Openmind to master

mindnlp/accelerate/__init__.py

@@ -5,8 +5,9 @@
     # DDPCommunicationHookType,
     # DeepSpeedPlugin,
     # DistributedDataParallelKwargs,
-    # DistributedType,
     # FullyShardedDataParallelPlugin,
+    accelerate_distributed_type,
+    DistributedType,
     # GradScalerKwargs,
     # InitProcessGroupKwargs,
     # ProfileKwargs,

mindnlp/accelerate/accelerator.py

@@ -1,19 +1,20 @@
 """accelerate"""
 import os
+import mindspore
+import numpy
+
 from contextlib import contextmanager
 from typing import Optional
-
-import mindspore
 from mindspore import nn
 from mindspore.communication import init
 
 from .state import AcceleratorState
 from .utils import (
-    DistributedType,
     MindFormersPlugin,
     is_mindformers_available,
     wait_for_everyone
 )
+from .utils import DistributedType,accelerate_distributed_type
 from ..utils import logging
 
 if is_mindformers_available():
@@ -45,7 +46,7 @@ def __init__(
         # init mindformers_plugin from env variables
         if mindformers_plugin is None:
             mindformers_plugin = (
-                MindFormersPlugin() if os.environ.get("ACCELERATE_USE_MINDFORMERS", "false") == "true" else None
+                MindFormersPlugin() if accelerate_distributed_type == DistributedType.MINDFORMERS else None
             )
         else:
             os.environ["ACCELERATE_USE_MINDFORMERS"] = "true"
@@ -104,12 +105,20 @@ def prepare(self, *args):
         """
         result = []
 
-        # Only support mindsormers now
+        # Only support mindsormers and MULTI_NPU_DP now
         if self.distributed_type == DistributedType.MINDFORMERS:
             result = self._prepare_mindformers(*args)
-
+        elif self.distributed_type == DistributedType.MULTI_NPU_DP:
+            result = self._prepare_data_parallel_native_minspore(*args)
         return result
 
+    def _prepare_data_parallel_native_minspore(self, *args):
+        # initialize data parallel for native mindspore
+        mindspore.set_context(mode=mindspore.GRAPH_MODE)
+        mindspore.set_auto_parallel_context(parallel_mode=mindspore.ParallelMode.DATA_PARALLEL, gradients_mean=True)
+        mindspore.communication.init()
+        mindspore.set_seed(numpy.random.seed())
+
     def _prepare_mindformers(self, *args):
         mindformers_plugin = self.state.mindformers_plugin
 

mindnlp/accelerate/state.py

@@ -4,14 +4,16 @@
 from contextlib import contextmanager
 from typing import Callable, Any
 from mindspore import communication
+
 try:
     from mindspore.communication.comm_func import barrier
 except:
     barrier = None
 
 from .utils import (
-    DistributedType, is_mindformers_available
+    is_mindformers_available
 )
+from ..accelerate.utils import accelerate_distributed_type, DistributedType
 
 SharedDict = dict
 
@@ -341,11 +343,14 @@ def print(self, *args, **kwargs):
             print(*args, **kwargs)
 
     def _prepare_backend(self):
-        # now mindformers only
-        if is_mindformers_available():
+        # now mindformers and mindspore data parallel only
+        if accelerate_distributed_type == DistributedType.MINDFORMERS and is_mindformers_available():
             self.backend = "hccl"
             self.distributed_type = DistributedType.MINDFORMERS
-
+        elif accelerate_distributed_type == DistributedType.MULTI_NPU_DP:
+            self.backend = "hccl"
+            self.distributed_type = DistributedType.MULTI_NPU_DP
+
     @num_processes.setter
     def num_processes(self, value):
         self._num_processes = value
@@ -366,10 +371,14 @@ def __init__(self, mindformers_plugin=None, **kwargs):
         if PartialState._shared_state:
             PartialState(**kwargs)
         self.__dict__.update(PartialState._shared_state)
-
-        if os.environ.get("ACCELERATE_USE_MINDFORMERS", "false") == "true":
+        # set distributed_type
+        if accelerate_distributed_type == DistributedType.MULTI_NPU_DP:
+            self.distributed_type = DistributedType.MULTI_NPU_DP
+        elif accelerate_distributed_type == DistributedType.MINDFORMERS:
             self.distributed_type = DistributedType.MINDFORMERS
             self.mindformers_plugin = mindformers_plugin
+        else: 
+            self.distributed_type = DistributedType.NO
 
         PartialState._shared_state["distributed_type"] = self.distributed_type
 

mindnlp/accelerate/utils/__init__.py

@@ -1,4 +1,5 @@
 """accelerate utils"""
+from .constants import accelerate_distributed_type
 from .dataclasses import (
     DistributedType,
     MindFormersPlugin

mindnlp/accelerate/utils/constants.py

@@ -0,0 +1,21 @@
+"""constants"""
+import os
+from .dataclasses import DistributedType
+
+def detect_accelerate_distributed_type():
+    """
+    detect distributed_type
+
+    Returns:
+        _type_: According to the factors such as the available parallel software and hardware environment of the current system and the user-specified parallel scheme,
+          the optimal parallel strategy is comprehensively decided in different situations.
+    """
+    if os.environ.get("MULTI_NPU_DP", None) == "true": 
+        return DistributedType.MULTI_NPU_DP
+    if os.environ.get("ACCELERATE_USE_MINDFORMERS", "false") == "true": 
+        return DistributedType.MINDFORMERS
+    else:
+        return DistributedType.NO
+
+accelerate_distributed_type = detect_accelerate_distributed_type()
+

mindnlp/accelerate/utils/dataclasses.py

@@ -17,8 +17,11 @@ class DistributedType(str, enum.Enum):
 
     Values:
         - **MINDFORMERS** -- Using mindformers
+        - **NO** -- Not a distributed environment, just a single process.
+        - **MULTI_NPU_DP** -- Distributed data parallel on multiple NPUs.
     """
 
+    MULTI_NPU_DP = "MULTI_NPU_DP"
     MINDFORMERS = "MINDFORMERS"
     NO = "NO"
 

mindnlp/dataset/load.py

@@ -23,6 +23,10 @@
 from datasets import Dataset, IterableDataset, Split, Features, \
     DownloadConfig, DownloadMode, VerificationMode, Version
 from mindnlp.configs import DEFAULT_ROOT
+from mindspore.communication import get_rank, get_group_size
+from ..accelerate import DistributedType
+from ..accelerate.utils import accelerate_distributed_type
+
 
 class TransferIterableDataset():
     """TransferDataset for Huggingface Dataset."""
@@ -331,12 +335,19 @@ def load_dataset(
         column_names = list(raw_ds.features.keys())
         source = TransferDataset(raw_ds, column_names) if isinstance(raw_ds, Dataset) \
             else TransferIterableDataset(raw_ds, column_names)
-        ms_ds = GeneratorDataset(
-            source=source,
+        if accelerate_distributed_type == DistributedType.MULTI_NPU_DP:
+            ms_ds = GeneratorDataset(source=source, 
+            column_names=column_names, 
+            shuffle=shuffle,
+            num_parallel_workers=num_proc if num_proc else 1,
+            num_shards=get_group_size(), shard_id=get_rank())
+            datasets_dict[key] = ms_ds
+        else:
+            ms_ds = GeneratorDataset(source=source,
             column_names=column_names,
             shuffle=shuffle,
             num_parallel_workers=num_proc if num_proc else 1)
-        datasets_dict[key] = ms_ds
+            datasets_dict[key] = ms_ds
 
     if len(datasets_dict) == 1:
         return datasets_dict.popitem()[1]

mindnlp/engine/trainer/base.py

@@ -45,6 +45,8 @@
     WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME
 from ...dataset import BaseMapFunction
 from ...utils import logging, find_labels, can_return_loss
+from ...accelerate.utils import DistributedType
+from ...accelerate.utils import accelerate_distributed_type 
 from ...utils.import_utils import is_safetensors_available
 from ...transformers.modeling_utils import PreTrainedModel
 from ...transformers.configuration_utils import PretrainedConfig
@@ -88,6 +90,7 @@
     TrainerControl,
     TrainerState,
 )
+from ..utils import _get_learning_rate
 
 
 logger = logging.get_logger(__name__)
@@ -124,7 +127,6 @@ class Trainer:
     """
     Trainer is a simple but feature-complete training and eval loop for MindSpore, optimized for 🤗 Transformers.
     """
-    from ..utils import _get_learning_rate
     def __init__(
         self,
         model: Union[PreTrainedModel, nn.Module] = None,
@@ -284,6 +286,7 @@ def __init__(
         # Internal variables to help with automatic batch size reduction
         self._train_batch_size = args.train_batch_size
         self._created_lr_scheduler = False
+        self.actual_distributed_type = accelerate_distributed_type
 
     def _activate_neftune(self, model):
         r"""
@@ -1373,6 +1376,14 @@ def training_step(self, model: nn.Module, inputs: Dict[str, Union[mindspore.Tens
         inputs = self._prepare_inputs(inputs)
 
         def forward(inputs):
+            if accelerate_distributed_type == DistributedType.MULTI_NPU_DP:
+                from mindspore.communication import get_group_size
+                import mindspore.ops as msops
+                rank_size = get_group_size()
+                for parameter in model.parameters():
+                    all_reduce_sum =  msops.AllReduce(msops.ReduceOp.SUM)
+                    new_grads_mean = all_reduce_sum(parameter.grad) / rank_size
+                    parameter.grad = new_grads_mean
             return self.compute_loss(model, inputs)
 
         if getattr(self, 'grad_fn', None) is None or self.model_reload:

mindnlp/engine/trainer/default_func.py

@@ -15,10 +15,12 @@
 """
 utils for trainer.
 """
-from mindspore import ops, value_and_grad
+from mindspore import nn, ops, value_and_grad
 from mindspore.amp import all_finite
 
 from mindnlp.utils import ModelOutput
+from ...accelerate.utils import DistributedType
+from ...accelerate.utils import accelerate_distributed_type
 
 def get_default_forward_fn_with_loss_fn(network, loss_fn, loss_scaler):
     """get default forward function with loss function"""

tests/accelerate/grad_Reduce_ut/test_grad_Reduce.py

@@ -0,0 +1,35 @@
+
+
+def test_AllReduce_mean():
+    import numpy as np
+    from mindspore.communication import init, get_rank, get_group_size
+    import mindspore as ms
+    import mindspore.nn as nn
+    import mindspore.ops as ops
+
+    init()
+    rank_size = get_group_size()
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.all_reduce_sum = ops.AllReduce(ops.ReduceOp.SUM)
+
+        def construct(self, x):
+            new_grads_mean = self.all_reduce_sum(x) / rank_size
+            new_grad = new_grads_mean
+            return new_grad
+
+    rank_id_value = get_rank() # Current NPU number 0,...,7
+    print('rank_id_value=',rank_id_value)
+    input_x = ms.Tensor(np.array([[rank_id_value]]).astype(np.float32))
+    print('input_x=',input_x)
+    net = Net()
+    output = net(input_x)
+    print("mean:",output) # sum(0, rank_size) / rank_size
+
+
+
+
+if __name__ == '__main__':
+    test_AllReduce_mean()
+

tests/accelerate/grad_Reduce_ut/test_grad_Reduce.sh

@@ -0,0 +1,3 @@
+# mpirun -n 8 -H 127.0.0.1:8 --output-filename bak/log_output_mpirun_single/log_ python test_grad_Reduce.py
+msrun --worker_num=4 --local_worker_num=4 --master_port=8123 --log_dir=bak/msrun_log --join=True --cluster_time_out=100 test_grad_Reduce.py
+# msrun --worker_num=8 --local_worker_num=8 --master_port=8123 --log_dir=bak/msrun_log --join=True --cluster_time_out=100 test_grad_Reduce.py