mindspore/tests/st/ops/gpu/test_unique_op.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import numpy as np
import pytest

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P


class NetUnique(nn.Cell):
    def __init__(self):
        super(NetUnique, self).__init__()
        self.unique = P.Unique()

    def construct(self, x):
        x_unique, x_idx = self.unique(x)
        return x_unique, x_idx


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d():
    x = Tensor(np.array([4, 5, 1, 2, 3, 3, 4, 5]).astype(np.float32))
    exp_output = np.array([1, 2, 3, 4, 5]).astype(np.float32)
    exp_idx = np.array([3, 4, 0, 1, 2, 2, 3, 4]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d_float():
    x = Tensor(np.array([0.4, 0.5, 1.23, 2.2, 12.43, 12.43, 0.4, 0.5]).astype(np.float32))
    exp_output = np.array([0.4, 0.5, 1.23, 2.2, 12.43]).astype(np.float32)
    exp_idx = np.array([0, 1, 2, 3, 4, 4, 0, 1]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d_sorted():
    x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.float32))
    exp_output = np.array([1, 2, 4, 7, 8]).astype(np.float32)
    exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_zeros():
    x = Tensor(np.zeros(1000).astype(np.float32))
    exp_output = np.zeros(1).astype(np.float32)
    exp_idx = np.zeros(1000).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_large():
    x_np1 = np.arange(100)
    x_np2 = np.arange(100, 200)
    x_np3 = np.arange(200, 300)
    x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
    x = Tensor(x_np.astype(np.float32))
    exp_output = np.arange(300).astype(np.float32)
    exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d_half():
    x = Tensor(np.array([0.4, 0.5, 1.23, 2.2, 12.43, 12.43, 0.4, 0.5]).astype(np.float16))
    exp_output = np.array([0.4, 0.5, 1.23, 2.2, 12.43]).astype(np.float16)
    exp_idx = np.array([0, 1, 2, 3, 4, 4, 0, 1]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d_sorted_half():
    x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.float16))
    exp_output = np.array([1, 2, 4, 7, 8]).astype(np.float16)
    exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_zeros_half():
    x = Tensor(np.zeros(1000).astype(np.float16))
    exp_output = np.zeros(1).astype(np.float16)
    exp_idx = np.zeros(1000).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_large_half():
    x_np1 = np.arange(100)
    x_np2 = np.arange(100, 200)
    x_np3 = np.arange(200, 300)
    x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
    x = Tensor(x_np.astype(np.float16))
    exp_output = np.arange(300).astype(np.float16)
    exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d_int32():
    x = Tensor(np.array([4, 5, 1, 2, 3, 3, 4, 5]).astype(np.int32))
    exp_output = np.array([1, 2, 3, 4, 5]).astype(np.int32)
    exp_idx = np.array([3, 4, 0, 1, 2, 2, 3, 4]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_1d_sorted_int32():
    x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.int32))
    exp_output = np.array([1, 2, 4, 7, 8]).astype(np.int32)
    exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_zeros_int32():
    x = Tensor(np.zeros(1000).astype(np.int32))
    exp_output = np.zeros(1).astype(np.int32)
    exp_idx = np.zeros(1000).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_unique_large_int32():
    x_np1 = np.arange(100)
    x_np2 = np.arange(100, 200)
    x_np3 = np.arange(200, 300)
    x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
    x = Tensor(x_np.astype(np.int32))
    exp_output = np.arange(300).astype(np.int32)
    exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int32)
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetUnique()
    x_unique, x_idx = net(x)
    assert (x_unique.asnumpy() == exp_output).all()
    assert (x_idx.asnumpy() == exp_idx).all()