md support ps-lite

mindspore/dataset/engine/datasets.py

@@ -38,7 +38,7 @@ from mindspore._c_expression import typing
 
 from mindspore import log as logger
 from . import samplers
-from .iterators import DictIterator, TupleIterator
+from .iterators import DictIterator, TupleIterator, DummyIterator
 from .validators import check_batch, check_shuffle, check_map, check_filter, check_repeat, check_skip, check_zip, \
     check_rename, check_numpyslicesdataset, \
     check_take, check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
@@ -146,6 +146,12 @@ class Dataset:
         self._num_classes = None
         self._repeat_count = None
         self._sync = False
+        self.ms_role = os.getenv("MS_ROLE")
+
+    def _noop_mode(self):
+        if self.ms_role in ("MS_PSERVER", "MS_SCHED"):
+            return True
+        return False
 
     def __add__(self, datasets):
         return self.concat(datasets)
@@ -1062,6 +1068,8 @@ class Dataset:
             >>>     # convert the returned tuple to a list and print
             >>>     print(list(item))
         """
+        if self._noop_mode():
+            return DummyIterator(self, 'tuple')
         return TupleIterator(self, columns)
 
     def create_dict_iterator(self):
@@ -1085,6 +1093,8 @@ class Dataset:
             >>>     print(item["column1"])
 
         """
+        if self._noop_mode():
+            return DummyIterator(self, 'dict')
         return DictIterator(self)
 
     def __iter__(self):
@@ -2318,6 +2328,8 @@ class TransferDataset(DatasetOp):
 
     def send(self):
         # need to keep iterator alive so the executionTree is not destroyed
+        if self._noop_mode():
+            return
         self.iterator = TupleIterator(self)
 
 

mindspore/dataset/engine/iterators.py

@@ -17,7 +17,9 @@
 from abc import abstractmethod
 import copy
 import weakref
+import numpy as np
 
+from mindspore.common.tensor import Tensor
 from mindspore._c_dataengine import DEPipeline
 from mindspore._c_dataengine import OpName
 
@@ -287,3 +289,32 @@ class TupleIterator(Iterator):
         """
 
         return [t.as_array() for t in self.depipeline.GetNextAsList()]
+
+
+class DummyIterator():
+    """
+    A DummyIterator only work when env MS_ROLE="MS_PSERVER" or MS_ROLE="MS_SCHED"
+    """
+    def __init__(self, dataset, mode):
+        self.mode = mode
+        self.shapes = dataset.output_shapes()
+        self.types = dataset.output_types()
+        self.fetched_first = False
+
+    def __get_tensor(self):
+        tensor_row = []
+        for np_shape, np_type in zip(self.shapes, self.types):
+            input_np = np.zeros(np_shape, np_type)
+            tensor = Tensor(input_np)
+            tensor_row.append(tensor)
+        return tensor_row
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.mode == "tuple":
+            if not self.fetched_first:
+                self.fetched_first = True
+                return self.__get_tensor()
+        raise StopIteration()

mindspore/train/dataset_helper.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """Dataset help for minddata dataset"""
 import math
+import os
 
 from mindspore._checkparam import check_bool
 from .. import context
@@ -60,6 +61,10 @@ class DatasetHelper:
                 if context.get_context("device_target") == "Ascend":
                     iterclass = _DatasetIterMSLoopSink
                 elif context.get_context("device_target") == "GPU":
+                    ms_role = os.getenv("MS_ROLE")
+                    if ms_role in ("MS_PSERVER", "MS_SCHED"):
+                        iterclass = _DatasetIterPSLite
+                    else:
                         iterclass = _DatasetIterMS
                 elif context.get_context("device_target") == "CPU":
                     raise RuntimeError("Currently dataset sink mode is not supported when the device target is CPU.")
@@ -131,6 +136,9 @@ class _DatasetIterMSLoopSink(_DatasetIter):
     def __init__(self, dataset):
         super(_DatasetIterMSLoopSink, self).__init__(dataset)
         self.loop_count = self.get_loop_count(dataset)
+        ms_role = os.getenv("MS_ROLE")
+        if ms_role in ("MS_PSERVER", "MS_SCHED"):
+            self.loop_count = 1
         # for self._parallel_mode equal to semi_auto_parallel or auto_parallel, and not using full_batch,
         # use a complete tensor to compile, and slice tensor to run. The batch dimension of tensors for
         # compile is device_number times the batch dimension of tensors for run. Now only support LoopSink.
@@ -154,6 +162,18 @@ class _DatasetIterMS(_DatasetIter):
         self.op = GetNextSingleOp(self.dataset_types, self.dataset_shapes, queue_name)
 
 
+class _DatasetIterPSLite(_DatasetIter):
+    """Iter for context (device_target=GPU) on MS_PSERVER or MS_SCHED"""
+    def __init__(self, dataset):
+        super(_DatasetIterPSLite, self).__init__(dataset)
+        self.loop_count = 1
+        self.loop_size = 1
+        self.op = None
+        def op():
+            return _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num=1)
+        self.op = op
+
+
 class _DatasetIterGE(_DatasetIter):
     """Iter for ge"""
     def __init__(self, dataset):

mindspore/train/model.py

@@ -15,6 +15,7 @@
 """Model."""
 from collections.abc import Iterable
 
+import os
 import numpy as np
 
 from mindspore import log as logger
@@ -350,6 +351,9 @@ class Model:
         cb_params.train_dataset = train_dataset
         cb_params.list_callback = self._transform_callbacks(callbacks)
         cb_params.train_dataset_element = None
+        ms_role = os.getenv("MS_ROLE")
+        if ms_role in ("MS_PSERVER", "MS_SCHED"):
+            epoch = 1
 
         # build callback list
         with _CallbackManager(callbacks) as list_callback:

tests/ut/python/dataset/test_noop_mode.py

@@ -0,0 +1,45 @@
+# 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.
+# ==============================================================================
+"""
+Test No-op mode support with Dummy Iterator
+"""
+import os
+import mindspore.dataset as ds
+
+DATA_DIR = "../data/dataset/testVOC2012"
+
+def test_noop_pserver():
+    os.environ['MS_ROLE'] = 'MS_PSERVER'
+    data1 = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", decode=True, shuffle=False)
+    num = 0
+    for _ in data1.create_dict_iterator():
+        num += 1
+    assert num == 0
+    del os.environ['MS_ROLE']
+
+
+def test_noop_sched():
+    os.environ['MS_ROLE'] = 'MS_SCHED'
+    data1 = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", decode=True, shuffle=False)
+    num = 0
+    for _ in data1.create_dict_iterator():
+        num += 1
+    assert num == 0
+    del os.environ['MS_ROLE']
+
+
+if __name__ == '__main__':
+    test_noop_pserver()
+    test_noop_sched()