forked from mindspore-Ecosystem/mindspore
179 lines
7.5 KiB
Python
179 lines
7.5 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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import mindspore as ms
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import mindspore.ops.operations as P
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import mindspore.ops.operations._grad_ops as G
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from mindspore.ops.composite import GradOperation
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from mindspore import Tensor
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class GatherDNet(nn.Cell):
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def __init__(self, dim=0):
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super(GatherDNet, self).__init__()
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self.gather_d = P.GatherD()
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self.dim = dim
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def construct(self, x, index):
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return self.gather_d(x, self.dim, index)
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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_gather_grad_graph_int32_fp32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = Tensor(np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]), ms.float32)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int32)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float32)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float32)
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net = GatherDNet(dim)
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grad_net = GradOperation(get_all=True, sens_param=True)(net)
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output = grad_net(x, index, grad)
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error = 1e-4
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diff = output[0].asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_graph_int64_fp32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = Tensor(np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]), ms.float32)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int64)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float32)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float32)
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net = GatherDNet(dim)
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grad_net = GradOperation(get_all=True, sens_param=True)(net)
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output = grad_net(x, index, grad)
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error = 1e-4
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diff = output[0].asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_graph_int32_fp16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = Tensor(np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]), ms.float16)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int32)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float16)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float16)
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net = GatherDNet(dim)
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grad_net = GradOperation(get_all=True, sens_param=True)(net)
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output = grad_net(x, index, grad)
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error = 1e-4
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diff = output[0].asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_graph_int64_fp16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = Tensor(np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]), ms.float16)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int64)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float16)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float16)
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net = GatherDNet(dim)
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grad_net = GradOperation(get_all=True, sens_param=True)(net)
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output = grad_net(x, index, grad)
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error = 1e-4
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diff = output[0].asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_pynative_int32_fp32():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_shape = (2, 5)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int32)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float32)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float32)
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output = G.GatherDGrad(dim, x_shape)(index, grad)
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error = 1e-4
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_pynative_int64_fp32():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_shape = (2, 5)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int64)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float32)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float32)
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output = G.GatherDGrad(dim, x_shape)(index, grad)
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error = 1e-4
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_pynative_int32_fp16():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_shape = (2, 5)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int32)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float16)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float16)
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output = G.GatherDGrad(dim, x_shape)(index, grad)
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error = 1e-4
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_gather_grad_pynative_int64_fp16():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_shape = (2, 5)
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dim = 0
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index = Tensor(np.array([[0, 1, 1, 0, 0], [1, 0, 0, 1, 1]]), ms.int64)
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grad = Tensor(np.array([[0.9031, 0.0890, 0.2779, 0.3198, 0.5710],
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[0.6949, 0.8439, 0.2003, 0.6868, 0.4437]]), ms.float16)
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expect = np.array([[0.9031, 0.8439, 0.2003, 0.3198, 0.5710],
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[0.6949, 0.0890, 0.2779, 0.6868, 0.4437]], np.float16)
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output = G.GatherDGrad(dim, x_shape)(index, grad)
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error = 1e-4
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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