mindspore2022/tests/st/ops/gpu/test_pyfunc_op.py

# 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.
# ============================================================================
""" test loss """
import numpy as np
import pytest

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

def func_single_output(x1, x2):
    return x1 - x2

def func_multi_output(x1, x2):
    return (x1 + x2), (x1 - x2)

output = 0
def func_no_output(x1, x2):
    global output
    output = x1 + x2

class PyFuncNet(nn.Cell):
    def __init__(self, fn, in_types, in_shapes, out_types, out_shapes):
        super().__init__()
        self.func = P.PyFunc(fn, in_types, in_shapes, out_types, out_shapes)
        self.relu = P.ReLU()

    def construct(self, x1, x2):
        x = self.func((x1, x2))
        return self.relu(x[0])


def func_with_dtype(ms_dtype, np_dtype):
    shape = (40, 40)
    np.random.seed(42)
    x1 = np.random.randint(-5, 5, size=shape).astype(np_dtype)
    x2 = np.random.randint(-5, 5, size=shape).astype(np_dtype)

    expect = func_single_output(x1, x2)
    expect = P.ReLU()(Tensor(expect))

    net = PyFuncNet(func_single_output, [ms_dtype, ms_dtype], [shape, shape], [ms_dtype], [shape])
    x = net(Tensor(x1), Tensor(x2))
    assert np.allclose(x.asnumpy(), expect.asnumpy())


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pyfunc_single_output():
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
    func_with_dtype(ms.float16, np.float16)
    func_with_dtype(ms.float32, np.float32)
    func_with_dtype(ms.float64, np.float64)
    func_with_dtype(ms.int32, np.int32)
    func_with_dtype(ms.int64, np.int64)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pyfunc_multi_output():
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
    shape = (40, 40)
    dtype = ms.float32

    np.random.seed(42)
    x1 = np.random.randint(-5, 5, size=shape).astype(np.float32)
    x2 = np.random.randint(-5, 5, size=shape).astype(np.float32)
    expect, _ = func_multi_output(x1, x2)
    expect = P.ReLU()(Tensor(expect))

    net = PyFuncNet(func_multi_output, [dtype, dtype], [shape, shape], [dtype, dtype], [shape, shape])
    x = net(Tensor(x1), Tensor(x2))

    assert np.allclose(x.asnumpy(), expect.asnumpy())


class PyFuncGraph(nn.Cell):
    def __init__(self, fn, in_types, in_shapes, out_types, out_shapes):
        super().__init__()
        self.func = P.PyFunc(fn, in_types, in_shapes, out_types, out_shapes)

    def construct(self, x1, x2):
        return self.func((x1, x2))

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pyfunc_no_output():
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
    shape = (40, 40)
    dtype = ms.float32

    np.random.seed(42)
    x1 = np.random.randint(-5, 5, size=shape).astype(np.float32)
    x2 = np.random.randint(-5, 5, size=shape).astype(np.float32)
    func_no_output(x1, x2)
    global output
    expect = output

    net = PyFuncGraph(func_no_output, [dtype, dtype], [shape, shape], [], [])
    net(Tensor(x1), Tensor(x2))
    net_output = output

    assert np.allclose(net_output, expect)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pyfunc_scalar():
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
    shape = ()
    ms_dtype = ms.int32

    x1 = int(10)
    x2 = int(5)
    expect = func_single_output(x1, x2)

    net = PyFuncGraph(func_single_output, [ms_dtype, ms_dtype], [shape, shape], [ms_dtype], [shape])
    x = net(Tensor(x1), Tensor(x2))
    assert np.allclose(x[0].asnumpy(), expect)