add use case
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@ -13,9 +13,9 @@
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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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LOCAL_HIAI=/usr/local/HiAI
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export TBE_IMPL_PATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/impl/
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export LD_LIBRARY_PATH=${LOCAL_HIAI}/runtime/lib64/:${LD_LIBRARY_PATH}
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LOCAL_HIAI=/usr/local/Ascend
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export TBE_IMPL_PATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/impl/:${TBE_IMPL_PATH}
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export LD_LIBRARY_PATH=${LOCAL_HIAI}/runtime/lib64/:${LOCAL_HIAI}/add-ons/:${LD_LIBRARY_PATH}
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export PATH=${LOCAL_HIAI}/runtime/ccec_compiler/bin/:${PATH}
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export PYTHONPATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/:${PYTHONPATH}
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export DEVICE_MEMORY_CAPACITY=1073741824000
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@ -0,0 +1,354 @@
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# 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 os
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import numpy as np
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from mindspore.communication.management import init
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from mindspore.communication.management import release
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from mindspore.communication.management import get_rank
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from mindspore.communication.management import get_group_size
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from mindspore.nn import Cell
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from mindspore.nn import Conv2d
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from mindspore.nn import ReLU
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from mindspore.nn import Dense
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from mindspore.nn import Softmax
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import mindspore.ops.operations as P
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from mindspore.train.serialization import load_param_into_net
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from mindspore.train.callback import CheckpointConfig
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from mindspore.train.callback import ModelCheckpoint
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from mindspore.train.serialization import load_checkpoint
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from mindspore.nn import Momentum
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from mindspore.nn import SoftmaxCrossEntropyWithLogits
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from mindspore.train import Model
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from mindspore.parallel import set_algo_parameters
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from mindspore.common.initializer import initializer
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from mindspore.common import dtype as mstype
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from mindspore import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore import context
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from mindspore.context import ParallelMode
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context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
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def _count_unequal_element(data_expected, data_me, rtol, atol):
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assert data_expected.shape == data_me.shape
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total_count = len(data_expected.flatten())
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error = np.abs(data_expected - data_me)
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greater = np.greater(error, atol + np.abs(data_me) * rtol)
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loss_count = np.count_nonzero(greater)
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assert (loss_count / total_count) < rtol, \
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"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
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format(data_expected[greater], data_me[greater], error[greater])
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def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
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if np.any(np.isnan(data_expected)):
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assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
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elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
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_count_unequal_element(data_expected, data_me, rtol, atol)
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else:
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assert True
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def clean_all_ckpt_files(folder_path):
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if os.path.exists(folder_path):
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for file_name in os.listdir(folder_path):
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if file_name.endswith('.ckpt') or file_name.endswith('.meta'):
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os.remove(os.path.join(folder_path, file_name))
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def find_newest_ckpt_file(folder_path):
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ckpt_files = map(lambda f: os.path.join(folder_path, f),
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filter(lambda f: f.endswith('.ckpt'),
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os.listdir(folder_path)))
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return max(ckpt_files, key=os.path.getctime)
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class FakeDataInitMode:
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RandomInit = 0
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OnesInit = 1
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UniqueInit = 2
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ZerosInit = 3
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class FakeData:
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def __init__(self, size=1024, batch_size=32, image_size=(3, 224, 224),
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num_classes=10, random_offset=0, use_parallel=False,
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fakedata_mode=FakeDataInitMode.RandomInit):
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self.size = size
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self.rank_batch_size = batch_size
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self.total_batch_size = self.rank_batch_size
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self.random_offset = random_offset
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self.image_size = image_size
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self.num_classes = num_classes
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self.rank_size = 1
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self.rank_id = 0
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self.batch_index = 0
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self.image_data_type = np.float32
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self.label_data_type = np.float32
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self.is_onehot = True
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self.fakedata_mode = fakedata_mode
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if use_parallel is True:
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init(backend_name='hccl')
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self.rank_size = get_group_size()
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self.rank_id = get_rank()
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self.total_batch_size = self.rank_batch_size * self.rank_size
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assert (self.size % self.total_batch_size) == 0
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self.total_batch_data_size = (self.rank_size, self.rank_batch_size) + image_size
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def get_dataset_size(self):
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return int(self.size / self.total_batch_size)
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def get_repeat_count(self):
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return 1
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def set_image_data_type(self, data_type):
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self.image_data_type = data_type
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def set_label_data_type(self, data_type):
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self.label_data_type = data_type
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def set_label_onehot(self, is_onehot=True):
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self.is_onehot = is_onehot
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def create_tuple_iterator(self, num_epochs=-1):
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_ = num_epochs
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return self
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def __getitem__(self, batch_index):
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if batch_index * self.total_batch_size >= len(self):
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raise IndexError("{} index out of range".format(self.__class__.__name__))
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rng_state = np.random.get_state()
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np.random.seed(batch_index + self.random_offset)
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if self.fakedata_mode == FakeDataInitMode.OnesInit:
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img = np.ones(self.total_batch_data_size)
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elif self.fakedata_mode == FakeDataInitMode.ZerosInit:
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img = np.zeros(self.total_batch_data_size)
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elif self.fakedata_mode == FakeDataInitMode.UniqueInit:
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total_size = 1
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for i in self.total_batch_data_size:
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total_size = total_size * i
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img = np.reshape(np.arange(total_size) * 0.0001, self.total_batch_data_size)
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else:
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img = np.random.randn(*self.total_batch_data_size)
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target = np.random.randint(0, self.num_classes, size=(self.rank_size, self.rank_batch_size))
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np.random.set_state(rng_state)
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img = img[self.rank_id]
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target = target[self.rank_id]
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img_ret = img.astype(self.image_data_type)
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target_ret = target.astype(self.label_data_type)
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if self.is_onehot:
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target_onehot = np.zeros(shape=(self.rank_batch_size, self.num_classes))
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target_onehot[np.arange(self.rank_batch_size), target] = 1
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target_ret = target_onehot.astype(self.label_data_type)
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return Tensor(img_ret), Tensor(target_ret)
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def __len__(self):
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return self.size
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def __iter__(self):
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self.batch_index = 0
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return self
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def reset(self):
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self.batch_index = 0
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def __next__(self):
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if self.batch_index * self.total_batch_size < len(self):
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data = self[self.batch_index]
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self.batch_index += 1
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return data
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raise StopIteration
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class ParallelStrategySearchNet(Cell):
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def __init__(self, in_channel, out_channel, axis, input_shape, mul_size,
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test_size, prelu_size, transpose_b, matmul_size, num_class):
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super().__init__()
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mul_np = np.full(mul_size, 0.5, dtype=np.float32)
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self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
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bias_np = np.full((12,), 7.1, dtype=np.float32)
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self.bias = Parameter(Tensor(bias_np), name="bias")
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prelu_np = np.full(prelu_size, 0.8, dtype=np.float32)
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self.prelu_weight = Parameter(Tensor(prelu_np), name="prelu_weight")
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matmul_np = np.full(matmul_size, 1.1, dtype=np.float32)
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self.matmul_weight = Parameter(Tensor(matmul_np), name="matmul_weight")
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self.mul = P.Mul()
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self.conv = Conv2d(in_channels=in_channel, out_channels=out_channel,
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kernel_size=5, has_bias=True,
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weight_init='ones', bias_init='ones',
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pad_mode='valid')
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self.scalar = 0.5
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self.parameter = Parameter(
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initializer(0.5, test_size, dtype=mstype.float32),
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name='parameter')
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self.tensor = Tensor(np.full(test_size, 0.05, dtype=np.float32))
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self.softmax = Softmax(axis=axis)
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self.relu = ReLU()
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self.relu.relu.add_prim_attr("primitive_target", "CPU")
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self.reshape = P.Reshape()
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self.input_shape = input_shape
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self.equal = P.Equal()
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self.cast = P.Cast()
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self.concat = P.Concat(axis=1)
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self.reduce_sum = P.ReduceSum()
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self.bias_add = P.BiasAdd()
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self.cos = P.Cos()
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self.prelu = P.PReLU()
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self.matmul = P.MatMul(transpose_b=transpose_b)
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self.l2norm = P.L2Normalize(axis=(1 - axis))
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self.tensoradd = P.TensorAdd()
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self.strided_slice = P.StridedSlice()
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self.dense = Dense(in_channels=6,
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out_channels=num_class,
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weight_init='ones',
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bias_init='ones',
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has_bias=True)
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def construct(self, inputs):
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x = self.conv(inputs)
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x = self.softmax(x)
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x = self.relu(x)
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x = self.mul(x, self.mul_weight)
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x = self.reshape(x, self.input_shape)
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y = self.parameter * self.tensor * self.scalar
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z = self.equal(self.parameter, self.scalar)
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z = self.cast(z, mstype.float16)
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z = self.cast(z, mstype.float32)
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x = self.concat((x, y, z))
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x = self.reduce_sum(x, (2, 3))
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x = self.bias_add(x, self.bias)
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y = self.cos(x)
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y = self.prelu(y, self.prelu_weight)
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z = self.matmul(x, self.matmul_weight)
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z = self.l2norm(z)
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x = self.tensoradd(y, z)
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x = self.strided_slice(x, (0, 0), (32, 6), (1, 1))
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x = self.dense(x)
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return x
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class ParallelStrategySearchFactory:
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def __init__(self, standalone_mode_net, parallel_mode_net):
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self.standalone_mode_net = standalone_mode_net
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self.parallel_mode_net = parallel_mode_net
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self.parallel_ckpt = None
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self.standalone_ckpt = None
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self.global_rank_id = None
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self._set_parallel_env()
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self._init_parallel()
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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return
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def __del__(self):
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self._release_parallel()
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def _set_parallel_env(self):
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if 'RANK_ID' in os.environ:
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self.global_rank_id = int(os.environ['RANK_ID'])
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def _init_parallel(self):
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self._init_parallel_flag = False
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init(backend_name='hccl')
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self._init_parallel_flag = True
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def _release_parallel(self):
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if self._init_parallel_flag:
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release()
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def _model_train_and_save_ckpt(self, net, dataset, epoch):
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self.opt = Momentum(learning_rate=0.01, momentum=0.9, params=net.get_parameters())
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self.loss_fn = SoftmaxCrossEntropyWithLogits(reduction='mean')
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self.model = Model(network=net,
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loss_fn=self.loss_fn,
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optimizer=self.opt)
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ckpt_config = CheckpointConfig(keep_checkpoint_max=1)
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ckpt_path = './rank_{}_ckpt'.format(self.global_rank_id)
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ckpt_callback = ModelCheckpoint(prefix='parallel', directory=ckpt_path,
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config=ckpt_config)
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clean_all_ckpt_files(ckpt_path)
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self.model.train(epoch=epoch,
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train_dataset=dataset,
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callbacks=[ckpt_callback],
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dataset_sink_mode=False)
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newest_ckpt_file = find_newest_ckpt_file(ckpt_path)
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return load_checkpoint(newest_ckpt_file)
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def mindspore_auto_parallel_impl(self, dataset, epoch, device_num):
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parallel_mode_net = self.parallel_mode_net
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set_algo_parameters(fully_use_devices=False)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL,
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device_num=device_num)
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self.parallel_ckpt = self._model_train_and_save_ckpt(net=parallel_mode_net,
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dataset=dataset, epoch=epoch)
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context.reset_auto_parallel_context()
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def mindspore_standalone_impl(self, dataset, epoch):
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standalone_mode_net = self.standalone_mode_net
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.STAND_ALONE)
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self.standalone_ckpt = self._model_train_and_save_ckpt(net=standalone_mode_net,
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dataset=dataset, epoch=epoch)
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context.reset_auto_parallel_context()
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def checkpoint_cmp(self, inputs_np):
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standalone_net = self.standalone_mode_net
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load_param_into_net(standalone_net, self.standalone_ckpt)
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standalone_out = standalone_net(Tensor(inputs_np))
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parallel_net = self.standalone_mode_net
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load_param_into_net(parallel_net, self.parallel_ckpt)
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parallel_out = parallel_net(Tensor(inputs_np))
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allclose_nparray(standalone_out.asnumpy(), parallel_out.asnumpy(),
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0.001, 0.001)
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def test_auto_parallel_strategy_search_axis_1_basic():
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inputs_np = np.random.randn(32, 3, 224, 224).astype(np.float32)
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standalone_mode_net = ParallelStrategySearchNet(in_channel=3,
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out_channel=8, axis=1, input_shape=(32, 4, 110, -1),
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mul_size=(32, 1, 220, 220), test_size=(32, 4, 110, 880),
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prelu_size=(1,), transpose_b=True, matmul_size=(1, 12),
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num_class=12)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL)
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parallel_mode_net = ParallelStrategySearchNet(in_channel=3,
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out_channel=8, axis=1, input_shape=(32, 4, 110, -1),
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mul_size=(32, 1, 220, 220), test_size=(32, 4, 110, 880),
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prelu_size=(1,), transpose_b=True, matmul_size=(1, 12),
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num_class=12)
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parallel_mode_net.cos.shard(((2, 4),))
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parallel_mode_net.matmul.shard(((1, 2), (1, 2)))
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standalone_dataset = FakeData(size=128, batch_size=32,
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image_size=(3, 224, 224), num_classes=12)
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fact = ParallelStrategySearchFactory(standalone_mode_net=standalone_mode_net,
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parallel_mode_net=parallel_mode_net)
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fact.mindspore_standalone_impl(dataset=standalone_dataset, epoch=2)
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parallel_dataset = FakeData(size=128, batch_size=4,
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image_size=(3, 224, 224), use_parallel=True,
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num_classes=12)
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fact.mindspore_auto_parallel_impl(dataset=parallel_dataset,
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epoch=2, device_num=8)
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fact.checkpoint_cmp(inputs_np=inputs_np)
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@ -0,0 +1,52 @@
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#!/bin/bash
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# 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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set -e
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BASE_PATH=$(cd "$(dirname $0)"; pwd)
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CONFIG_PATH=/home/workspace/mindspore_config
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export DEVICE_NUM=8
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export RANK_SIZE=$DEVICE_NUM
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source ${BASE_PATH}/env.sh
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unset SLOG_PRINT_TO_STDOUT
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export MINDSPORE_HCCL_CONFIG_PATH=$CONFIG_PATH/hccl/rank_table_${DEVICE_NUM}p.json
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export LD_LIBRARY_PATH=/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}
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export ASCEND_OPP_PATH=/usr/local/Ascend/opp/
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process_pid=()
|
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for((i=0; i<$DEVICE_NUM; i++)); do
|
||||
rm -rf ${BASE_PATH}/parallel_strategy_search${i}
|
||||
mkdir ${BASE_PATH}/parallel_strategy_search${i}
|
||||
cp -r ${BASE_PATH}/parallel_strategy_search.py ${BASE_PATH}/parallel_strategy_search${i}/
|
||||
cd ${BASE_PATH}/parallel_strategy_search${i}
|
||||
export RANK_ID=${i}
|
||||
export DEVICE_ID=${i}
|
||||
echo "start training for device $i"
|
||||
env > env$i.log
|
||||
pytest -s -v parallel_strategy_search.py > parallel_strategy_search$i.log 2>&1 &
|
||||
process_pid[${i}]=`echo $!`
|
||||
done
|
||||
|
||||
for((i=0; i<${DEVICE_NUM}; i++)); do
|
||||
wait ${process_pid[i]}
|
||||
status=`echo $?`
|
||||
if [ "${status}" != "0" ]; then
|
||||
echo "[ERROR] test_parallel_strategy_search failed. status: ${status}"
|
||||
exit 1
|
||||
else
|
||||
echo "[INFO] test_parallel_strategy_search success."
|
||||
fi
|
||||
done
|
||||
|
||||
exit 0
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
import os
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_single
|
||||
def test_parallel_strategy_search():
|
||||
sh_path = os.path.split(os.path.realpath(__file__))[0]
|
||||
ret = os.system(f"sh {sh_path}/run_parallel_strategy_search.sh")
|
||||
os.system(f"grep -E 'ERROR|error' {sh_path}/parallel_strategy_search*/parallel_strategy_search*log -C 3")
|
||||
assert ret == 0
|
||||
Loading…
Reference in New Issue