st multi-hot

Signed-off-by: Daniel <chentanjie@huawei.com>
2021-01-29 15:51:30 +08:00 · 2021-01-29 15:51:30 +08:00 · 2833614a0b
parent 424e68a803
commit 2833614a0b
2 changed files with 341 additions and 0 deletions
--- a/tests/st/auto_parallel/multifieldembeddinglookup_parallel.py
+++ b/tests/st/auto_parallel/multifieldembeddinglookup_parallel.py
@ -0,0 +1,315 @@
+# Copyright 2021 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 numpy as np
+
+import mindspore.ops.operations as P
+from mindspore.nn import Cell
+from mindspore.nn import Adam
+from mindspore.nn import MultiFieldEmbeddingLookup as embedding
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train import Model
+from mindspore.train.callback import CheckpointConfig
+from mindspore.train.callback import ModelCheckpoint
+from mindspore.train.serialization import load_checkpoint
+from mindspore.train.serialization import load_param_into_net
+from mindspore.communication.management import init
+from mindspore.communication.management import release
+from mindspore.communication.management import get_rank
+from mindspore.communication.management import get_group_size
+from mindspore.context import ParallelMode
+
+
+context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+
+
+def _count_unequal_element(data_expected, data_me, rtol, atol):
+    assert data_expected.shape == data_me.shape
+    total_count = len(data_expected.flatten())
+    error = np.abs(data_expected - data_me)
+    greater = np.greater(error, atol + np.abs(data_me) * rtol)
+    loss_count = np.count_nonzero(greater)
+    assert (loss_count / total_count) < rtol, \
+        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
+            format(data_expected[greater], data_me[greater], error[greater])
+
+
+def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
+    if np.any(np.isnan(data_expected)):
+        assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
+    elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
+        _count_unequal_element(data_expected, data_me, rtol, atol)
+    else:
+        assert True
+
+def clean_all_ckpt_files(folder_path):
+    if os.path.exists(folder_path):
+        for file_name in os.listdir(folder_path):
+            if file_name.endswith('.ckpt') or file_name.endswith('.meta'):
+                os.remove(os.path.join(folder_path, file_name))
+
+
+def find_newest_ckpt_file(folder_path):
+    ckpt_files = map(lambda f: os.path.join(folder_path, f),
+                     filter(lambda f: f.endswith('.ckpt'),
+                            os.listdir(folder_path)))
+    return max(ckpt_files, key=os.path.getctime)
+
+
+class FakeDataInitMode:
+    RandomInit = 0
+    OnesInit = 1
+    UniqueInit = 2
+    ZerosInit = 3
+
+
+
+
+class FakeData:
+    def __init__(self, size=1024, batch_size=32, image_size=(3, 224, 224),
+                 num_classes=10, random_offset=0, use_parallel=False,
+                 fakedata_mode=FakeDataInitMode.RandomInit):
+        self.size = size
+        self.rank_batch_size = batch_size
+        self.total_batch_size = self.rank_batch_size
+        self.random_offset = random_offset
+        self.image_size = image_size
+        self.num_classes = num_classes
+        self.rank_size = 1
+        self.rank_id = 0
+        self.batch_index = 0
+        self.image_data_type = np.float32
+        self.label_data_type = np.float32
+        self.is_onehot = True
+        self.fakedata_mode = fakedata_mode
+
+        if use_parallel is True:
+            init(backend_name='nccl')
+            self.rank_size = get_group_size()
+            self.rank_id = get_rank()
+
+        self.total_batch_size = self.rank_batch_size * self.rank_size
+
+        assert (self.size % self.total_batch_size) == 0
+
+        self.total_batch_data_size = (self.rank_size, self.rank_batch_size) + image_size
+
+    def get_dataset_size(self):
+        return int(self.size / self.total_batch_size)
+
+    def get_repeat_count(self):
+        return 1
+
+    def set_image_data_type(self, data_type):
+        self.image_data_type = data_type
+
+    def set_label_data_type(self, data_type):
+        self.label_data_type = data_type
+
+    def set_label_onehot(self, is_onehot=True):
+        self.is_onehot = is_onehot
+
+    def create_tuple_iterator(self, num_epochs=-1, do_copy=True):
+        _ = num_epochs
+        return self
+
+    def __getitem__(self, batch_index):
+        if batch_index * self.total_batch_size >= len(self):
+            raise IndexError("{} index out of range".format(self.__class__.__name__))
+        rng_state = np.random.get_state()
+        np.random.seed(batch_index + self.random_offset)
+        if self.fakedata_mode == FakeDataInitMode.OnesInit:
+            img = np.ones(self.total_batch_data_size)
+        elif self.fakedata_mode == FakeDataInitMode.ZerosInit:
+            img = np.zeros(self.total_batch_data_size)
+        elif self.fakedata_mode == FakeDataInitMode.UniqueInit:
+            total_size = 1
+            for i in self.total_batch_data_size:
+                total_size = total_size * i
+            img = np.reshape(np.arange(total_size) * 0.0001, self.total_batch_data_size)
+        else:
+            img = np.random.randn(*self.total_batch_data_size)
+        target = np.random.randint(0, self.num_classes, size=(self.rank_size, self.rank_batch_size))
+        np.random.set_state(rng_state)
+        img = img[self.rank_id]
+        target = target[self.rank_id]
+        img_ret = img.astype(self.image_data_type)
+        target_ret = target.astype(self.label_data_type)
+        if self.is_onehot:
+            target_onehot = np.zeros(shape=(self.rank_batch_size, self.num_classes))
+            target_onehot[np.arange(self.rank_batch_size), target] = 1
+            target_ret = target_onehot.astype(self.label_data_type)
+        return Tensor(img_ret), Tensor(target_ret)
+
+    def __len__(self):
+        return self.size
+
+    def __iter__(self):
+        self.batch_index = 0
+        return self
+
+    def reset(self):
+        self.batch_index = 0
+
+    def __next__(self):
+        if self.batch_index * self.total_batch_size < len(self):
+            data = self[self.batch_index]
+            self.batch_index += 1
+            return data
+        raise StopIteration
+
+
+
+class MultiHotNet(Cell):
+    def __init__(self, vocab_size, embedding_size, field_size,
+                 param_init, target, slice_mode, sparse, operator, indices, field_ids):
+        super().__init__()
+        self.embedding = embedding(vocab_size=vocab_size,
+                                   embedding_size=embedding_size, field_size=field_size,
+                                   param_init=param_init, target=target, slice_mode=slice_mode,
+                                   sparse=sparse, operator=operator)
+        self.relu = P.ReLU()
+        self.indices = Tensor(indices)
+        self.field_ids = Tensor(field_ids)
+        if slice_mode == "table_column_slice":
+            self.relu.shard(((1, 1, 8),))
+        elif slice_mode == "table_row_slice":
+            self.relu.shard(((8, 1, 1),))
+        elif slice_mode == "batch_slice":
+            self.relu.shard(((8, 1, 1),))
+
+    def construct(self, values, label):
+        x = self.embedding(self.indices, values, self.field_ids)
+        output = self.relu(x)
+        return output
+
+
+class ParallelMultiHotFactory:
+    def __init__(self, vocab_size, embedding_size, field_size,
+                 param_init, target, slice_mode, sparse, operator, indices, field_ids):
+        self.vocab_size = vocab_size
+        self.embedding_size = embedding_size
+        self.field_size = field_size
+        self.param_init = param_init
+        self.target = target
+        self.slice_mode = slice_mode
+        self.sparse = sparse
+        self.operator = operator
+        self.indices = indices
+        self.field_ids = field_ids
+        self.global_rank_id = None
+        self.opt = None
+        self.model = None
+        self.standalone_ckpt = None
+        self.parallel_ckpt = None
+        self.loss_fn = None
+        self._init_parallel()
+        self._set_parallel_env()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        return
+
+    def __del__(self):
+        self._release_parallel()
+
+    def _set_parallel_env(self):
+        self.global_rank_id = get_rank()
+
+    def _init_parallel(self):
+        self._init_parallel_flag = False
+        init(backend_name='nccl')
+        self._init_parallel_flag = True
+
+    def _release_parallel(self):
+        release()
+
+    def _model_train_and_save_ckpt(self, net, dataset, epoch):
+        self.opt = Adam(params=net.get_parameters())
+        if self.target == 'CPU':
+            self.opt.target = self.target
+        if self.sparse:
+            context.set_context(enable_sparse=True)
+        self.model = Model(network=net,
+                           loss_fn=self.loss_fn,
+                           optimizer=self.opt)
+        ckpt_config = CheckpointConfig(keep_checkpoint_max=1)
+        ckpt_path = './rank_{}_ckpt'.format(self.global_rank_id)
+        ckpt_callback = ModelCheckpoint(prefix='parallel', directory=ckpt_path,
+                                        config=ckpt_config)
+        clean_all_ckpt_files(ckpt_path)
+        self.model.train(epoch=epoch,
+                         train_dataset=dataset,
+                         callbacks=[ckpt_callback],
+                         dataset_sink_mode=False)
+        newest_ckpt_file = find_newest_ckpt_file(ckpt_path)
+        return load_checkpoint(newest_ckpt_file)
+
+    def mindspore_auto_parallel_impl(self, dataset, epoch, device_num):
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL,
+                                          device_num=device_num)
+        parallel_mode_net = MultiHotNet(vocab_size=self.vocab_size, embedding_size=self.embedding_size,
+                                        field_size=self.field_size, param_init=self.param_init, target=self.target,
+                                        slice_mode=self.slice_mode, sparse=self.sparse, operator=self.operator,
+                                        indices=self.indices, field_ids=self.field_ids)
+        self.parallel_ckpt = self._model_train_and_save_ckpt(net=parallel_mode_net, epoch=epoch, dataset=dataset)
+
+    def mindspore_standalone_impl(self, epoch, dataset):
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.STAND_ALONE)
+        stand_alone_net = MultiHotNet(vocab_size=self.vocab_size, embedding_size=self.embedding_size,
+                                      field_size=self.field_size, param_init=self.param_init, target=self.target,
+                                      slice_mode=self.slice_mode, sparse=self.sparse, operator=self.operator,
+                                      indices=self.indices, field_ids=self.field_ids)
+        self.standalone_ckpt = self._model_train_and_save_ckpt(net=stand_alone_net,
+                                                               epoch=epoch, dataset=dataset)
+
+    def checkpoint_cmp(self, inputs_np, label):
+        standalone_net = MultiHotNet(vocab_size=self.vocab_size, embedding_size=self.embedding_size,
+                                     field_size=self.field_size, param_init=self.param_init, target=self.target,
+                                     slice_mode=self.slice_mode, sparse=self.sparse, operator=self.operator,
+                                     indices=self.indices, field_ids=self.field_ids)
+        parallel_net = MultiHotNet(vocab_size=self.vocab_size, embedding_size=self.embedding_size,
+                                   field_size=self.field_size, param_init=self.param_init, target=self.target,
+                                   slice_mode=self.slice_mode, sparse=self.sparse, operator=self.operator,
+                                   indices=self.indices, field_ids=self.field_ids)
+        load_param_into_net(standalone_net, self.standalone_ckpt)
+        load_param_into_net(parallel_net, self.parallel_ckpt)
+        standalone_out = standalone_net(Tensor(inputs_np), Tensor(label))
+        parallel_out = parallel_net(Tensor(inputs_np), Tensor(label))
+        allclose_nparray(standalone_out.asnumpy(), parallel_out.asnumpy(), 0.001, 0.001)
+
+def test_auto_parallel_multifieldembeddinglookup_device_table_column_slice_mean():
+    inputs_np = 10 * np.random.randn(64, 64).astype(np.float32)
+    label = 10 * np.random.randn(64, 64).astype(np.float32)
+    indices = np.random.randint(0, 9, (64, 64), np.int32)
+    field_ids = np.random.randint(0, 20, (64, 64), np.int32)
+    fact = ParallelMultiHotFactory(vocab_size=32, embedding_size=64, field_size=64, param_init='one', target='DEVICE',
+                                   slice_mode='table_column_slice', sparse=False, operator='MEAN',
+                                   indices=indices, field_ids=field_ids)
+
+    #stand alone
+    standalone_dataset = FakeData(size=64, batch_size=64, image_size=(64,))
+    fact.mindspore_standalone_impl(dataset=standalone_dataset, epoch=2)
+
+    #auto parallel
+    parallel_dataset = FakeData(size=64, batch_size=8, image_size=(64,), use_parallel=True)
+    fact.mindspore_auto_parallel_impl(dataset=parallel_dataset, epoch=2, device_num=8)
+
+    #compare
+    fact.checkpoint_cmp(inputs_np=inputs_np, label=label)
--- a/tests/st/auto_parallel/test_multifieldembeddinglookup_parallel.py
+++ b/tests/st/auto_parallel/test_multifieldembeddinglookup_parallel.py
@ -0,0 +1,26 @@
+# Copyright 2021 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_x86_gpu_training
+@pytest.mark.env_single
+def test_sit_multifieldembeddinglookup_parallel():
+    cmd = "mpirun -n 8 pytest -s multifieldembeddinglookup_parallel.py > multifieldembeddinglookup.log 2>&1"
+    ret = os.system(cmd)
+    os.system(f"grep -E 'ERROR|error' multifieldembeddinglookup.log -C 3")
+    assert ret == 0