore.int32), name="global_step")
>>> value = Tensor(np.ones([1]).astype(np.int32) * 100)
>>> output = ops.assign_add(variable, value)
>>> ops.assign_add(variable, value)
>>> print(variable.asnumpy())
[101]
"""
@ -186,7 +191,7 @@ def index_add(x, indices, y, axis, use_lock=True, check_index_bound=True):
>>> x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), name="name_x")
>>> indices = Tensor(np.array([0, 2]), mindspore.int32)
>>> y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
>>> output = ops.index_add(x, indices, y, 1)
>>> ops.index_add(x, indices, y, 1)
>>> print(output)
[[ 1.5 2. 4. ]
[ 5. 5. 7.5]

4
mindspore/python/mindspore/ops/operations/other_ops.py
View File

@ -36,8 +36,8 @@ class Assign(Primitive):
>>> value = Tensor([2.0], mindspore.float32)
>>> variable = mindspore.Parameter(Tensor([1.0], mindspore.float32), name="variable")
>>> assign = ops.Assign()
>>> output = assign(variable, value)
>>> print(output)
>>> assign(variable, value)
>>> print(variable)
[2.]
"""
__mindspore_signature__ = (

18
tests/st/auto_monad/test_auto_monad.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -709,10 +709,10 @@ def test_multi_assign():
self.para3 = Parameter(Tensor(3, dtype=ms.int32), name='para3')
def construct(self, x, y, z):
a = self.assign(self.para1, x)
a = self.assign(self.para2, y)
a = self.assign(self.para3, z)
return self.para1 + self.para2 + a
self.assign(self.para1, x)
self.assign(self.para2, y)
self.assign(self.para3, z)
return self.para1 + self.para2 + self.para3
x = Tensor(4, dtype=ms.int32)
y = Tensor(5, dtype=ms.int32)
@ -1275,11 +1275,12 @@ class AssignNet(Cell):
1, [1, 3, 2, 2], ms.float32), name='value')
def construct(self, x):
x = self.assign_sub(self.input_data, x)
x = self.relu(x)
self.assign_sub(self.input_data, x)
x = self.relu(self.input_data)
x = self.mean(x, (2, 3))
return x
@security_off_wrap
def test_auto_mixed_precision_train_1(pynative_save_graphs):
net = AssignNet()
@ -1287,6 +1288,7 @@ def test_auto_mixed_precision_train_1(pynative_save_graphs):
label32 = Tensor(np.zeros([1, 3]).astype(np.float32))
use_build_train_network_check_cast_num(net, "O0", input32, label32, 0)
@security_off_wrap
def test_auto_mixed_precision_train_2(pynative_save_graphs):
net = AssignNet()
@ -1364,6 +1366,7 @@ def use_build_train_network_controlflow_check_cast_num(network, level, input_x,
assert len(castnum) == cast_num
return out_me
@security_off_wrap
def test_auto_mixed_precision_controlflow_auto(pynative_save_graphs):
net = MixControlNet(3, 5)
@ -1527,6 +1530,7 @@ def test_print_assign_print():
Description: Test load eliminate when umonad and iomona both exist.
Expectation: No exception.
"""
class Print(Cell):
def __init__(self):
super().__init__()

19
tests/st/auto_monad/test_auto_monad_gpu.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -82,11 +82,11 @@ def _count_unequal_element(data_expected, data_me, rtol, atol):
assert data_expected.shape == data_me.shape
total_count = len(data_expected.flatten())
error = np.abs(data_expected - data_me)
greater = np.greater(error, atol + np.abs(data_me)*rtol)
greater = np.greater(error, atol + np.abs(data_me) * rtol)
loss_count = np.count_nonzero(greater)
assert (loss_count/total_count) < rtol, \
"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}".\
format(data_expected[greater], data_me[greater], error[greater])
assert (loss_count / total_count) < rtol, \
"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
format(data_expected[greater], data_me[greater], error[greater])
def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
@ -130,6 +130,7 @@ class SideEffectCastAll(Cell):
out_b = self.cast(self.parameter_b, self.dtype)
return out_a, out_b
@security_off_wrap
def test_side_effect_castall():
clear_files()
@ -335,6 +336,7 @@ class InplaceNet(Cell):
output = self.add(tmp_c1, tmp_c2)
return output
@security_off_wrap
def test_ir_fusion_inplace_bn_conv_conv():
clear_files()
@ -393,7 +395,7 @@ class Add(Cell):
class MixControlNet(Cell):
def __init__(self, in_channel, x):
super().__init__()
#self._save_graphs(save_graph_flag=True, save_graph_path=".")
# self._save_graphs(save_graph_flag=True, save_graph_path=".")
self.biasadd = P.BiasAdd()
self.equal = P.Equal()
self.addn = P.AddN()
@ -460,6 +462,7 @@ def use_build_train_network_controlflow_check_cast_num(network, level, input_x,
assert len(castnum) == cast_num
return out_me
@security_off_wrap
def test_auto_mixed_precision_controlflow_auto():
context.set_context(mode=context.PYNATIVE_MODE, save_graphs=True)
@ -474,6 +477,7 @@ def test_auto_mixed_precision_controlflow_auto():
use_build_train_network_controlflow_check_cast_num(net, "auto", input_x,
label, cast_num)
@security_off_wrap
def test_updatestate_between_assigns():
class UpdateState_Assigns(Cell):
@ -499,6 +503,7 @@ def test_updatestate_between_assigns():
updatestate_num = re.findall('UpdateState', content)
assert len(updatestate_num) == 1
@security_off_wrap
def test_updatestate_between_maketuple_assign():
class UpdateState_MakeTuple_Assign(Cell):
@ -526,6 +531,7 @@ def test_updatestate_between_maketuple_assign():
updatestate_num = re.findall('UpdateState', content)
assert len(updatestate_num) == 1
@security_off_wrap
def test_updatestate_between_assign_maketuple():
class UpdateState_Assign_MakeTuple(Cell):
@ -563,6 +569,7 @@ def test_cycle_parameter_binding():
Description: Auto-monad should work properly when cycle parameter binding existed.
Expectation: Normal output, no core dump.
"""
class MyActor(Cell):
def construct(self, inputs):
return inputs

27
tests/st/auto_monad/test_auto_monad_mindtester.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -266,7 +266,8 @@ class ControlOneIfOneParaOneAddn(Cell):
else:
out = self.addn([input_data, input_data, input_data])
if x > y:
out = self.assign(self.inputdata, input_data)
self.assign(self.inputdata, input_data)
out = self.inputdata
return out
@ -368,7 +369,8 @@ class SideEffectReturnParameterNet(Cell):
self.relu = P.ReLU()
def construct(self, inputs):
p1 = self.assign(self.para, inputs)
self.assign(self.para, inputs)
p1 = self.para
out = self.addn((inputs, inputs, inputs))
out = self.relu(out)
return p1
@ -401,7 +403,8 @@ class SideEffectAssignAddnReluReturnParNet(Cell):
self.relu = P.ReLU()
def construct(self, inputs):
p1 = self.assign(self.parameter1, inputs)
self.assign(self.parameter1, inputs)
p1 = self.parameter1
out = self.addN((inputs, inputs, inputs))
out = self.relu(out)
return p1
@ -426,10 +429,8 @@ def test_side_effect_grad_read_dependency_assign_addn_relu_return_parameter():
try:
context.set_context(mode=context.PYNATIVE_MODE)
out2 = net.grad_mindspore_impl(inputs, grad_ys)
allclose_nparray(out1[0][0].asnumpy(), out2[0]
[0].asnumpy(), 0.001, 0.001)
allclose_nparray(out1[1][0].asnumpy(), out2[1]
[0].asnumpy(), 0.001, 0.001)
allclose_nparray(out1[0][0].asnumpy(), out2[0][0].asnumpy(), 0.001, 0.001)
allclose_nparray(out1[1][0].asnumpy(), out2[1][0].asnumpy(), 0.001, 0.001)
finally:
context.set_context(mode=context.GRAPH_MODE)
@ -462,6 +463,7 @@ class SideEffectPrintInHighOrdeAddnNet(Cell):
grad_out = grad_net(params, grad_ys)
return grad_out
@security_off_wrap
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@ -495,7 +497,8 @@ class SideEffectControlFlowAssignDependTwoIfNet(Cell):
self.assign(self.parameter1, x)
if self.parameter1 > y:
x = self.mul(x, x)
p2 = self.assign(self.parameter1, x)
self.assign(self.parameter1, x)
p2 = self.parameter1
if self.parameter1 > y:
x = self.addn((x, self.parameter1))
p3 = self.assign(self.parameter1, x)
@ -661,10 +664,12 @@ class SideEffectControlFlowAssignDependWhileNet(Cell):
self.depend = P.Depend()
def construct(self, x, y, z):
p1 = self.assign(self.parameter1, x)
self.assign(self.parameter1, x)
p1 = self.parameter1
while self.parameter1 < y:
x = self.addn((x, x))
p2 = self.assignadd(self.parameter1, z)
self.assignadd(self.parameter1, z)
p2 = self.parameter1
self.depend(p2, p1)
return x

7
tests/st/auto_monad/test_auto_monad_vmap.py
View File

@ -39,17 +39,18 @@ def test_monad_vmap():
self.value = Tensor([3, 4], mstype.int32)
def construct(self, x):
return self.assign(x, self.value)
x = self.assign(x, self.value)
return x
vampfunc = F.vmap(AssignNet())
@ms_function
def test_monad(a):
c = Tensor([[1, 2], [3, 4], [5, 6]], mstype.int32)
out = vampfunc(a)
c = a + c
out2 = P.AssignAdd()(a, c)
P.AssignAdd()(a, c)
out2 = a
return out, out2
a = Parameter(Tensor([[1, 2], [3, 4], [5, 6]], mstype.int32), name='param_a')

5
tests/st/control/inner/test_031_for_in_while.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -22,6 +22,8 @@ from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
grad_all = C.GradOperation(get_all=True)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@ -75,6 +77,7 @@ def test_for_in_while_01():
assert graph_forward_res == expect_forward_res
assert graph_backward_res == expect_backward_res
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training

4
tests/st/control/inner/test_331_for_after_for_in_while.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -23,6 +23,7 @@ from mindspore.common import dtype as mstype
grad_all = C.GradOperation(get_all=True)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@ -87,6 +88,7 @@ def test_for_after_for_in_while_01():
assert graph_forward_res == Tensor([1], mstype.int32)
assert graph_backward_res == (Tensor([0], mstype.int32), Tensor([0], mstype.int32))
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training

5
tests/st/control/test_ascend_control_sink.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -58,7 +58,8 @@ class ControlSimpleIfWithAssign(nn.Cell):
if x > y:
out = self.addn([input_data, input_data, input_data])
else:
out = self.assign(self.input_data, input_data)
self.assign(self.input_data, input_data)
out = self.input_data
return out

3
tests/st/networks/test_gradient_accumulation.py
View File

@ -26,7 +26,8 @@ _clear_op = C.MultitypeFuncGraph("clear_op")
def _cumulative_gard(grad_sum, grad):
"""Apply gard sum to cumulative gradient."""
add = P.AssignAdd()
return add(grad_sum, grad)
add(grad_sum, grad)
return grad_sum
@_clear_op.register("Tensor", "Tensor")

14
tests/st/ops/ascend/test_tbe_ops/test_AssignAdd.py → tests/st/ops/ascend/test_tbe_ops/test_assign_add.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -29,17 +29,21 @@ class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.AssignAdd = P.AssignAdd()
self.assign_add = P.AssignAdd()
self.inputdata = Parameter(initializer('normal', [1]), name="global_step")
print("inputdata: ", self.inputdata)
def construct(self, x):
out = self.AssignAdd(self.inputdata, x)
return out
self.assign_add(self.inputdata, x)
return self.inputdata
def test_net():
"""test AssignAdd"""
"""
Feature: test AssignAdd.
Description: test AssignAdd.
Expectation: No exception.
"""
net = Net()
x = Tensor(np.ones([1]).astype(np.float32) * 100)

14
tests/st/ops/ascend/test_tbe_ops/test_AssignSub.py → tests/st/ops/ascend/test_tbe_ops/test_assign_sub.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -29,17 +29,21 @@ class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.AssignSub = P.AssignSub()
self.assign_sub = P.AssignSub()
self.inputdata = Parameter(initializer('normal', [1]), name="global_step")
print("inputdata: ", self.inputdata)
def construct(self, x):
out = self.AssignSub(self.inputdata, x)
return out
self.assign_sub(self.inputdata, x)
return self.inputdata
def test_net():
"""test AssignSub"""
"""
Feature: test AssignSub.
Description: test AssignSub.
Expectation: No exception.
"""
net = Net()
x = Tensor(np.ones([1]).astype(np.int32) * 100)

6
tests/st/ops/cpu/test_assign_add_op.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -29,8 +29,8 @@ class AssignAdd(nn.Cell):
self.add = P.AssignAdd()
def construct(self, y):
res = self.add(self.var, y)
return res
self.add(self.var, y)
return self.var
@pytest.mark.level0

18
tests/st/ops/cpu/test_assign_sub_op.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2022 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.
@ -29,8 +29,8 @@ class AssignSub(nn.Cell):
self.sub = P.AssignSub()
def construct(self, y):
res = self.sub(self.var, y)
return res
self.sub(self.var, y)
return self.var
@pytest.mark.level0
@ -117,13 +117,17 @@ def test_assign_sub_func():
y2 = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
output1 = F.assign_sub(x1, y1)
F.assign_sub(x1, y1)
output1 = x1
assert (output1.asnumpy() == expect1).all()
output2 = F.assign_sub(output1, y1)
F.assign_sub(output1, y1)
output2 = output1
assert (output2.asnumpy() == expect2).all()
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
output1 = F.assign_sub(x2, y2)
F.assign_sub(x2, y2)
output1 = x2
assert (output1.asnumpy() == expect1).all()
output2 = F.assign_sub(output1, y2)
F.assign_sub(output1, y2)
output2 = output1
assert (output2.asnumpy() == expect2).all()

6
tests/st/ops/gpu/test_assign_add_op.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2022 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.
@ -29,8 +29,8 @@ class AssignAdd(nn.Cell):
self.add = P.AssignAdd()
def construct(self, y):
res = self.add(self.var, y)
return res
self.add(self.var, y)
return self.var
def assign_add(nptype):

5
tests/st/ops/gpu/test_assign_op.py
View File

@ -1,4 +1,4 @@
# Copyright 2020-2021 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -29,7 +29,8 @@ class Net(nn.Cell):
self.assign = P.Assign()
def construct(self, param):
return self.assign(self.var, param)
self.assign(self.var, param)
return self.var
x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)

6
tests/st/ops/graph_kernel/test_assign_add.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -32,6 +32,7 @@ class AssignAdd(nn.Cell):
self.add(self.var, y)
return self.var
def get_output(x2, y2, enable_graph_kernel=False):
context.set_context(enable_graph_kernel=enable_graph_kernel)
add = AssignAdd(x2)
@ -41,6 +42,7 @@ def get_output(x2, y2, enable_graph_kernel=False):
output = [result_gk_on_1, result_gk_on_2]
return output
def assign_add():
x2 = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
y2 = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
@ -53,6 +55,7 @@ def assign_add():
assert np.allclose(o1.asnumpy(), e1.asnumpy())
assert np.allclose(o2.asnumpy(), e2.asnumpy())
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
@ -60,6 +63,7 @@ def test_assign_add_gpu():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
assign_add()
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training

6
tests/st/optimizer/test_lamb_op.py
View File

@ -98,9 +98,9 @@ class LambGPUOrigin(nn.Cell):
next_param = param_fp32 - self.op_reshape(update_with_lr, self.op_shape(param_fp32))
next_param = F.depend(next_param, F.assign(self.param, self.op_cast(next_param, F.dtype(self.param))))
next_param = F.depend(next_param, F.assign(self.m, self.op_cast(next_m, F.dtype(self.m))))
next_param = F.depend(next_param, F.assign(self.v, self.op_cast(next_v, F.dtype(self.v))))
F.assign(self.param, self.op_cast(next_param, F.dtype(self.param)))
F.assign(self.m, self.op_cast(next_m, F.dtype(self.m)))
F.assign(self.v, self.op_cast(next_v, F.dtype(self.v)))
return self.op_cast(next_param, F.dtype(self.param))

16
tests/st/parameter/test_parameter.py
View File

@ -34,6 +34,7 @@ def test_parameter_1_1():
Description: If the name of the input of construct is same as the parameters, add suffix to the name of the input.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -58,6 +59,7 @@ def test_parameter_1_2():
Description: If the name of the input of construct is same as the parameters, add suffix to the name of the input.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -82,6 +84,7 @@ def test_parameter_2_1():
Description: If parameters in init have same name, an exception will be thrown.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -107,6 +110,7 @@ def test_parameter_2_2():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -134,6 +138,7 @@ def test_parameter_3():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -158,6 +163,7 @@ def test_parameter_4():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -183,6 +189,7 @@ def test_parameter_5_1():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -207,6 +214,7 @@ def test_parameter_5_2():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -233,6 +241,7 @@ def test_parameter_list_tuple_no_name():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -257,6 +266,7 @@ def test_parameter_in_tuple():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -282,6 +292,7 @@ def test_parameter_parameter_tuple_1():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -308,6 +319,7 @@ def test_parameter_parameter_tuple_2():
Description: Check the name of parameter in init.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -332,6 +344,7 @@ def test_parameter():
Description: If parameter in list or tuple is not given a name, will give it a unique name.
Expectation: No exception.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -369,6 +382,7 @@ def test_parameter_same_name_between_tuple_or_list():
Description: If the same name exists between tuple and list, an exception will be thrown.
Expectation: Get the expected exception report.
"""
class ParamNet(Cell):
def __init__(self):
super(ParamNet, self).__init__()
@ -402,6 +416,7 @@ def test_parameter_argument_and_fv():
Expectation: Parameter used as argument should equal to used as FV.
"""
y = Parameter(Tensor([1]))
class Demo(Cell):
def construct(self, x):
ms.ops.Assign()(x, Tensor([0]))
@ -428,6 +443,7 @@ def test_parameter_argument_grad():
Description: Use Parameter as input argmument, and pass it to varargs.
Expectation: Parameter used as argument should equal to used as FV.
"""
class ParameterArgumentCell(Cell):
def __init__(self):
super(ParameterArgumentCell, self).__init__()

7
tests/st/parameter/test_parameter_celllist.py
View File

@ -56,13 +56,14 @@ class EmaUpdate(nn.Cell):
def ema(self, tau, policy_param, target_param):
new_param = (1 - tau) * target_param + tau * policy_param
out = P.Assign()(target_param, new_param)
return out
P.Assign()(target_param, new_param)
return target_param
def construct(self):
if self.step % self.period == 0:
self.hyper_map(F.partial(self.ema, self.tau), self.policy_param, self.target_param)
return self.assignadd(self.step, 1)
self.assignadd(self.step, 1)
return self.step
@pytest.mark.level1

18
tests/st/pynative/mindrt/test_refnode.py
View File

@ -1,3 +1,17 @@
# Copyright 2022 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.
# ============================================================================
import pytest
import numpy as np
from mindspore import ops, Tensor, context
@ -12,7 +26,9 @@ class AssignNet(Cell):
self.input_data = input_variable
def construct(self, input_x):
return self.op(self.input_data, input_x)
self.op(self.input_data, input_x)
return self.input_data
@pytest.mark.level0
@pytest.mark.platform_x86_cpu

7
tests/st/vmap/test_vmap_graph.py
View File

@ -160,7 +160,8 @@ def test_vmap_monad():
def construct(self, assign_add_val, assign_add_var, scatter_ref, indices, updates):
self.assign(self.assign_ref, self.replace_tensor)
F.print(self.assign_ref)
out = self.assign_add(assign_add_var, assign_add_val) + self.scatter_add(scatter_ref, indices, updates)
self.assign_add(assign_add_var, assign_add_val)
out = assign_add_var + self.scatter_add(scatter_ref, indices, updates)
return out
class VmapMonadNet(nn.Cell):
@ -411,8 +412,8 @@ def test_vmap_with_celllist_input():
self.ref_b = Parameter(Tensor([0, 1, 2], mstype.float32), name='ref_b')
def construct(self, replace_tensor):
out = self.assign(self.ref_a, replace_tensor)
out = self.ref_b + out
self.assign(self.ref_a, replace_tensor)
out = self.ref_b + self.ref_a
return out
m1 = AssignNet()

20
tests/syntax/simple_expression/test_assignment_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -255,10 +255,10 @@ def test_tuple_assignadd_tuple():
def test_string_assignadd_string():
result1 = "string111"
result2 = "string111"
input2 = "string111"
input_x = "string222"
result1 += input_x
result2 = AssignAdd(result2, input_x)()
result2 = AssignAdd(input2, input_x)
expect = "string111string222"
assert result1 == expect
assert result2 == expect
@ -279,9 +279,9 @@ class AssignSub(nn.Cell):
def test_number_assignsub_number():
input_x = 2
result1 = 5
result2 = 5
input2 = 5
result1 -= input_x
result2 = AssignSub(result2, input_x)()
result2 = AssignSub(input2, input_x)
expect = 3
assert np.all(result1 == expect)
assert np.all(result2 == expect)
@ -292,7 +292,7 @@ def test_tensor_assignsub_tensor():
result1 = Tensor(np.array([[4, -2], [2, 17]]))
result2 = Tensor(np.array([[4, -2], [2, 17]]))
result1 -= input_x
result2 = AssignSub(result2, input_x)()
result2 = AssignSub(result2, input_x)
expect = Tensor(np.array([[2, -4], [-1, 14]]))
assert np.all(result1.asnumpy() == expect)
assert np.all(result2.asnumpy() == expect)
@ -301,9 +301,9 @@ def test_tensor_assignsub_tensor():
def test_tensor_assignsub_number():
input_x = 3
result1 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
result2 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
input2 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
result1 -= input_x
result2 = AssignSub(result2, input_x)()
result2 = AssignSub(input2, input_x)
expect = Tensor(np.array([[1, -5], [-1, 14]]))
assert np.all(result1.asnumpy() == expect)
assert np.all(result2.asnumpy() == expect)
@ -311,10 +311,10 @@ def test_tensor_assignsub_number():
def test_number_assignsub_tensor():
result1 = 3
result2 = 3
input2 = 3
input_x = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
result1 -= input_x
result2 = AssignSub(result2, input_x)()
result2 = AssignSub(input2, input_x)
expect = Tensor(np.array([[-1, 5], [1, -14]]))
assert np.all(result1.asnumpy() == expect)
assert np.all(result2.asnumpy() == expect)

216
tests/ut/python/exec/test_AssignAdd.py → tests/ut/python/exec/test_assign_add.py
View File

@ -1,102 +1,114 @@
# 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 assign add
"""
import numpy as np
import mindspore as ms
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
from ..ut_filter import non_graph_engine
context.set_context(mode=context.GRAPH_MODE)
class Net(nn.Cell):
"""Net definition"""
def __init__(self):
super(Net, self).__init__()
self.AssignAdd = P.AssignAdd()
self.inputdata = Parameter(initializer(1, [1], ms.int64), name="global_step")
print("inputdata: ", self.inputdata)
def construct(self, x):
out = self.AssignAdd(self.inputdata, x)
return out
@non_graph_engine
def test_AssignAdd_1():
"""test AssignAdd 1"""
context.set_context(mode=context.GRAPH_MODE)
net = Net()
x = Tensor(np.ones([1]).astype(np.int64) * 100)
print("MyPrintResult dataX:", x)
result = net(x)
print("MyPrintResult data::", result)
expect = np.ones([1]).astype(np.int64) * 101
diff = result.asnumpy() - expect
print("MyPrintExpect:", expect)
print("MyPrintDiff:", diff)
error = np.ones(shape=[1]) * 1.0e-3
assert np.all(diff < error)
@non_graph_engine
def test_AssignAdd_2():
"""test AssignAdd 2"""
context.set_context(mode=context.GRAPH_MODE)
net = Net()
x = Tensor(np.ones([1]).astype(np.int64) * 102)
print("MyPrintResult dataX:", x)
result = net(x)
print("MyPrintResult data::", result.asnumpy())
expect = np.ones([1]).astype(np.int64) * 103
diff = result.asnumpy() - expect
print("MyPrintExpect:", expect)
print("MyPrintDiff:", diff)
error = np.ones(shape=[1]) * 1.0e-3
assert np.all(diff < error)
class AssignAddNet(nn.Cell):
"""Net definition"""
def __init__(self):
super(AssignAddNet, self).__init__()
self.AssignAdd = P.AssignAdd()
self.inputdata = Parameter(initializer(1, [1], ms.float16), name="KIND_AUTOCAST_SCALAR_TO_TENSOR")
self.one = 1
def construct(self, ixt):
z1 = self.AssignAdd(self.inputdata, self.one)
return z1
@non_graph_engine
def test_assignadd_scalar_cast():
net = AssignAddNet()
x = Tensor(np.ones([1]).astype(np.int64) * 102)
# _executor.compile(net, 1)
_ = net(x)
# Copyright 2020-2022 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 assign add
"""
import numpy as np
import mindspore as ms
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
from ..ut_filter import non_graph_engine
context.set_context(mode=context.GRAPH_MODE)
class Net(nn.Cell):
"""Net definition"""
def __init__(self):
super(Net, self).__init__()
self.assign_add = P.AssignAdd()
self.inputdata = Parameter(initializer(1, [1], ms.int64), name="global_step")
print("inputdata: ", self.inputdata)
def construct(self, x):
self.assign_add(self.inputdata, x)
return self.inputdata
@non_graph_engine
def test_assign_add_1():
"""
Feature: test AssignAdd.
Description: test AssignAdd.
Expectation: No exception.
"""
context.set_context(mode=context.GRAPH_MODE)
net = Net()
x = Tensor(np.ones([1]).astype(np.int64) * 100)
print("MyPrintResult dataX:", x)
result = net(x)
print("MyPrintResult data::", result)
expect = np.ones([1]).astype(np.int64) * 101
diff = result.asnumpy() - expect
print("MyPrintExpect:", expect)
print("MyPrintDiff:", diff)
error = np.ones(shape=[1]) * 1.0e-3
assert np.all(diff < error)
@non_graph_engine
def test_assign_add_2():
"""
Feature: test AssignAdd.
Description: test AssignAdd.
Expectation: No exception.
"""
context.set_context(mode=context.GRAPH_MODE)
net = Net()
x = Tensor(np.ones([1]).astype(np.int64) * 102)
print("MyPrintResult dataX:", x)
result = net(x)
print("MyPrintResult data::", result.asnumpy())
expect = np.ones([1]).astype(np.int64) * 103
diff = result.asnumpy() - expect
print("MyPrintExpect:", expect)
print("MyPrintDiff:", diff)
error = np.ones(shape=[1]) * 1.0e-3
assert np.all(diff < error)
class AssignAddNet(nn.Cell):
"""Net definition"""
def __init__(self):
super(AssignAddNet, self).__init__()
self.assign_add = P.AssignAdd()
self.inputdata = Parameter(initializer(1, [1], ms.float16), name="KIND_AUTOCAST_SCALAR_TO_TENSOR")
self.one = 1
def construct(self, ixt):
self.assign_add(self.inputdata, self.one)
return self.inputdata
@non_graph_engine
def test_assignadd_scalar_cast():
"""
Feature: test AssignAdd.
Description: test AssignAdd.
Expectation: No exception.
"""
net = AssignAddNet()
x = Tensor(np.ones([1]).astype(np.int64) * 102)
_ = net(x)

3
tests/ut/python/exec/test_assign_sub.py
View File

@ -34,7 +34,8 @@ class Net(nn.Cell):
self.sub = P.AssignSub()
def construct(self, value):
return self.sub(self.b, value)
self.sub(self.b, value)
return self.b
def test_net():

6
tests/ut/python/ops/test_math_ops.py
View File

@ -30,6 +30,9 @@ from mindspore.ops.operations._grad_ops import IgammaGradA
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.operations.math_ops import Zeta, Igamma, Igammac
from mindspore.ops.operations.sparse_ops import DenseToDenseSetOperation
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
@ -368,7 +371,8 @@ class AssignAdd(nn.Cell):
def construct(self, input_):
self.inputdata = input_
return self.op(self.inputdata, input_)
self.op(self.inputdata, input_)
return self.inputdata
class FloorNet(nn.Cell):

17
tests/ut/python/optimizer/test_bprop_mindir.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 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.
@ -206,7 +206,8 @@ def test_assign():
self.variable = Parameter(Tensor([1.0], mstype.float32), name="variable")
def construct(self, x):
return self.assign(self.variable, x)
self.assign(self.variable, x)
return self.variable
value = Tensor([2.0], mstype.float32)
assign = AssignNet()
@ -222,7 +223,8 @@ def test_assign_add():
self.variable = Parameter(initializer(1, [1], mstype.int64), name="global_step")
def construct(self, x):
return self.assign_add(self.variable, x)
self.assign_add(self.variable, x)
return self.variable
value = Tensor(np.ones([1]).astype(np.int64) * 100)
assign_add = AssignAddNet()
@ -234,11 +236,12 @@ def test_assign_sub():
class AssignSubNet(nn.Cell):
def __init__(self):
super(AssignSubNet, self).__init__()
self.assign = P.AssignSub()
self.assign_sub = P.AssignSub()
self.variable = Parameter(initializer(1, [1], mstype.int32), name="global_step")
def construct(self, x):
return self.assign(self.variable, x)
self.assign_sub(self.variable, x)
return self.variable
value = Tensor(np.ones([1]).astype(np.int32) * 100)
assign_sub = AssignSubNet()
@ -500,6 +503,7 @@ def test_switch():
Expectation: Load the bprop mindir successfully.
"""
context.set_context(mode=context.PYNATIVE_MODE)
class SwitchNet(nn.Cell):
def construct(self, x, y):
if x > y:
@ -527,7 +531,8 @@ def test_update_state():
self.variable = Parameter(initializer(1, [1], mstype.int64), name="global_step")
def construct(self, x):
return self.assign_add(self.variable, x)
self.assign_add(self.variable, x)
return self.variable
value = Tensor(np.ones([1]).astype(np.int64) * 100)
update_state = UpdateStateNet()

65
tests/ut/python/parallel/test_arithmetic.py
View File

@ -62,6 +62,7 @@ def test_matmul_sub():
Description: matmul-sub net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -91,6 +92,7 @@ def test_matmul_add():
Description: matmul-add net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -120,6 +122,7 @@ def test_matmul_mul():
Description: matmul-mul net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -142,12 +145,14 @@ def test_matmul_mul():
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
compile_net(net, x, y, b)
def test_matmul_mod():
"""
Feature: distribute operator sub in auto parallel.
Description: matmul-mod net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -170,12 +175,14 @@ def test_matmul_mod():
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
compile_net(net, x, y, b)
def test_matmul_floormod():
"""
Feature: distribute operator sub in auto parallel.
Description: matmul-floormod net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -205,6 +212,7 @@ def test_matmul_atan2():
Description: matmul-atan2 net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -234,6 +242,7 @@ def test_matmul_divNoNan():
Description: matmul-divNoNan net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -263,6 +272,7 @@ def test_matmul_logicaland():
Description: matmul-logical_and net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -297,6 +307,7 @@ def test_matmul_logicalor():
Description: matmul-logical_or net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -331,6 +342,7 @@ def test_matmul_div():
Description: matmul-div net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -360,6 +372,7 @@ def test_matmul_add_broadcast():
Description: matmul-add broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -389,6 +402,7 @@ def test_matmul_add_broadcast2():
Description: matmul-add broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -418,6 +432,7 @@ def test_matmul_sub_broadcast():
Description: matmul-sub broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -447,6 +462,7 @@ def test_matmul_sub_broadcast2():
Description: matmul-sub broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -476,6 +492,7 @@ def test_matmul_mul_broadcast():
Description: matmul-mul broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -505,6 +522,7 @@ def test_matmul_mul_broadcast2():
Description: matmul-mul broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -534,6 +552,7 @@ def test_matmul_div_broadcast():
Description: matmul-div broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -563,6 +582,7 @@ def test_matmul_div_broadcast2():
Description: matmul-div broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -592,6 +612,7 @@ def test_matmul_greater_broadcast():
Description: matmul-greater broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -621,6 +642,7 @@ def test_matmul_greater_broadcast2():
Description: matmul-greater broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -650,6 +672,7 @@ def test_matmul_floordiv():
Description: matmul-floordiv net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -679,6 +702,7 @@ def test_matmul_floordiv_broadcast():
Description: matmul-floordiv broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -708,6 +732,7 @@ def test_matmul_floordiv_broadcast2():
Description: matmul-floordiv broadcast net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -737,6 +762,7 @@ def test_assign_sub():
Description: mul-assign_sub net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self):
super().__init__()
@ -751,7 +777,7 @@ def test_assign_sub():
def construct(self, x):
out = self.mul(x, self.mul_weight)
out = self.assign_sub(self.assignsub_weight, out)
self.assign_sub(self.assignsub_weight, out)
return out
class SubNetWithLoss(nn.Cell):
@ -786,25 +812,26 @@ def test_assign_sub():
def test_assign_add():
"""
Feature: distribute operator sub in auto parallel.
Feature: distribute operator add in auto parallel.
Description: mul-assign_add net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self):
super().__init__()
self.assign_sub = P.AssignAdd()
self.assign_add = P.AssignAdd()
self.mul = P.Mul()
self.mul_weight = Parameter(Tensor(np.full([128, 32],
0.5, dtype=np.float32)),
name="mul_weight")
self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
self.assignadd_weight = Parameter(Tensor(np.full([128, 32],
1.1, dtype=np.float32)),
name="assignsub_weight")
name="assignadd_weight")
def construct(self, x):
out = self.mul(x, self.mul_weight)
out = self.assign_sub(self.assignsub_weight, out)
self.assign_add(self.assignadd_weight, out)
return out
class SubNetWithLoss(nn.Cell):
@ -839,25 +866,26 @@ def test_assign_add():
def test_assign():
"""
Feature: distribute operator sub in auto parallel.
Feature: distribute operator assign in auto parallel.
Description: mul-assign_sub net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self):
super().__init__()
self.assign_sub = P.Assign()
self.assign = P.Assign()
self.mul = P.Mul()
self.mul_weight = Parameter(Tensor(np.full([128, 32],
0.5, dtype=np.float32)),
name="mul_weight")
self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
1.1, dtype=np.float32)),
name="assignsub_weight")
self.assign_weight = Parameter(Tensor(np.full([128, 32],
1.1, dtype=np.float32)),
name="assign_weight")
def construct(self, x):
out = self.mul(x, self.mul_weight)
out = self.assign_sub(self.assignsub_weight, out)
self.assign(self.assign_weight, out)
return out
class SubNetWithLoss(nn.Cell):
@ -896,17 +924,16 @@ def test_matmul_bitwise_and_broadcast():
Description: mul-BitwiseAnd net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
self.bitwise_and = P.BitwiseAnd().shard(strategy1)
self.matmul = P.MatMul().shard(strategy2)
def construct(self, x, y, z):
out = self.bitwise_and(x, y)
out = self.matmul(out, z)
return out
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
@ -926,6 +953,7 @@ def test_matmul_bitwise_or_broadcast():
Description: mul-BitwiseOr net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -954,6 +982,7 @@ def test_matmul_bitwise_xor_broadcast():
Description: mul-BitwiseXor net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -982,6 +1011,7 @@ def test_matmul_mul_no_nan_broadcast():
Description: mul-MulNoNan net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1010,6 +1040,7 @@ def test_matmul_truncate_div_broadcast():
Description: mul-TruncateDiv net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1038,6 +1069,7 @@ def test_matmul_truncate_mod_broadcast():
Description: mul-TruncateMod net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1066,6 +1098,7 @@ def test_matmul_xdivy_broadcast():
Description: mul-Xdivy net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1094,6 +1127,7 @@ def test_matmul_xlogy_broadcast():
Description: mul-Xlogy net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1122,6 +1156,7 @@ def test_matmul_squared_difference_broadcast():
Description: mul-SquaredDifference net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1150,6 +1185,7 @@ def test_matmul_masked_fill_broadcast_with_value_float():
Description: mul-MaskedFill net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
@ -1179,6 +1215,7 @@ def test_matmul_masked_fill_broadcast_with_value_tensor():
Description: mul-MaskedFill net with strategy in semi auto parallel.
Expectation: compile done without error.
"""
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()

18
tests/ut/python/pipeline/infer/test_net_infer.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -21,8 +21,10 @@ from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
import mindspore.ops.operations as op
def test_net_infer():
""" test_net_infer """
class Net(nn.Cell):
""" Net definition """
@ -40,13 +42,14 @@ def test_net_infer():
x = self.flatten(x)
out = self.fc(x)
return out
Tensor(np.random.randint(0, 255, [1, 3, 224, 224]))
Net()
def test_assign_in_while():
context.set_context(device_target="Ascend")
context.set_context(mode=context.GRAPH_MODE)
context.set_context(device_target="Ascend", mode=context.GRAPH_MODE)
class Net(nn.Cell):
def __init__(self, input_shape):
super().__init__()
@ -58,7 +61,8 @@ def test_assign_in_while():
while x < y:
inputdata = self.inputdata
x = x + 1
out = self.assign(inputdata, z)
self.assign(inputdata, z)
out = inputdata
return out
x = Tensor(np.array(1).astype(np.int32))
@ -76,9 +80,6 @@ def test_dup_context():
context.set_context(mode=context.GRAPH_MODE)
class Net(nn.Cell):
def __init__(self):
super().__init__()
def construct(self, x):
def identity(f):
return f
@ -106,9 +107,6 @@ def test_maybe_poly_func():
context.set_context(mode=context.GRAPH_MODE)
class Net(nn.Cell):
def __init__(self):
super().__init__()
def construct(self, x, y, z):
def identity(f, inp):
return f(inp)

27
tests/ut/python/pipeline/parse/test_parse.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -34,6 +34,7 @@ from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.functional import tensor_add
from ...ut_filter import non_graph_engine
# pylint: disable=W0613,W0612
# W0613: unused-argument
@ -46,11 +47,11 @@ def fixture_enable_check_bprop():
grad_all = C.GradOperation(get_all=True)
log = logging.getLogger("test")
log.setLevel(level=logging.ERROR)
context.set_context(mode=context.GRAPH_MODE)
# Test case: use the parse obj interface use default parameter
class Net(nn.Cell):
""" Net definition """
@ -60,7 +61,7 @@ class Net(nn.Cell):
self.softmax1 = nn.Softmax(dim)
self.softmax2 = nn.Softmax(dim + 1)
def construct(self, input_data, input1=1+2+3+4):
def construct(self, input_data, input1=1 + 2 + 3 + 4):
return self.softmax1(input_data)
@ -84,10 +85,6 @@ def test_parse_defalut_parameter_case2():
# Test case: use the variable parameter for parse object
class Net1(nn.Cell):
""" Net1 definition """
def __init__(self):
super(Net1, self).__init__()
def construct(self, *args):
x = args[0]
return x
@ -177,9 +174,6 @@ def test_bprop_with_wrong_output_num(enable_check_bprop):
return bprop
class BpropWithWrongOutputNumCell(nn.Cell):
def __init__(self):
super(BpropWithWrongOutputNumCell, self).__init__()
def construct(self, x, y):
return BpropWithWrongOutputNum()(x, y)
@ -187,6 +181,7 @@ def test_bprop_with_wrong_output_num(enable_check_bprop):
grad_all(BpropWithWrongOutputNumCell())(Tensor(np.array(1).astype(np.int32)),
Tensor(np.array(2).astype(np.int32)))
def test_bprop_with_wrong_output_type(enable_check_bprop):
class BpropWithWrongOutputType(PrimitiveWithInfer):
@prim_attr_register
@ -212,9 +207,6 @@ def test_bprop_with_wrong_output_type(enable_check_bprop):
return bprop
class BpropWithWrongOutputTypeCell(nn.Cell):
def __init__(self):
super(BpropWithWrongOutputTypeCell, self).__init__()
def construct(self, x):
return BpropWithWrongOutputType()(x)
@ -248,9 +240,6 @@ def test_bprop_with_wrong_output_shape(enable_check_bprop):
return bprop
class BpropWithWrongOutputShapeCell(nn.Cell):
def __init__(self):
super(BpropWithWrongOutputShapeCell, self).__init__()
def construct(self, x):
return BpropWithWrongOutputShape()(x)
@ -259,6 +248,7 @@ def test_bprop_with_wrong_output_shape(enable_check_bprop):
net.set_grad()
grad_all(net)(Tensor(np.ones([64, 10]).astype(np.int32)))
class AssignWhenInsertGrad(nn.Cell):
""" NetWithNDarray definition """
@ -280,8 +270,10 @@ class AssignWhenInsertGrad(nn.Cell):
out = self.getG(out)
return out
grad_all = C.GradOperation(get_all=True)
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
@ -291,6 +283,7 @@ class GradNet(nn.Cell):
out = self.net(*inputs)
return out, grad_all(self.net)(*inputs)
def test_assign_in_insert_grad():
context.set_context(mode=context.GRAPH_MODE)
net = AssignWhenInsertGrad().to_float(ms.float16)
@ -298,6 +291,7 @@ def test_assign_in_insert_grad():
net_back = GradNet(net)
net_back(ms.Tensor(input_data))
class Assign(nn.Cell):
""" NetWithNDarray definition """
@ -317,6 +311,7 @@ def test_assign(enable_check_bprop):
net_back = GradNet(net)
net_back(input_data)
class AssignCheck(nn.Cell):
""" NetWithNDarray definition """

25
tests/ut/python/pipeline/parse/test_while_param.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 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.
@ -44,6 +44,7 @@ def test_while_loop_phi():
net = WhileSubGraphParam()
net(x, y, z)
class WhileSubGraphParam2(Cell):
def __init__(self):
super().__init__()
@ -73,15 +74,15 @@ class WhileSubGraphParam3(Cell):
def __init__(self, initial_input_x):
super().__init__()
self.initial_input_x = initial_input_x
self.X = ms.Parameter(initial_input_x, name="parameter_x")
self.Y = ms.Parameter(self.initial_input_x, name="parameter_y")
self.x = ms.Parameter(initial_input_x, name="parameter_x")
self.y = ms.Parameter(self.initial_input_x, name="parameter_y")
def construct(self):
a = 0
while a < 3:
self.X = self.X + self.Y
self.x = self.x + self.y
a += 1
return self.X
return self.x
def test_while_loop_phi_3():
@ -91,6 +92,7 @@ def test_while_loop_phi_3():
net = WhileSubGraphParam3(x)
net()
class ControlMixedWhileIf(nn.Cell):
def __init__(self):
super().__init__()
@ -101,24 +103,28 @@ class ControlMixedWhileIf(nn.Cell):
def construct(self, x, y, z, c2, c4):
out = self.assign(self.var, c4)
while x < c2:
y = self.assign(self.var, c4)
self.assign(self.var, c4)
y = self.var
while y < c2 and x < c2:
if 2 * y < c2:
y = y + 2
else:
y = y + 1
out = out + y
z = self.assign(self.var, c4)
self.assign(self.var, c4)
z = self.var
while z < c2:
z = z + 1
out = out + z
x = x + 1
out = out + x
while x < 2 * c2:
y = self.assign(self.var, c4)
self.assign(self.var, c4)
y = self.var
x = x + 1
while y < c2:
z = self.assign(self.var, c4)
self.assign(self.var, c4)
z = self.var
while z < c2:
z = z + 1
if x < c2:
@ -130,6 +136,7 @@ class ControlMixedWhileIf(nn.Cell):
out = out + x
return out
def test_mixed_while_if():
context.set_context(mode=context.PYNATIVE_MODE)
x = np.array(2).astype(np.int32)