forked from mindspore-Ecosystem/mindspore
360 lines
16 KiB
Python
360 lines
16 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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# all cases tested against dchip
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class TestScatterUpdateNet(nn.Cell):
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def __init__(self, inputx, indices, updates):
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super(TestScatterUpdateNet, self).__init__()
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self.scatter_update = P.ScatterUpdate()
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self.inputx = Parameter(inputx, name="inputx")
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self.indices = Parameter(indices, name="indices")
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self.updates = Parameter(updates, name="updates")
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def construct(self):
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out = self.scatter_update(self.inputx, self.indices, self.updates)
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return out
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def scatter_update_net(inputx, indices, updates):
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net = TestScatterUpdateNet(inputx, indices, updates)
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return net()
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class TestScatterUpdateDynamicNet(nn.Cell):
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def __init__(self, inputx, indices, updates):
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super(TestScatterUpdateDynamicNet, self).__init__()
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self.scatter_update = P.ScatterUpdate()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.inputx = Parameter(inputx, name="inputx")
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self.indices = Parameter(indices, name="indices")
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self.updates = Parameter(updates, name="updates")
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def construct(self):
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indices = self.test_dynamic(self.indices)
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updates = self.test_dynamic(self.updates)
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out = self.scatter_update(self.inputx, indices, updates)
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return out
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def scatter_update_d_net(inputx, indices, updates):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = TestScatterUpdateDynamicNet(inputx, indices, updates)
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return net()
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class TestScatterUpdateDynamicNet2(nn.Cell):
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def __init__(self, inputx):
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super(TestScatterUpdateDynamicNet2, self).__init__()
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self.scatter_update = P.ScatterUpdate()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.inputx = Parameter(inputx, name="inputx")
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def construct(self, indices, updates):
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indices = self.test_dynamic(indices)
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updates = self.test_dynamic(updates)
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out = self.scatter_update(self.inputx, indices, updates)
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return out
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def scatter_update_d2_net(inputx, indices_1, updates_1,
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indices_2, updates_2):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = TestScatterUpdateDynamicNet2(inputx)
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out1 = net(indices_1, updates_1)
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out2 = net(indices_2, updates_2)
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return (out1, out2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_small_float32():
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inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[0., 1., 2.],
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[3., 4., 5.]])
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_input_updated():
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inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
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net = TestScatterUpdateNet(inputx, indices, updates)
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net()
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expected = np.array([[0., 1., 2.],
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[3., 4., 5.]])
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np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_input_less_than_1_float32():
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inputx = Tensor(np.array([[0.214141, 0.415151, 0.51516],
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[0.876542, 0.451611, 0.55112],
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[0.111244, 0.633333, 0.34444]]).astype(np.float32))
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indices = Tensor(np.array([1, 0, 2]).astype(np.int32))
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updates = Tensor(np.arange(34, 43).reshape((3, 3)).astype(np.float32))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[37., 38., 39.],
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[34., 35., 36.],
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[40., 41., 42.]], dtype=np.float32)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_float16():
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inputx = Tensor(np.zeros((2, 3)).astype(np.float16))
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float16))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[0., 1., 2.],
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[3., 4., 5.]]).astype(np.float16)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_int32():
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inputx = Tensor(np.zeros((2, 3)).astype(np.int32))
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.int32))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[0., 1., 2.],
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[3., 4., 5.]]).astype(np.int32)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_large_float16():
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inputx = Tensor(np.zeros((4, 3)).astype(np.float16))
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indices = Tensor(np.array([[2, 1], [0, 3]]).astype(np.int32))
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updates = Tensor(np.arange(63, 75).reshape((2, 2, 3)).astype(np.float16))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[69., 70., 71.],
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[66., 67., 68.],
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[63., 64., 65.],
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[72., 73., 74.]]).astype(np.float16)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_disordered_float16():
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inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.float16)))
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indices = Tensor(np.array([1, 2]).astype(np.int32))
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updates = Tensor(np.arange(63, 71).reshape((2, 4)).astype(np.float16))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[45., 44., 43., 42.],
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[63., 64., 65., 66.],
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[67., 68., 69., 70.]]).astype(np.float16)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_disordered_int32():
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inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int32)))
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indices = Tensor(np.array([1, 2]).astype(np.int32))
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updates = Tensor(np.arange(63, 71).reshape((2, 4)).astype(np.int32))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[45., 44., 43., 42.],
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[63., 64., 65., 66.],
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[67., 68., 69., 70.]]).astype(np.int32)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_large_shape_float16():
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inputx = Tensor(np.arange(96).reshape((4, 2, 3, 4)).astype(np.float16))
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indices = Tensor(np.array([1, 0]).astype(np.int32))
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updates = Tensor(np.flip(np.arange(48).reshape((2, 2, 3, 4)).astype(np.float16)))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[[[23., 22., 21., 20.],
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[19., 18., 17., 16.],
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[15., 14., 13., 12.]],
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[[11., 10., 9., 8.],
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[7., 6., 5., 4.],
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[3., 2., 1., 0.]]],
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[[[47., 46., 45., 44.],
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[43., 42., 41., 40.],
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[39., 38., 37., 36.]],
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[[35., 34., 33., 32.],
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[31., 30., 29., 28.],
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[27., 26., 25., 24.]]],
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[[[48., 49., 50., 51.],
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[52., 53., 54., 55.],
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[56., 57., 58., 59.]],
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[[60., 61., 62., 63.],
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[64., 65., 66., 67.],
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[68., 69., 70., 71.]]],
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[[[72., 73., 74., 75.],
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[76., 77., 78., 79.],
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[80., 81., 82., 83.]],
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[[84., 85., 86., 87.],
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[88., 89., 90., 91.],
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[92., 93., 94., 95.]]]]).astype(np.float16)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_disordered_int8():
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inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int8)))
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indices = Tensor(np.array([1, 2]).astype(np.int32))
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updates = Tensor(np.arange(63, 71).reshape((2, 4)).astype(np.int8))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[45., 44., 43., 42.],
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[63., 64., 65., 66.],
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[67., 68., 69., 70.]]).astype(np.int8)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_large_shape_int8():
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inputx = Tensor(np.arange(96).reshape((4, 2, 3, 4)).astype(np.int8))
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indices = Tensor(np.array([1, 0]).astype(np.int32))
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updates = Tensor(np.flip(np.arange(48).reshape((2, 2, 3, 4)).astype(np.int8)))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[[[23., 22., 21., 20.],
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[19., 18., 17., 16.],
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[15., 14., 13., 12.]],
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[[11., 10., 9., 8.],
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[7., 6., 5., 4.],
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[3., 2., 1., 0.]]],
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[[[47., 46., 45., 44.],
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[43., 42., 41., 40.],
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[39., 38., 37., 36.]],
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[[35., 34., 33., 32.],
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[31., 30., 29., 28.],
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[27., 26., 25., 24.]]],
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[[[48., 49., 50., 51.],
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[52., 53., 54., 55.],
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[56., 57., 58., 59.]],
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[[60., 61., 62., 63.],
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[64., 65., 66., 67.],
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[68., 69., 70., 71.]]],
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[[[72., 73., 74., 75.],
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[76., 77., 78., 79.],
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[80., 81., 82., 83.]],
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[[84., 85., 86., 87.],
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[88., 89., 90., 91.],
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[92., 93., 94., 95.]]]]).astype(np.int8)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_large_uint8():
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inputx = Tensor(np.zeros((4, 3)).astype(np.uint8))
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indices = Tensor(np.array([[2, 1], [0, 3]]).astype(np.int32))
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updates = Tensor(np.arange(63, 75).reshape((2, 2, 3)).astype(np.uint8))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[69., 70., 71.],
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[66., 67., 68.],
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[63., 64., 65.],
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[72., 73., 74.]]).astype(np.uint8)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_disordered_uint8():
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inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.uint8)))
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indices = Tensor(np.array([1, 2]).astype(np.int32))
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updates = Tensor(np.arange(63, 71).reshape((2, 4)).astype(np.uint8))
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output = scatter_update_net(inputx, indices, updates)
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expected = np.array([[45., 44., 43., 42.],
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[63., 64., 65., 66.],
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[67., 68., 69., 70.]]).astype(np.uint8)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_large_shape_dynamic_int8():
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inputx = Tensor(np.arange(96).reshape((4, 2, 3, 4)).astype(np.int8))
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indices = Tensor(np.array([1, 0]).astype(np.int32))
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updates = Tensor(np.flip(np.arange(48).reshape((2, 2, 3, 4)).astype(np.int8)))
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output = scatter_update_d_net(inputx, indices, updates)
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expected = np.array([[[[23., 22., 21., 20.],
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[19., 18., 17., 16.],
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[15., 14., 13., 12.]],
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[[11., 10., 9., 8.],
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[7., 6., 5., 4.],
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[3., 2., 1., 0.]]],
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[[[47., 46., 45., 44.],
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[43., 42., 41., 40.],
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[39., 38., 37., 36.]],
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[[35., 34., 33., 32.],
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[31., 30., 29., 28.],
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[27., 26., 25., 24.]]],
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[[[48., 49., 50., 51.],
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[52., 53., 54., 55.],
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[56., 57., 58., 59.]],
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[[60., 61., 62., 63.],
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[64., 65., 66., 67.],
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[68., 69., 70., 71.]]],
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[[[72., 73., 74., 75.],
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[76., 77., 78., 79.],
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[80., 81., 82., 83.]],
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[[84., 85., 86., 87.],
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[88., 89., 90., 91.],
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[92., 93., 94., 95.]]]]).astype(np.int8)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_disordered_dynamic_int32():
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inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int32)))
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indices = Tensor(np.array([1, 2]).astype(np.int32))
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updates = Tensor(np.arange(63, 71).reshape((2, 4)).astype(np.int32))
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output = scatter_update_d_net(inputx, indices, updates)
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expected = np.array([[45., 44., 43., 42.],
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[63., 64., 65., 66.],
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[67., 68., 69., 70.]]).astype(np.int32)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scatter_update_two_inputs():
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inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
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indices_1 = Tensor(np.array([0, 1]).astype(np.int32))
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updates_1 = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
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indices_2 = Tensor(np.array([1]).astype(np.int32))
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updates_2 = Tensor(np.arange(34, 37).reshape((1, 3)).astype(np.float32))
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output_1, output_2 = scatter_update_d2_net(inputx, indices_1, updates_1,
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indices_2, updates_2)
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expected_1 = np.array([[0., 1., 2.],
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[3., 4., 5.]], dtype=np.float32)
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expected_2 = np.array([[0., 1., 2.],
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[34., 35., 36.]], dtype=np.float32)
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np.testing.assert_array_almost_equal(output_1.asnumpy(), expected_1)
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np.testing.assert_array_almost_equal(output_2.asnumpy(), expected_2)
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