forked from mindspore-Ecosystem/mindspore
227 lines
9.4 KiB
Python
227 lines
9.4 KiB
Python
# Copyright 2020 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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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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import mindspore.common.dtype as mstype
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.001,
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l1=0.0,
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l2=0.0,
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lr_power=-0.5,
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use_locking=False)
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self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)),
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name="var")
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self.accum = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)),
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name="accum")
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self.linear = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)),
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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,
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indices)
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return out
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class NetHalf(nn.Cell):
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def __init__(self):
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super(NetHalf, self).__init__()
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self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.001,
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l1=0.0,
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l2=0.0,
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lr_power=-0.5,
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use_locking=False)
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self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float16)),
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name="var")
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self.accum = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float16)),
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name="accum")
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self.linear = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float16)),
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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,
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indices)
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return out
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def dyn_case():
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net = Net()
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grad_dyn = Tensor(shape=[3, None, None], dtype=mstype.float32)
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indices_dyn = Tensor(shape=[None], dtype=mstype.int32)
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net.set_inputs(grad_dyn, indices_dyn)
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grad = Tensor(np.ones([3, 3, 3]).astype(np.float32))
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indices = Tensor([0, 1, 2], mstype.int32)
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out = net(grad, indices)
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expect_shape = (3, 3, 3)
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for i in range(3):
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assert out[i].asnumpy().shape == expect_shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_sparse_apply_ftrl_dyn():
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"""
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Feature: test SparseApplyFtrl in PyNative and Graph modes.
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Description: test dynamic shape case.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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dyn_case()
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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dyn_case()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ftrl():
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gradient = Tensor(np.ones([3, 3, 3]).astype(np.float32))
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indices = Tensor([0, 1, 2], mstype.int32)
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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,
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0.291479]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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sparse_apply_ftrl = Net()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sparse_apply_ftrl = Net()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ftrl_sparse_int64_ind():
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gradient = Tensor(np.ones([2, 3, 3]).astype(np.float32))
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indices = Tensor([0, 2], mstype.int64)
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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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[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
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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,
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0.291479]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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sparse_apply_ftrl = Net()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sparse_apply_ftrl = Net()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ftrl_half():
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gradient = Tensor(np.ones([3, 3, 3]).astype(np.float16))
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indices = Tensor([0, 1, 2], mstype.int32)
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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,
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0.291479]]]).astype(np.float16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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sparse_apply_ftrl = NetHalf()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sparse_apply_ftrl = NetHalf()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ftrl_sparse_half_int64_ind():
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gradient = Tensor(np.ones([2, 3, 3]).astype(np.float16))
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indices = Tensor([0, 2], mstype.int64)
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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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[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
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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,
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0.291479]]]).astype(np.float16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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sparse_apply_ftrl = NetHalf()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sparse_apply_ftrl = NetHalf()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(sparse_apply_ftrl.var.data.asnumpy() == expect_var)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ftrl_half_return_output():
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gradient = Tensor(np.ones([3, 3, 3]).astype(np.float16))
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indices = Tensor([0, 1, 2], mstype.int32)
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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,
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0.291479]]]).astype(np.float16)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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sparse_apply_ftrl = NetHalf()
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output = sparse_apply_ftrl(gradient, indices)
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assert np.all(output[0].asnumpy() == expect_var)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sparse_apply_ftrl = NetHalf()
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sparse_apply_ftrl(gradient, indices)
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assert np.all(output[0].asnumpy() == expect_var)
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