forked from mindspore-Ecosystem/mindspore
224 lines
7.2 KiB
Python
224 lines
7.2 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.common import dtype as mstype
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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#{cast} would be recompute and fused
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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.cast = P.Cast()
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self.sum = P.ReduceSum(keep_dims=False)
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def construct(self, x):
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cast_res = self.cast(x, mstype.float32)
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sum1_res = self.sum(cast_res, (0,))
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sum2_res = self.sum(cast_res, (1,))
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return sum1_res, sum2_res
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#{sqrt} would be recompute on Ascend
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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, x0, x1):
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sqrt_res = self.sqrt(x0)
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neg_res = self.neg(sqrt_res)
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add_res = self.add(x1, sqrt_res)
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sum_res = self.sum(add_res, (0,))
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return neg_res, sum_res
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#{sqrt} would be recompute
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class Net3(Cell):
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def __init__(self):
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super(Net3, self).__init__()
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self.sqrt = P.Sqrt()
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self.add = P.Add()
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self.neg = P.Neg()
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def construct(self, x0, x1):
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sqrt_res = self.sqrt(x0)
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neg_res = self.neg(sqrt_res)
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add_res = self.add(x1, sqrt_res)
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return neg_res, add_res
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#{sqrt neg} would be recompute
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class Net4(Cell):
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def __init__(self):
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super(Net4, self).__init__()
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self.sqrt = P.Sqrt()
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self.neg = P.Neg()
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self.sum = P.ReduceSum(keep_dims=False)
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def construct(self, x):
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sqrt_res = self.sqrt(x)
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neg_res = self.neg(sqrt_res)
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sum1_res = self.sum(neg_res, (0,))
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sum2_res = self.sum(neg_res, (1,))
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return sum1_res, sum2_res
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#{sqrt} would be recompute
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class Net5(Cell):
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def __init__(self):
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super(Net5, self).__init__()
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self.sqrt = P.Sqrt()
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self.add = P.Add()
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def construct(self, x0, x1, x2):
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sqrt_res = self.sqrt(x0)
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add1_res = self.add(sqrt_res, x1)
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add2_res = self.add(sqrt_res, x2)
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return add1_res, add2_res
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def test_basic1(net):
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def get_output(i0, 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(i0)
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return output
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i0 = Tensor(np.random.uniform(1, 2, [1024, 1024]).astype(np.float16))
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expect = get_output(i0, net, False)
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output = get_output(i0, net, True)
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expect0_np = expect[0].asnumpy().copy()
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output0_np = output[0].asnumpy().copy()
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expect1_np = expect[1].asnumpy().copy()
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output1_np = output[1].asnumpy().copy()
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assert np.allclose(expect0_np, output0_np, 1.e-3, 1.e-3)
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assert np.allclose(expect1_np, output1_np, 1.e-3, 1.e-3)
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def test_basic2(net):
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def get_output(i0, i1, 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(i0, i1)
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return output
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i0 = Tensor(np.random.uniform(1, 2, [1, 1024]).astype(np.float32))
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i1 = Tensor(np.random.uniform(1, 2, [1024, 1024]).astype(np.float32))
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expect = get_output(i0, i1, net, False)
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output = get_output(i0, i1, net, True)
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expect0_np = expect[0].asnumpy().copy()
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output0_np = output[0].asnumpy().copy()
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expect1_np = expect[1].asnumpy().copy()
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output1_np = output[1].asnumpy().copy()
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assert np.allclose(expect0_np, output0_np, 1.e-3, 1.e-3)
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assert np.allclose(expect1_np, output1_np, 1.e-3, 1.e-3)
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def test_basic3(net):
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def get_output(i0, i1, i2, 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(i0, i1, i2)
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return output
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i0 = Tensor(np.random.uniform(1, 2, [1, 1024]).astype(np.float16))
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i1 = Tensor(np.random.uniform(1, 2, [1024, 1024]).astype(np.float16))
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i2 = Tensor(np.random.uniform(1, 2, [2048, 1024]).astype(np.float16))
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expect = get_output(i0, i1, i2, net, False)
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output = get_output(i0, i1, i2, net, True)
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expect0_np = expect[0].asnumpy().copy()
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output0_np = output[0].asnumpy().copy()
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expect1_np = expect[1].asnumpy().copy()
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output1_np = output[1].asnumpy().copy()
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assert np.allclose(expect0_np, output0_np, 1.e-3, 1.e-3)
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assert np.allclose(expect1_np, output1_np, 1.e-3, 1.e-3)
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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_1():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_basic1(Net1)
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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_2():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_basic2(Net2)
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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_3():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_basic2(Net3)
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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_4():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_basic1(Net4)
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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_5():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_basic3(Net5)
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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_1():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic1(Net1)
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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_2():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic2(Net2)
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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_3():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic2(Net3)
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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_4():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic1(Net4)
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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_5():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic3(Net5)
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