forked from mindspore-Ecosystem/mindspore
162 lines
7.0 KiB
Python
162 lines
7.0 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.api import ms_function
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from mindspore.common.parameter import Parameter
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class Net(nn.Cell):
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def __init__(self, decay_flag=True):
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super(Net, self).__init__()
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self.decay_flag = decay_flag
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self.op_mul = P.Mul()
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self.op_square = P.Square()
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self.op_sqrt = P.Sqrt()
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self.op_cast = P.Cast()
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self.op_reshape = P.Reshape()
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self.op_shape = P.Shape()
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self.param = Parameter(Tensor(np.array([1, 3, 5]).astype(np.float32)), name='param')
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self.m = Parameter(Tensor(np.array([0.11, 0.33, 0.55]).astype(np.float32)), name='m')
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self.v = Parameter(Tensor(np.array([1.2, 3.4, 5.6]).astype(np.float32)), name='v')
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@ms_function
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def construct(self, beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr):
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param_fp32 = self.op_cast(self.param, mstype.float32)
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m_fp32 = self.op_cast(self.m, mstype.float32)
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v_fp32 = self.op_cast(self.v, mstype.float32)
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gradient_fp32 = self.op_cast(gradient, mstype.float32)
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next_m = self.op_mul(beta1, m_fp32) + \
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self.op_mul(self.op_cast(one_sub_beta_1, mstype.float32), gradient_fp32)
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next_v = self.op_mul(beta2, v_fp32) + self.op_mul(self.op_cast(one_sub_beta_2,
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mstype.float32), self.op_square(gradient_fp32))
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update = next_m / (eps + self.op_sqrt(next_v))
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if self.decay_flag:
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update = self.op_mul(weight_decay_tensor, param_fp32) + update
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update_with_lr = self.op_mul(lr, update)
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next_param = param_fp32 - self.op_reshape(update_with_lr, self.op_shape(param_fp32))
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depend_v = F.depend(next_param, F.assign(self.param, next_param))
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depend_v = F.depend(depend_v, F.assign(self.m, next_m))
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depend_v = F.depend(depend_v, F.assign(self.v, next_v))
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return depend_v
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def CalFusedAdam(beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr, param, m, v,
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is_weight_decay=False):
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m_expect = beta1 * m + one_sub_beta_1 * gradient
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v_expect = beta2 * v + one_sub_beta_2 * gradient * gradient
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update = m_expect / (np.sqrt(v_expect) + eps)
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if is_weight_decay:
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update += weight_decay_tensor * param
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param_expect = param - lr * update
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return param_expect, m_expect, v_expect
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def test_adam():
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np.random.seed(0)
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beta1 = np.array([0.9]).astype(np.float32)
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beta2 = np.array([0.999]).astype(np.float32)
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one_sub_beta_1 = (np.array([1.0]) - np.array([0.9])).astype(np.float32)
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one_sub_beta_2 = (np.array([1.0]) - np.array([0.999])).astype(np.float32)
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lr = np.array([0.012]).astype(np.float32)
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eps = np.array([1e-6]).astype(np.float32)
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weight_decay_tensor = np.array([0.021]).astype(np.float32)
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gradient = np.array([0.01, 0.03, 0.05]).astype(np.float32)
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m = np.array([0.11, 0.33, 0.55]).astype(np.float32)
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v = np.array([1.2, 3.4, 5.6]).astype(np.float32)
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param = np.array([1, 3, 5]).astype(np.float32)
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is_weight_decay = False
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opt = Net(is_weight_decay)
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_ = opt(Tensor(beta1), Tensor(beta2), Tensor(one_sub_beta_1), Tensor(one_sub_beta_2), Tensor(gradient), Tensor(eps),
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Tensor(weight_decay_tensor), Tensor(lr))
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param_expect, m_expect, v_expect = CalFusedAdam(
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beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr,
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param, m, v, is_weight_decay)
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assert np.allclose(opt.param.data.asnumpy(), param_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
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assert np.allclose(opt.m.data.asnumpy(), m_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
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assert np.allclose(opt.v.data.asnumpy(), v_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
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def test_adam_weight_decay():
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np.random.seed(0)
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beta1 = np.array([0.9]).astype(np.float32)
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beta2 = np.array([0.999]).astype(np.float32)
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one_sub_beta_1 = (np.array([1.0]) - np.array([0.9])).astype(np.float32)
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one_sub_beta_2 = (np.array([1.0]) - np.array([0.999])).astype(np.float32)
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lr = np.array([0.012]).astype(np.float32)
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eps = np.array([1e-6]).astype(np.float32)
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weight_decay_tensor = np.array([0.021]).astype(np.float32)
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gradient = np.array([0.01, 0.03, 0.05]).astype(np.float32)
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m = np.array([0.11, 0.33, 0.55]).astype(np.float32)
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v = np.array([1.2, 3.4, 5.6]).astype(np.float32)
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param = np.array([1, 3, 5]).astype(np.float32)
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is_weight_decay = True
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opt = Net(is_weight_decay)
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_ = opt(Tensor(beta1), Tensor(beta2), Tensor(one_sub_beta_1), Tensor(one_sub_beta_2), Tensor(gradient), Tensor(eps),
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Tensor(weight_decay_tensor), Tensor(lr))
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param_expect, m_expect, v_expect = CalFusedAdam(
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beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr,
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param, m, v, is_weight_decay)
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assert np.allclose(opt.param.data.asnumpy(), param_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
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assert np.allclose(opt.m.data.asnumpy(), m_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
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assert np.allclose(opt.v.data.asnumpy(), v_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
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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_adam_gpu():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
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test_adam()
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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_adam_ascend():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
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test_adam()
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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_adam_weight_decay_gpu():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
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test_adam_weight_decay()
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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_adam_weight_decay_ascend():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
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test_adam_weight_decay()
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