forked from mindspore-Ecosystem/mindspore
644 lines
25 KiB
Python
644 lines
25 KiB
Python
# Copyright 2021 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 numpy as np
|
|
import pytest
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, Parameter
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
|
|
|
|
class TestScatterAddNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterAddNet, self).__init__()
|
|
self.scatter_add = P.ScatterAdd(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_add(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_add_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterAddNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_add_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterAddNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_small_float32():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[6., 8., 10.],
|
|
[12., 14., 16.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_input_updated():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterAddNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[6., 8., 10.],
|
|
[12., 14., 16.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_large_shape_float32():
|
|
inputx = Tensor(np.ones((4, 2, 3, 4)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 2], [3, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(96).reshape((2, 2, 2, 3, 4)).astype(np.float32))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[[[1., 2., 3., 4.],
|
|
[5., 6., 7., 8.],
|
|
[9., 10., 11., 12.]],
|
|
[[13., 14., 15., 16.],
|
|
[17., 18., 19., 20.],
|
|
[21., 22., 23., 24.]]],
|
|
[[[73., 74., 75., 76.],
|
|
[77., 78., 79., 80.],
|
|
[81., 82., 83., 84.]],
|
|
[[85., 86., 87., 88.],
|
|
[89., 90., 91., 92.],
|
|
[93., 94., 95., 96.]]],
|
|
[[[25., 26., 27., 28.],
|
|
[29., 30., 31., 32.],
|
|
[33., 34., 35., 36.]],
|
|
[[37., 38., 39., 40.],
|
|
[41., 42., 43., 44.],
|
|
[45., 46., 47., 48.]]],
|
|
[[[49., 50., 51., 52.],
|
|
[53., 54., 55., 56.],
|
|
[57., 58., 59., 60.]],
|
|
[[61., 62., 63., 64.],
|
|
[65., 66., 67., 68.],
|
|
[69., 70., 71., 72.]]]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_small_float32_use_locking_false():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([1, 0]).astype(np.int32))
|
|
updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
|
|
output = scatter_add_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[3., 4., 5.],
|
|
[0., 1., 2.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_input_less_than_1_float32():
|
|
inputx = Tensor(np.array([[0.214141, 0.415151, 0.51516],
|
|
[0.876542, 0.451611, 0.55112],
|
|
[0.111244, 0.633333, 0.34444]]).astype(np.float32))
|
|
indices = Tensor(np.array([[[1, 0, 2],
|
|
[2, 2, 0]],
|
|
[[1, 0, 1],
|
|
[2, 1, 2]]]).astype(np.int32))
|
|
updates = Tensor(np.arange(34, 70).reshape((2, 2, 3, 3)).astype(np.float32))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[141.21414, 144.41515, 147.51517],
|
|
[208.87654, 212.45161, 216.55112],
|
|
[257.11124, 262.63333, 267.34442]], dtype=np.float32)
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_float16():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float16))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float16))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[6., 8., 10.],
|
|
[12., 14., 16.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_large_float16():
|
|
inputx = Tensor(np.zeros((2, 3, 4)).astype(np.float16))
|
|
indices = Tensor(np.array([[0, 0], [1, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[[138., 140., 142., 144.],
|
|
[146., 148., 150., 152.],
|
|
[154., 156., 158., 160.]],
|
|
[[186., 188., 190., 192.],
|
|
[194., 196., 198., 200.],
|
|
[202., 204., 206., 208.]]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_disordered_float16():
|
|
inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.float16)))
|
|
indices = Tensor(np.array([[[0, 1, 2],
|
|
[2, 1, 0]],
|
|
[[0, 0, 0],
|
|
[2, 2, 2]]]).astype(np.int32))
|
|
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[464., 468., 472., 476.],
|
|
[187., 188., 189., 190.],
|
|
[492., 496., 500., 504.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_large_int32():
|
|
inputx = Tensor(np.zeros((2, 3, 4)).astype(np.int32))
|
|
indices = Tensor(np.array([[0, 0], [1, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[[138., 140., 142., 144.],
|
|
[146., 148., 150., 152.],
|
|
[154., 156., 158., 160.]],
|
|
[[186., 188., 190., 192.],
|
|
[194., 196., 198., 200.],
|
|
[202., 204., 206., 208.]]]).astype(np.int32)
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_add_disordered_int32():
|
|
inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int32)))
|
|
indices = Tensor(np.array([[[0, 1, 2],
|
|
[2, 1, 0]],
|
|
[[0, 0, 0],
|
|
[2, 2, 2]]]).astype(np.int32))
|
|
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.int32))
|
|
output = scatter_add_net(inputx, indices, updates)
|
|
expected = np.array([[464., 468., 472., 476.],
|
|
[187., 188., 189., 190.],
|
|
[492., 496., 500., 504.]]).astype(np.int32)
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
class TestScatterSubNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterSubNet, self).__init__()
|
|
self.scatter_sub = P.ScatterSub(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_sub(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_sub_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterSubNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_sub_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterSubNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_sub_input_updated():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterSubNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[-6., -8., -10.],
|
|
[-12., -14., -16.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_sub_large_shape_float32():
|
|
inputx = Tensor(np.ones((4, 2, 3, 4)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 2], [3, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(96).reshape((2, 2, 2, 3, 4)).astype(np.float32))
|
|
output = scatter_sub_net(inputx, indices, updates)
|
|
expected = np.array(
|
|
[[[[1.0, 0.0, -1.0, -2.0],
|
|
[-3.0, -4.0, -5.0, -6.0],
|
|
[-7.0, -8.0, -9.0, -10.0]],
|
|
[[-11.0, -12.0, -13.0, -14.0],
|
|
[-15.0, -16.0, -17.0, -18.0],
|
|
[-19.0, -20.0, -21.0, -22.0]]],
|
|
[[[-71.0, -72.0, -73.0, -74.0],
|
|
[-75.0, -76.0, -77.0, -78.0],
|
|
[-79.0, -80.0, -81.0, -82.0]],
|
|
[[-83.0, -84.0, -85.0, -86.0],
|
|
[-87.0, -88.0, -89.0, -90.0],
|
|
[-91.0, -92.0, -93.0, -94.0]]],
|
|
[[[-23.0, -24.0, -25.0, -26.0],
|
|
[-27.0, -28.0, -29.0, -30.0],
|
|
[-31.0, -32.0, -33.0, -34.0]],
|
|
[[-35.0, -36.0, -37.0, -38.0],
|
|
[-39.0, -40.0, -41.0, -42.0],
|
|
[-43.0, -44.0, -45.0, -46.0]]],
|
|
[[[-47.0, -48.0, -49.0, -50.0],
|
|
[-51.0, -52.0, -53.0, -54.0],
|
|
[-55.0, -56.0, -57.0, -58.0]],
|
|
[[-59.0, -60.0, -61.0, -62.0],
|
|
[-63.0, -64.0, -65.0, -66.0],
|
|
[-67.0, -68.0, -69.0, -70.0]]]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_sub_small_float32_use_locking_false():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([1, 0]).astype(np.int32))
|
|
updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
|
|
output = scatter_sub_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[-3., -4., -5.],
|
|
[-0., -1., -2.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
class TestScatterMulNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterMulNet, self).__init__()
|
|
self.scatter_mul = P.ScatterMul(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_mul(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_mul_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterMulNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_mul_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterMulNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_mul_input_updated():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterMulNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[0., 7., 16.],
|
|
[27., 40., 55.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_mul_output_updated_float32():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_mul_net(inputx, indices, updates)
|
|
expected = np.array([[0., 7., 16.],
|
|
[27., 40., 55.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_mul_small_float32_use_locking_false():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_mul_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[0., 7., 16.],
|
|
[27., 40., 55.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
class TestScatterDivNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterDivNet, self).__init__()
|
|
self.scatter_div = P.ScatterDiv(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_div(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_div_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterDivNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_div_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterDivNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_div_input_updated():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(1, 13).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterDivNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[0., 0., 0.],
|
|
[0., 0., 0.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_div_output_updated_float32():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(1, 13).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_div_net(inputx, indices, updates)
|
|
expected = np.array([[0., 0., 0.],
|
|
[0., 0., 0.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_div_small_float32_use_locking_false():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32) * 10)
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.ones(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_div_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[10., 10., 10.],
|
|
[10., 10., 10.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
class TestScatterMaxNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterMaxNet, self).__init__()
|
|
self.scatter_max = P.ScatterMax(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_max(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_max_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterMaxNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_max_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterMaxNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_max_input_updated():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterMaxNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[6., 7., 8.],
|
|
[9., 10., 11.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_max_output_updated_float32():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_max_net(inputx, indices, updates)
|
|
expected = np.array([[6., 7., 8.],
|
|
[9., 10., 11.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_max_small_float32_use_locking_false():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32) * 10)
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_max_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[10., 10., 10.],
|
|
[10., 10., 11.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
class TestScatterMinNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterMinNet, self).__init__()
|
|
self.scatter_min = P.ScatterMin(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_min(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_min_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterMinNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_min_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterMinNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_min_input_updated():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterMinNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[0., 0., 0.],
|
|
[0., 0., 0.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_min_output_updated_float32():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_min_net(inputx, indices, updates)
|
|
expected = np.array([[0., 1., 1.],
|
|
[1., 1., 1.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_min_small_float32_use_locking_false():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_min_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[0., 1., 1.],
|
|
[1., 1., 1.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
class TestScatterUpdateNet(nn.Cell):
|
|
def __init__(self, lock, inputx, indices, updates):
|
|
super(TestScatterUpdateNet, self).__init__()
|
|
self.scatter_update = P.ScatterUpdate(use_locking=lock)
|
|
self.inputx = Parameter(inputx, name="inputx")
|
|
self.indices = Parameter(indices, name="indices")
|
|
self.updates = Parameter(updates, name="updates")
|
|
|
|
def construct(self):
|
|
out = self.scatter_update(self.inputx, self.indices, self.updates)
|
|
return out
|
|
|
|
|
|
def scatter_update_net(inputx, indices, updates):
|
|
lock = True
|
|
net = TestScatterUpdateNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
def scatter_update_use_locking_false_net(inputx, indices, updates):
|
|
lock = False
|
|
net = TestScatterUpdateNet(lock, inputx, indices, updates)
|
|
return net()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_update_input_updated():
|
|
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
lock = True
|
|
net = TestScatterUpdateNet(lock, inputx, indices, updates)
|
|
net()
|
|
expected = np.array([[6., 7., 8.],
|
|
[9., 10., 11.]])
|
|
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_update_output_updated_float32():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_update_net(inputx, indices, updates)
|
|
expected = np.array([[6., 7., 8.],
|
|
[9., 10., 11.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_scatter_update_small_float32_use_locking_false():
|
|
inputx = Tensor(np.ones((2, 3)).astype(np.float32))
|
|
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
|
|
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
|
|
output = scatter_update_use_locking_false_net(inputx, indices, updates)
|
|
expected = np.array([[6., 7., 8.],
|
|
[9., 10., 11.]])
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|