forked from mindspore-Ecosystem/mindspore
215 lines
6.8 KiB
Python
215 lines
6.8 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 os
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import platform
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import numpy as np
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import pytest
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from mindspore import context, Tensor
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from mindspore.common import dtype as mstype
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from mindspore.nn import Cell
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import mindspore.ops as ops
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class JuliaTwoInputsNet(Cell):
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def __init__(self, func, out_shapes, out_types, reg=None):
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super(JuliaTwoInputsNet, self).__init__()
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self.program = ops.Custom(func, out_shapes, out_types, "julia", reg_info=reg)
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def construct(self, x, y):
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return self.program(x, y)
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class JuliaOneInputNet(Cell):
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def __init__(self, func, out_shapes, out_types, reg=None):
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super(JuliaOneInputNet, self).__init__()
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self.program = ops.Custom(func, out_shapes, out_types, "julia", reg_info=reg)
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def construct(self, x):
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return self.program(x)
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def add(x, y):
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"""
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function add for benchmark
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"""
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return x + y
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def sub(x, y):
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"""
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function sub for benchmark
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"""
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return x - y
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def matmul(x, y):
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"""
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function matmul for benchmark
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"""
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return np.matmul(x, y)
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def reducesum(x, axis=0, keepdims=True):
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return np.sum(x, axis=axis, keepdims=keepdims)
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def multiout(a, b):
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return a + b, a - b
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def julia_elemwise_test(func_name, bench):
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shape = (4, 5)
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input_x = np.random.normal(0, 1, shape).astype(np.float32)
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input_y = np.random.normal(0, 1, shape).astype(np.float32)
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func_path = os.path.dirname(os.path.abspath(__file__)) + "/julia_test_files/"
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try:
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test = JuliaTwoInputsNet(func_path + func_name, (shape,), (mstype.float32,))
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output = test(Tensor(input_x), Tensor(input_y))[0]
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except Exception as e:
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raise e
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assert np.allclose(bench(input_x, input_y), output.asnumpy(), 0.001, 0.001)
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def julia_matmul_test(func_name, bench):
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shape1 = (2, 3)
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shape2 = (3, 4)
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shape3 = (2, 4)
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input_x = np.random.normal(0, 1, shape1).astype(np.float32)
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input_y = np.random.normal(0, 1, shape2).astype(np.float32)
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func_path = os.path.dirname(os.path.abspath(__file__)) + "/julia_test_files/"
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try:
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test = JuliaTwoInputsNet(func_path + func_name, (shape3,), (mstype.float32,))
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output = test(Tensor(input_x), Tensor(input_y))[0]
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except Exception as e:
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raise e
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assert np.allclose(bench(input_x, input_y), output.asnumpy(), 0.001, 0.001)
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def julia_reducesum_test(func_name, bench):
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shape1 = (2, 3, 4)
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input_x = np.random.normal(0, 1, shape1).astype(np.float32)
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expect = bench(input_x, 1)
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func_path = os.path.dirname(os.path.abspath(__file__)) + "/julia_test_files/"
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try:
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test = JuliaOneInputNet(func_path + func_name, (expect.shape,), (mstype.float32,))
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output = test(Tensor(input_x))[0]
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except Exception as e:
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raise e
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assert np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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def julia_multiout_test(func_name, bench):
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shape = (4, 5)
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input_x = np.random.normal(0, 1, shape).astype(np.float32)
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input_y = np.random.normal(0, 1, shape).astype(np.float32)
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func_path = os.path.dirname(os.path.abspath(__file__)) + "/julia_test_files/"
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try:
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test = JuliaTwoInputsNet(func_path + func_name, (shape, shape,), (mstype.float32, mstype.float32,))
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output1 = test(Tensor(input_x), Tensor(input_y))[0]
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output2 = test(Tensor(input_x), Tensor(input_y))[1]
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except Exception as e:
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raise e
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expect1, expect2 = bench(input_x, input_y)
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assert np.allclose(expect1, output1.asnumpy(), 0.001, 0.001)
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assert np.allclose(expect2, output2.asnumpy(), 0.001, 0.001)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_julia_single_output_cpu_add():
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"""
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Feature: custom julia operator, multiple inputs, single output, CPU, GRAPH_MODE
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Description: pre-write xxx.jl, custom operator launches xxx.jl
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Expectation: nn result matches numpy result
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"""
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system = platform.system()
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if system != 'Linux':
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pass
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else:
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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julia_elemwise_test("add.jl:Add:foo!", add)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_julia_single_output_cpu_sub():
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"""
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Feature: custom julia operator, multiple inputs, single output, CPU, GRAPH_MODE
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Description: pre-write xxx.jl, custom operator launches xxx.jl
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Expectation: nn result matches numpy result
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"""
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system = platform.system()
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if system != 'Linux':
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pass
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else:
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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julia_elemwise_test("sub.jl:Sub:foo!", sub)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_julia_single_output_cpu_matmul():
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"""
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Feature: custom julia operator, multiple inputs, single output, CPU, GRAPH_MODE
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Description: pre-write xxx.jl, custom operator launches xxx.jl
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Expectation: nn result matches numpy result
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"""
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system = platform.system()
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if system != 'Linux':
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pass
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else:
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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julia_matmul_test("matmul.jl:Matmul:foo!", matmul)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_julia_single_output_cpu_reducesum():
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"""
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Feature: custom julia operator, multiple inputs, single output, CPU, GRAPH_MODE
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Description: pre-write xxx.jl, custom operator launches xxx.jl
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Expectation: nn result matches numpy result
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"""
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system = platform.system()
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if system != 'Linux':
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pass
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else:
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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julia_reducesum_test("reducesum.jl:ReduceSum:foo!", reducesum)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_julia_multi_output_cpu():
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"""
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Feature: custom julia operator, multiple inputs, multi output, CPU, GRAPH_MODE
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Description: pre-write xxx.jl, custom operator launches xxx.jl
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Expectation: nn result matches numpy result
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"""
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system = platform.system()
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if system != 'Linux':
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pass
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else:
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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julia_multiout_test("multi_output.jl:MultiOutput:foo!", multiout)
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