forked from mindspore-Ecosystem/mindspore
405 lines
14 KiB
Python
405 lines
14 KiB
Python
# Copyright 2022 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
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor, Parameter, ParameterTuple
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from mindspore.ops.functional import vmap
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class NetIndexAdd(nn.Cell):
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def __init__(self, x, axis):
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super(NetIndexAdd, self).__init__()
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self.input_x = Parameter(Tensor(x), name='x')
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self.index_add = ops.IndexAdd(axis)
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def construct(self, idx, y):
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return self.index_add(self.input_x, idx, y)
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def index_add_forward(nptype):
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x = np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(nptype)
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y = np.ones((2, 2, 4), dtype=nptype)
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idx = np.array([0, 2]).astype(np.int32)
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axis = 1
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expect = np.copy(x)
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expect[:, idx, :] = expect[:, idx, :] + y
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net = NetIndexAdd(x, axis)
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output = net(Tensor(idx), Tensor(y))
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_float64():
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"""
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Feature: test IndexAdd forward.
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Description: test float64 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_forward(np.float64)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_forward(np.float64)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_float16():
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"""
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Feature: test IndexAdd forward.
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Description: test float16 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_forward(np.float16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_forward(np.float16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_int32():
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"""
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Feature: test IndexAdd forward.
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Description: test int32 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_forward(np.int32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_forward(np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_int16():
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"""
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Feature: test IndexAdd forward.
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Description: test int16 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_forward(np.int16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_forward(np.int16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_int8():
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"""
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Feature: test IndexAdd forward.
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Description: test int8 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_forward(np.int8)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_forward(np.int8)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_uint8():
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"""
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Feature: test IndexAdd forward.
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Description: test uint8 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_forward(np.uint8)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_forward(np.uint8)
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class IndexAddGradNet(nn.Cell):
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def __init__(self, network):
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super(IndexAddGradNet, self).__init__()
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self.grad = ops.GradOperation(get_all=True, sens_param=True, get_by_list=True)
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self.network = network
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self.params = ParameterTuple(network.trainable_params())
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def construct(self, idx, y, dout):
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out = self.grad(self.network, self.params)(idx, y, dout)
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return out
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def index_add_grad_with_type(nptype):
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x = np.arange(15).reshape(5, 3).astype(nptype)
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net = NetIndexAdd(x, 1)
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grad_net = IndexAddGradNet(net)
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y = Tensor(np.arange(5).reshape(5, 1).astype(nptype))
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dout = Tensor(np.array([[63., 64., 65.],
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[66., 67., 68.],
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[69., 70., 71.],
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[72., 73., 74.],
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[75., 76., 77.]]).astype(nptype))
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index = Tensor(np.array([1]), dtype=mindspore.int32)
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output = grad_net(index, y, dout)
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ygrad = output[0][1]
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xgrad = output[1][0]
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expect_xgrad = np.array([[63., 64., 65.],
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[66., 67., 68.],
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[69., 70., 71.],
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[72., 73., 74.],
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[75., 76., 77.]]).astype(nptype)
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expect_ygrad = np.array([[64.],
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[67.],
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[70.],
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[73.],
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[76.]]).astype(nptype)
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np.testing.assert_array_equal(xgrad.asnumpy(), expect_xgrad)
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np.testing.assert_array_equal(ygrad.asnumpy(), expect_ygrad)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_float64():
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"""
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Feature: test IndexAdd backward.
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Description: test float64 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.float64)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.float64)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_float32():
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"""
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Feature: test IndexAdd backward.
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Description: test float32 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_float16():
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"""
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Feature: test IndexAdd backward.
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Description: test float16 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.float16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.float16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_int32():
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"""
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Feature: test IndexAdd backward.
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Description: test int32 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.int32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_int16():
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"""
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Feature: test IndexAdd backward.
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Description: test int16 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.int16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.int16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_int8():
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"""
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Feature: test IndexAdd backward.
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Description: test int8 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.int8)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.int8)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_grad_uint8():
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"""
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Feature: test IndexAdd backward.
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Description: test uint8 inputs.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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index_add_grad_with_type(np.uint8)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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index_add_grad_with_type(np.uint8)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_function():
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"""
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Feature: test IndexAdd function interface.
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Description: test interface.
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Expectation: the result match with numpy result
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"""
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context.set_context(device_target="CPU")
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x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32), name="name_x")
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indices = Tensor(np.array([0, 2]), mindspore.int32)
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y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
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output = ops.index_add(x, indices, y, 1)
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expect = np.array([[1.5, 2, 4], [5, 5, 7.5], [9, 8, 11.5]])
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np.testing.assert_array_equal(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_dynamic():
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"""
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Feature: test IndexAdd dynamic shape.
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Description: input y is dynamic shape.
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Expectation: the result match with numpy result
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"""
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x = np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(np.float32)
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y = np.ones((2, 2, 4), dtype=np.float32)
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idx = np.array([0, 2]).astype(np.int32)
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axis = 1
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expect = np.copy(x)
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expect[:, idx, :] = expect[:, idx, :] + y
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y_dyn = Tensor(shape=[2, None, 4], dtype=mindspore.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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net = NetIndexAdd(x, axis)
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net.set_inputs(Tensor(idx), y_dyn)
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output = net(Tensor(idx), Tensor(y))
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = NetIndexAdd(x, axis)
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net.set_inputs(Tensor(idx), y_dyn)
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output = net(Tensor(idx), Tensor(y))
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assert (output.asnumpy() == expect).all()
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def vmap_case():
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class Net(nn.Cell):
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def __init__(self, axis):
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super(Net, self).__init__()
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self.index_add = ops.IndexAdd(axis)
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def construct(self, a, idx, b):
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return self.index_add(a, idx, b)
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class WrapNet(nn.Cell):
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def __init__(self, net, a, in_axes, out_axes):
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super(WrapNet, self).__init__()
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self.net = net
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self.a = a
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self.in_axes = in_axes
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self.out_axes = out_axes
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def construct(self, idx, b):
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return vmap(self.net, self.in_axes, self.out_axes)(self.a, idx, b)
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# batch dimension of x and y is same, batch dimension <= axis
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x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)))
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indices = Tensor(np.array([0, 2], dtype=np.int32))
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y = Tensor(np.array([[0.5, 1], [1, 1.5], [2, 2.5]], dtype=np.float32))
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output = WrapNet(Net(0), x, (0, None, 0), 0)(indices, y)
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expect = np.array([[1.5, 2, 4], [5, 5, 7.5], [9, 8, 11.5]], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expect)
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# batch dimension of x and y is different, batch dimension <= axis
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x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)))
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indices = Tensor(np.array([0, 2], dtype=np.int32))
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y = Tensor(np.array([[0.5, 1, 2], [1, 1.5, 2.5]], dtype=np.float32))
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output = WrapNet(Net(0), x, (0, None, 1), 0)(indices, y)
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expect = np.array([[1.5, 2, 4], [5, 5, 7.5], [9, 8, 11.5]], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expect)
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# batch dimension y is None
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x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)))
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indices = Tensor(np.array([0, 2], dtype=np.int32))
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y = Tensor(np.array([0.5, 1], dtype=np.float32))
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output = WrapNet(Net(0), x, (0, None, None), 0)(indices, y)
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expect = np.array([[1.5, 2, 4], [4.5, 5, 7], [7.5, 8, 10]], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expect)
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# batch dimension of x and y is same, batch dimension > axis > 0
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x = Parameter(Tensor(np.array([[[1, 1], [1, 1]],
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[[2, 2], [2, 2]],
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[[3, 3], [3, 3]]], dtype=np.float32)))
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indices = Tensor(np.array([0, 2], dtype=np.int32))
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y = Tensor(np.array([[[0, 0.5], [1, 1.5]], [[1.5, 2], [2.5, 3]]], dtype=np.float32))
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output = WrapNet(Net(0), x, (2, None, 2), 2)(indices, y)
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expect = np.array([[[1, 1.5], [2, 2.5]],
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[[2, 2], [2, 2]],
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[[4.5, 5], [5.5, 6]]], dtype=np.float32)
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assert np.allclose(output.asnumpy(), expect)
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# batch dimension of x and y is same, 0 > batch dimension > axis
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x = Parameter(Tensor(np.array([[[1, 1], [1, 1]],
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[[2, 2], [2, 2]],
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[[3, 3], [3, 3]]], dtype=np.float32)))
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output = WrapNet(Net(-2), x, (-1, None, -1), -1)(indices, y)
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assert np.allclose(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_index_add_vmap_cpu():
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"""
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Feature: test IndexAdd vmap on CPU.
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Description: inputs with batch.
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Expectation: the result match with expect
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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vmap_case()
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