forked from mindspore-Ecosystem/mindspore
143 lines
5.6 KiB
Python
143 lines
5.6 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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import numpy as np
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import pytest
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from mindspore import context, nn, Tensor
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.common.parameter import Parameter
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import mindspore.common.dtype as mstype
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grad_all = C.GradOperation(get_all=True)
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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_gpu_training
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@pytest.mark.env_onecard
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def test_while_grad_with_memory_optimize():
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"""
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Feature: Integration of dynamic and static memory.
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Description: Test the control flow scene.
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Expectation: The result meet expectation.
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"""
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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def construct(self, idx, end, x):
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while idx < end:
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part = x[idx, :, :]
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max_num = self.max(part)
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x[idx, :, 0:2] = max_num
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idx = idx + 1
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return x
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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idx = Tensor(np.array(0), dtype=mstype.int32)
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end = Tensor(np.array(2), dtype=mstype.int32)
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input_x = np.array([[[4, 0], [0, 0]],
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[[0, 4], [0, 0]]]).astype(np.float32)
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x = Tensor(input_x, dtype=mstype.float32)
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# memory optimize mode
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context.set_context(mode=context.GRAPH_MODE, memory_optimize_level="O1")
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while_net = MyWhileNet()
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net = GradNet(while_net)
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graph_output = net(idx, end, x)
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expect_zero = np.array([0], dtype=np.float32)
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expect_two = input_x
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assert np.allclose(graph_output[0].asnumpy(), expect_zero, 0.0001, 0.0001)
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assert np.allclose(graph_output[1].asnumpy(), expect_zero, 0.0001, 0.0001)
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assert np.allclose(graph_output[2].asnumpy(), expect_two, 0.0001, 0.0001)
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class SparseApplyFtrlNet(nn.Cell):
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def __init__(self, var, accum, linear, lr=0.001, l1=0.0, l2=0.0, lr_power=-0.5):
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super(SparseApplyFtrlNet, self).__init__()
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self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=lr, l1=l1, l2=l2, lr_power=lr_power)
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self.var = Parameter(var, name="var")
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self.accum = Parameter(accum, name="accum")
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self.linear = Parameter(linear, name="linear")
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def construct(self, grad, indices):
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out = self.sparse_apply_ftrl(self.var, self.accum, self.linear, grad, indices)
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return out
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_sparse_apply_ftrl_with_memory_optimize():
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"""
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Feature: Integration of dynamic and static memory.
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Description: Test the scene of output ref node.
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Expectation: The result meet expectation.
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"""
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context.set_context(mode=context.GRAPH_MODE, memory_optimize_level="O1")
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grad_np = np.ones([3, 3, 3])
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indice_np = [0, 1, 2]
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var_np = np.ones([3, 3, 3])
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accum_np = np.ones([3, 3, 3])
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linear_np = np.ones([3, 3, 3])
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# test1: var/accum/linear/gradient are float32 and indices is int32.
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gradient = Tensor(grad_np, dtype=mstype.float32)
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indices = Tensor(indice_np, dtype=mstype.int32)
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var = Tensor(var_np, dtype=mstype.float32)
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accum = Tensor(accum_np, dtype=mstype.float32)
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linear = Tensor(linear_np, dtype=mstype.float32)
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sparse_apply_ftrl = SparseApplyFtrlNet(var, accum, linear)
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out = sparse_apply_ftrl(gradient, indices)
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expect_var = np.array([[[0.291479, 0.291479, 0.291479],
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[0.291479, 0.291479, 0.291479],
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[0.291479, 0.291479, 0.291479]],
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[[0.291479, 0.291479, 0.291479],
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[0.291479, 0.291479, 0.291479],
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[0.291479, 0.291479, 0.291479]],
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[[0.291479, 0.291479, 0.291479],
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[0.291479, 0.291479, 0.291479],
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[0.291479, 0.291479, 0.291479]]]).astype(np.float32)
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assert np.all(out[0].asnumpy() == expect_var)
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# test2: var/accum/linear/gradient are float16 and indices is int32.
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gradient = Tensor(grad_np, dtype=mstype.float16)
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indices = Tensor(indice_np, dtype=mstype.int32)
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var = Tensor(var_np, dtype=mstype.float16)
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accum = Tensor(accum_np, dtype=mstype.float16)
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linear = Tensor(linear_np, dtype=mstype.float16)
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sparse_apply_ftrl = SparseApplyFtrlNet(var, accum, linear)
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out = sparse_apply_ftrl(gradient, indices)
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expect_var = np.array([[[0.2915, 0.2915, 0.2915],
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[0.2915, 0.2915, 0.2915],
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[0.2915, 0.2915, 0.2915]],
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[[0.2915, 0.2915, 0.2915],
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[0.2915, 0.2915, 0.2915],
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[0.2915, 0.2915, 0.2915]],
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[[0.2915, 0.2915, 0.2915],
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[0.2915, 0.2915, 0.2915],
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[0.2915, 0.2915, 0.2915]]]).astype(np.float16)
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assert np.all(out[0].asnumpy() == expect_var)
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