fix zero3 fp16 and add zero3 model context (#62)

colossalai/initialize.py

@@ -220,6 +220,8 @@ def initialize(model: Union[nn.Module, List[nn.Module]],
 
     # first sync model across dp ranks
     model.to(get_current_device())
+    use_zero3 = hasattr(gpc.config, 'zero') and gpc.config.zero.level == 3
+    if not use_zero3:
         sync_model_param_in_dp(model)
 
     # check amp and zero

colossalai/zero/__init__.py

+import torch
 import torch.nn as nn
 from torch.optim import Optimizer
 from colossalai.amp.naive_amp import NaiveAMPModel
 from colossalai.utils import is_no_pp_or_last_stage
+from colossalai.core import global_context as gpc
+from colossalai.context.parallel_mode import ParallelMode
 
 from .zero_redundancy_optimizer_level_2 import ZeroRedundancyOptimizer_Level_2
 from .zero_redundancy_optimizer_level_3 import ZeroRedundancyOptimizer_Level_3
@@ -12,7 +15,7 @@ def convert_to_zero(model: nn.Module,
                     level: int,
                     zero_config):
     assert level == 2 or level == 3, 'Only ZERO Optimizer Level 2 and 3 are provided'
-
+    if level == 2:
         if is_no_pp_or_last_stage():
             model = NaiveAMPModel(model, output_to_fp32=True)
         else:
@@ -25,4 +28,71 @@ def convert_to_zero(model: nn.Module,
     return model, optimizer
 
 
-__all__ = ['convert_to_zero', 'ZeroRedundancyOptimizer_Level_2', 'ZeroRedundancyOptimizer_Level_3']
+def zero3_model_context(dtype=torch.half):
+    """A context to enable massive model construction for training with
+        ZeRO-3. Models are automatically partitioned (or, sharded) across the
+        system and converted to half precision. Note that the config of ZeRO-3 will be loaded automatically from `gpc.config`.
+
+        Args:
+            dtype (``dtype``, optional): Can be used to change the data type of the parameters.
+                Supported options are ``torch.half`` and ``torch.float``. Defaults to ``torch.half``
+
+        This context accelerates model initialization and enables models that
+        are too large to allocate in their entirety in CPU memory. It has the
+        following effects:
+
+        #. allocates tensors to either GPU or CPU memory or NVMe
+        #. converts floating point tensors to half precision
+        #. immediately partitions tensors among the group of data-parallel devices
+        #. (*optional*) replaces ``torch.nn.functional.linear`` with a more
+           memory-efficient implementation
+
+        These modifications allow for models that exceed the size of local CPU/GPU
+        memory/NVMe, but fit within the total NVMe capacity (*i.e.*, aggregate CPU
+        or GPU memory or NVMe) across all nodes. Consider initializing a model with one
+        trillion parameters, whose weights occupy two terabytes (TB) in half
+        precision. The initial CPU allocation in full precision requires 4TB of
+        memory *per process*, and so a system with 8 GPUs per node would need 32TB of
+        CPU memory due to data-parallel redundancies. Instead, by immediately
+        partitioning tensors we remove the redundancies. The result is that
+        regardless of the number of GPUs, we still only require the original 4TB. This
+        allows for a linear increase in model size with the aggregate system memory.
+        For example, if a node has 1TB of memory and 8 GPUs, we could fit a trillion
+        parameter model with 4 nodes and 32 GPUs.
+
+        Important: If the fp16 weights of the model can't fit onto a single GPU memory
+        this feature must be used.
+
+        Examples
+        --------
+
+        #. Allocate a model and partition it among all processes:
+
+            .. code-block:: python
+
+                with zero3_model_context():
+                    model = MyLargeModel()
+
+    """
+    assert dtype == torch.half or dtype == torch.float, f'Invalid dtype, except torch.half or torch.float, got {dtype}'
+    import deepspeed
+    ds_config = {
+        "train_micro_batch_size_per_gpu": 1,
+        "gradient_accumulation_steps": 1,
+        "zero_optimization": {
+            "offload_param": getattr(gpc.config.zero, 'offload_param_config', None),
+            "offload_optimizer": getattr(gpc.config.zero, 'offload_optimizer_config'),
+        },
+        "aio": getattr(gpc.config.zero, 'aio_config', None)
+    }
+    remote_device = getattr(ds_config['zero_optimization']['offload_param'], 'device', None)
+    pin_memory = getattr(ds_config['zero_optimization']['offload_param'], 'pin_memory', False)
+    return deepspeed.zero.Init(data_parallel_group=gpc.get_group(ParallelMode.DATA),
+                               remote_device=remote_device,
+                               config_dict_or_path=ds_config,
+                               pin_memory=pin_memory,
+                               dtype=dtype)
+
+
+__all__ = ['convert_to_zero', 'ZeroRedundancyOptimizer_Level_2',
+           'ZeroRedundancyOptimizer_Level_3', 'zero3_model_context']

colossalai/zero/zero_redundancy_optimizer_level_3.py

@@ -637,7 +637,8 @@ class ZeroRedundancyOptimizer_Level_3(Optimizer):
                  postscale_gradients=True,
                  gradient_predivide_factor=1.0,
                  gradient_accumulation_steps=1,
-                 aio_config=None):
+                 aio_config=None,
+                 dtype=torch.half):
         # mpu = None
         # mpu is removed from the parameter list
         # tensor parallel will be automatically detected later
@@ -682,13 +683,25 @@ class ZeroRedundancyOptimizer_Level_3(Optimizer):
         util_ops = UtilsBuilder().load()
         self.flatten = util_ops.flatten
         self.unflatten = util_ops.unflatten
-        self.dtype = self.optimizer.param_groups[0]['params'][0].dtype
+        self.dtype = dtype
 
         if not all(is_zero_param(p) for p in module.parameters()):
+            ds_config = {
+                "train_micro_batch_size_per_gpu": 1,
+                "gradient_accumulation_steps": 1,
+                "zero_optimization": {
+                    "offload_param": offload_param_config,
+                    "offload_optimizer": offload_optimizer_config,
+                },
+                "aio": aio_config
+            }
+            remote_device = offload_param_config['device']
             group = None
             if gpc.is_initialized(ParallelMode.DATA):
                 group = gpc.get_group(ParallelMode.DATA)
-            Init(module=module, data_parallel_group=group, dtype=self.dtype)
+            Init(module=module, data_parallel_group=group, dtype=self.dtype,
+                 remote_device=remote_device, config_dict_or_path=ds_config,
+                 pin_memory=offload_optimizer_config[OFFLOAD_OPTIMIZER_PIN_MEMORY])
 
         for m in module.modules():
             _init_external_params(m)

docs/zero.md

@@ -83,4 +83,13 @@ Note that `fp16` is automatically enabled when using ZeRO. This relies on `AMP_T
 
 ### Training
 
+Note that if your model is too large to fit within the memory when using ZeRO-3, you should use `colossalai.zero.zero3_model_context` to construct your model:
+
+```python
+from colossalai.zero import zero3_model_context
+
+with zero3_model_context():
+    model = Model()
+```
+
 Once you have completed your configuration, just use `colossalai.initialize()` to initialize your training.

docs/zero_zh.md

@@ -23,7 +23,7 @@ ZeRO优化器可以切分三种模型状态（优化器状态、梯度、参数
     )
 
     zero = dict(
-        type='ZeroRedundancyOptimizer_Level_3',
+        level=3,
         dynamic_loss_scale=True,
         clip_grad=1.0
     )
@@ -78,4 +78,13 @@ ZeRO优化器可以切分三种模型状态（优化器状态、梯度、参数
 
 ### 使用ZeRO优化器进行训练
 
+注意，当使用ZeRO-3时，如果您的模型过大以至于无法放入内存, 您应该使用`colossalai.zero.zero3_model_context`来构建您的模型:
+
+```python
+from colossalai.zero import zero3_model_context
+
+with zero3_model_context():
+    model = Model()
+```
+
 如果您完成了上述配置，可以运行`colossalai.initialize()`来开始您的训练。