forked from mindspore-Ecosystem/mindspore
121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
# Copyright 2021 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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from mindspore import Tensor
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from mindspore.nn import Cell
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import mindspore.ops as ops
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import mindspore.ops.operations as P
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def test_case_1():
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class Net1(Cell):
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def __init__(self):
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super(Net1, self).__init__()
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self.sub = ops.Sub()
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self.mul = ops.Mul()
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self.sum = ops.ReduceSum(keep_dims=False)
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self.add = ops.Add()
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self.pow = ops.Pow()
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def construct(self, x, y, z):
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t1 = self.sub(x, y)
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t2 = self.mul(t1, x)
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t3 = self.add(y, t2)
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t4 = self.add(t3, t3)
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t5 = z + 1.0
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t6 = self.sum(t4)
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t7 = self.add(t5, t6)
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return t7
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def get_output(x, y, z, net, enable_graph_kernel=False):
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context.set_context(enable_graph_kernel=enable_graph_kernel)
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net_obj = net()
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output = net_obj(x, y, z)
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return output
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N = 8
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x = Tensor(np.random.uniform(1, 2, [N, N, N]).astype(np.float32))
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y = Tensor(np.random.uniform(1, 2, [N, N, N]).astype(np.float32))
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z = Tensor(np.random.uniform(1, 2, [N, N, N]).astype(np.float32))
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expect = get_output(x, y, z, Net1, False)
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output = get_output(x, y, z, Net1, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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assert np.allclose(expect_np, output_np, 1.e-2, 1.e-2)
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def test_case_2():
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class Net2(Cell):
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def __init__(self):
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super(Net2, self).__init__()
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self.sqrt = P.Sqrt()
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self.sum = P.ReduceSum(keep_dims=True)
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self.add = P.Add()
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self.neg = P.Neg()
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def construct(self, x, y):
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sqrt_res = self.sqrt(x)
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add_res = self.add(y, sqrt_res)
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neg_res = self.neg(add_res)
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return neg_res
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def get_output(x, y, net, enable_graph_kernel=False):
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context.set_context(enable_graph_kernel=enable_graph_kernel)
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net_obj = net()
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output = net_obj(x, y)
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return output
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N = 16
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x = Tensor(np.random.uniform(1, 2, [N, N]).astype(np.float32))
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y = Tensor(np.random.uniform(1, 2, [N, N]).astype(np.float32))
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expect = get_output(x, y, Net2, False)
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output = get_output(x, y, Net2, True)
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expect_np = expect[0].asnumpy().copy()
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output_np = output[0].asnumpy().copy()
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assert np.allclose(expect_np, output_np, 1.e-2, 1.e-2)
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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_gpu_case_1():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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context.set_context(graph_kernel_flags="--enable_low_precision=true --disable_pass=highlevelopt2.atomic_clean")
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test_case_1()
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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_gpu_case_2():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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context.set_context(graph_kernel_flags="--enable_low_precision=true")
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test_case_2()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_ascend_case_1():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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context.set_context(graph_kernel_flags="--enable_low_precision=true --disable_pass=highlevelopt2.atomic_clean")
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test_case_1()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_ascend_case_2():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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context.set_context(graph_kernel_flags="--enable_low_precision=true")
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test_case_2()
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