Multiple zero stage 3 related fixes (#3886)

* Option to override module apply

* Removing early partitioning in override

* Unit tests

* Cleanup

* Adapt unit test to succeed

* Handle missed params

* Add accelerate

* Code cleanup

* Add doc

* Add doc

* Add doc

deepspeed/runtime/engine.py

@@ -1041,23 +1041,22 @@ class DeepSpeedEngine(Module):
 
     def _configure_distributed_model(self, model):
         self._set_client_model(model)
-
-        is_zero3_model = self.zero_optimization_partition_weights() and any(
+        is_zero_init_model = self.zero_optimization_partition_weights() and any(
             [hasattr(param, "ds_id") for param in self.module.parameters()])
 
         if self.fp16_enabled():
-            if is_zero3_model:
+            if is_zero_init_model:
                 self.__check_params(self.module, torch.half)
             self.module.half()
         elif self.bfloat16_enabled():
-            if is_zero3_model:
+            if is_zero_init_model:
                 self.__check_params(self.module, torch.bfloat16)
             self.module.bfloat16()
         else:
             self.__check_params(self.module, torch.float)
 
         # zero.Init() handles device placement of model
-        if not self.dont_change_device:
+        if not (self.dont_change_device or is_zero_init_model):
             self.module.to(self.device)
 
         # MoE related initialization
@@ -1097,7 +1096,7 @@ class DeepSpeedEngine(Module):
         self.expert_parallel_group = groups._get_expert_parallel_group_dict()
         self.expert_data_parallel_group = groups._get_expert_data_parallel_group_dict()
 
-        if not self.amp_enabled():
+        if not (self.amp_enabled() or is_zero_init_model):
             self._broadcast_model()
 
     # check if parameters are duplicated in optimizer param_groups

deepspeed/runtime/zero/config.py

@@ -38,7 +38,8 @@ ZeRO optimization should be enabled as:
     "zero_hpz_partition_size": 1,
     "zero_quantized_weights": [true|false],
     "zero_quantized_gradients": [true|false],
-    "memory_efficient_linear": [true|false]
+    "memory_efficient_linear": [true|false],
+    "override_module_apply": [true|false],
     }
 }
 """
@@ -269,11 +270,17 @@ class DeepSpeedZeroConfig(DeepSpeedConfigModel):
     mics_shard_size: int = Field(-1, new_param="mics_shard_size")
 
     mics_hierarchical_params_gather: bool = False
+
     memory_efficient_linear: bool = True
     """
     Use memory efficient linear implementation, for Stage 3.
     """
 
+    override_module_apply: bool = True
+    """
+    Override nn.Module apply function, for Stage 3.
+    """
+
     # Validators
     @validator("overlap_comm")
     def overlap_comm_valid(cls, field_value, values):

deepspeed/runtime/zero/partition_parameters.py

@@ -34,7 +34,6 @@ from deepspeed.utils.debug import (debug_param2name_id_shape, debug_param2name_i
 from deepspeed.accelerator import get_accelerator
 from ..swap_tensor.partitioned_param_swapper import AsyncPartitionedParameterSwapper, PartitionedParamStatus
 
-param_count = 0
 partitioned_param_data_shape = [0]
 zero_init_context = 0
 top_level_context = None
@@ -217,12 +216,14 @@ class ZeroParamStatus(Enum):
     INFLIGHT = 3
 
 
+_orig_torch_tensor = torch.tensor
 _orig_torch_empty = torch.empty
 _orig_torch_zeros = torch.zeros
 _orig_torch_ones = torch.ones
 _orig_torch_full = torch.full
 _orig_torch_arange = torch.arange
 _orig_torch_eye = torch.eye
+_orig_torch_randn = torch.randn
 
 
 def zero_wrapper_for_fp_tensor_constructor(fn: Callable, target_fp_dtype: torch.dtype) -> Callable:
@@ -288,6 +289,8 @@ empty_buffers = {}
 # Inserts _post_init_method at the end of init method
 # for all sub classes of torch.nn.Module
 class InsertPostInitMethodToModuleSubClasses(object):
+    num_module_parameters = 0
+    num_module_elements = 0
 
     def __init__(self, enabled=True, mem_efficient_linear=True, ds_config=None, dtype=None):
         self.mem_efficient_linear = mem_efficient_linear
@@ -324,7 +327,10 @@ class InsertPostInitMethodToModuleSubClasses(object):
             top_level_context = None
 
             if dist.get_rank() == 0:
-                logger.info("finished initializing model with %.2fB parameters", param_count / 1e9)
+                billion_elems = InsertPostInitMethodToModuleSubClasses.num_module_elements / 1e9
+                num_params = InsertPostInitMethodToModuleSubClasses.num_module_parameters
+                logger.info(
+                    f"finished initializing model - num_params = {num_params}, num_elems = {billion_elems:.2f}B")
 
         # Now that we cleaned up the metaclass injection, raise the exception.
         if exc_type is not None:
@@ -381,14 +387,16 @@ class InsertPostInitMethodToModuleSubClasses(object):
                     3. broadcasts root rank's parameters to the other ranks
                     4. re-partitions the parameters
                     """
-                    if not all(is_zero_param(p) for p in module_to_apply_fn_to.parameters(recurse=False)):
-                        raise RuntimeError(f"not all parameters for {module_to_apply_fn_to.__class__.__name__}, "
-                                           f"were zero params, is it possible that the parameters were "
-                                           f"overwritten after they were initialized? "
-                                           f"params: {[p for p in module_to_apply_fn_to.parameters(recurse=False)]} ")
+
+                    # TODO Delay error checking for dangling partitioned parameters to post module init
+                    # raise RuntimeError(f"not all parameters for {module_to_apply_fn_to.__class__.__name__}, "
+                    #                    f"were zero params, is it possible that the parameters were "
+                    #                    f"overwritten after they were initialized? "
+                    #                    f"params: {[p for p in module_to_apply_fn_to.parameters(recurse=False)]} ")
 
                     params_to_apply_fn_to: Iterable[Parameter] = list(
-                        sorted(module_to_apply_fn_to.parameters(recurse=False), key=lambda p: p.ds_id))
+                        sorted([p for p in module_to_apply_fn_to.parameters(recurse=False) if is_zero_param(p)],
+                               key=lambda p: p.ds_id))
 
                     for param in params_to_apply_fn_to:
                         param.all_gather()
@@ -464,7 +472,8 @@ class InsertPostInitMethodToModuleSubClasses(object):
 
         # Replace .__init__() for future subclasses of torch.nn.Module
         torch.nn.modules.module.Module.__init_subclass__ = classmethod(_init_subclass)
-        torch.nn.modules.module.Module.apply = apply_with_gather(torch.nn.modules.module.Module._old_apply)
+        if Init.override_module_apply:
+            torch.nn.modules.module.Module.apply = apply_with_gather(torch.nn.modules.module.Module._old_apply)
 
         self._add_tensor_creation_wrappers()
 
@@ -489,7 +498,8 @@ class InsertPostInitMethodToModuleSubClasses(object):
 
             # putting methods back the way we found them
             torch.nn.modules.module.Module.__init_subclass__ = torch.nn.modules.module.Module._old_init_subclass
-            torch.nn.modules.module.Module.apply = torch.nn.modules.module.Module._old_apply
+            if Init.override_module_apply:
+                torch.nn.modules.module.Module.apply = torch.nn.modules.module.Module._old_apply
 
             self._remove_tensor_creation_wrappers()
 
@@ -497,21 +507,25 @@ class InsertPostInitMethodToModuleSubClasses(object):
 
     def _add_tensor_creation_wrappers(self):
         torch.Tensor.__new__ = get_new_tensor_fn_for_dtype(self.dtype)
+        torch.tensor = zero_wrapper_for_fp_tensor_constructor(_orig_torch_tensor, self.dtype)
         torch.empty = zero_wrapper_for_fp_tensor_constructor(_orig_torch_empty, self.dtype)
         torch.zeros = zero_wrapper_for_fp_tensor_constructor(_orig_torch_zeros, self.dtype)
         torch.ones = zero_wrapper_for_fp_tensor_constructor(_orig_torch_ones, self.dtype)
         torch.full = zero_wrapper_for_fp_tensor_constructor(_orig_torch_full, self.dtype)
         torch.arange = zero_wrapper_for_fp_tensor_constructor(_orig_torch_arange, self.dtype)
         torch.eye = zero_wrapper_for_fp_tensor_constructor(_orig_torch_eye, self.dtype)
+        torch.randn = zero_wrapper_for_fp_tensor_constructor(_orig_torch_randn, self.dtype)
 
     def _remove_tensor_creation_wrappers(self):
         torch.Tensor.__new__ = torch.Tensor.__old_new__
+        torch.tensor = _orig_torch_tensor
         torch.empty = _orig_torch_empty
         torch.zeros = _orig_torch_zeros
         torch.ones = _orig_torch_ones
         torch.full = _orig_torch_full
         torch.arange = _orig_torch_arange
         torch.eye = _orig_torch_eye
+        torch.randn = _orig_torch_randn
 
 
 def shutdown_init_context():
@@ -687,6 +701,7 @@ class Init(InsertPostInitMethodToModuleSubClasses):
     num_persisted_parameters = 0
     num_persisted_elements = 0
     apply_param_persistence = False
+    override_module_apply = get_config_default(DeepSpeedZeroConfig, "override_module_apply")
 
     def __init__(self,
                  module=None,
@@ -845,9 +860,12 @@ class Init(InsertPostInitMethodToModuleSubClasses):
             self.quantizer_module = CUDAQuantizer()
             print_rank_0(f'Using quantizer: {self.quantizer_module.__class__.__name__}', force=True)
 
-        if _ds_config is not None and _ds_config.zero_config.offload_param is not None:
-            remote_device = _ds_config.zero_config.offload_param.device
-            pin_memory = _ds_config.zero_config.offload_param.pin_memory
+        if _ds_config is not None:
+            Init.override_module_apply = _ds_config.zero_config.override_module_apply
+
+            if _ds_config.zero_config.offload_param is not None:
+                remote_device = _ds_config.zero_config.offload_param.device
+                pin_memory = _ds_config.zero_config.offload_param.pin_memory
 
         self._validate_remote_device(remote_device, _ds_config)
 
@@ -877,12 +895,21 @@ class Init(InsertPostInitMethodToModuleSubClasses):
         Init.param_persistence_threshold = ds_config.zero_config.param_persistence_threshold
         Init.model_persistence_threshold = ds_config.zero_config.model_persistence_threshold // self.num_partitions
 
+    def _zero_init_param(self, param):
+        self._convert_to_deepspeed_param(param)
+        if dist.get_world_group() == self.get_dp_process_group():
+            dist.broadcast(param, 0, self.get_dp_process_group())
+        else:
+            dist.broadcast(param, dist.get_global_rank(self.get_dp_process_group(), 0), self.get_dp_process_group())
+        param.partition()
+
     def _convert_to_zero_parameters(self, param_list):
         for param in param_list:
             if is_zero_param(param):
                 continue
-            self._convert_to_deepspeed_param(param)
-            param.partition()
+
+            param.data = param.data.to(self.local_device)
+            self._zero_init_param(param)
 
     def _validate_remote_device(self, remote_device, ds_config):
         if ds_config is not None:
@@ -904,28 +931,19 @@ class Init(InsertPostInitMethodToModuleSubClasses):
         print_rank_0(f'Converting Params in {module.__class__.__name__}', force=False)
         see_memory_usage(f"Before converting and partitioning params in {module.__class__.__name__}", force=False)
 
-        global param_count
         for name, param in module.named_parameters(recurse=False):
-            param_count += param.numel()
+            print_rank_0(f'Analyzing param {name} in {module.__class__.__name__}', force=False)
+            InsertPostInitMethodToModuleSubClasses.num_module_parameters += 1
+            InsertPostInitMethodToModuleSubClasses.num_module_elements += param.numel()
             if not is_zero_param(param):
-                self._convert_to_deepspeed_param(param)
+                if not get_accelerator().on_accelerator(param):
+                    param.data = param.data.to(self.local_device)
+                self._zero_init_param(param)
                 print_rank_0(
                     f"Partitioning param {debug_param2name_id_shape(param)} module={debug_module2name(module)}")
 
-                if get_accelerator().on_accelerator(param):
-                    if dist.get_world_group() == self.get_dp_process_group():
-                        dist.broadcast(param, 0, self.get_dp_process_group())
-                    else:
-                        dist.broadcast(param, dist.get_global_rank(self.get_dp_process_group(), 0),
-                                       self.get_dp_process_group())
-                else:
-                    if dist.get_rank() == 0:
-                        logger.warn(f"param `{name}` in {module.__class__.__name__} "
-                                    f"not on GPU so was not broadcasted from rank 0")
-
-                param.partition()
         see_memory_usage(
-            f"Param count {param_count}. After converting and partitioning params in {module.__class__.__name__}",
+            f"Param count {InsertPostInitMethodToModuleSubClasses.num_module_elements}. After converting and partitioning params in {module.__class__.__name__}",
             force=False)
 
     def _convert_to_deepspeed_param(self, param):
@@ -1342,7 +1360,6 @@ class Init(InsertPostInitMethodToModuleSubClasses):
 
             tensor_size = self._aligned_size(param)
             partition_size = tensor_size // self.num_partitions
-
             if param.ds_tensor is None:
                 final_location = None
                 if self.remote_device == OffloadDeviceEnum.nvme and self.param_swapper.swappable_tensor(

docs/code-docs/source/zero3.rst

@@ -309,6 +309,17 @@ DeepSpeed can automatically detect the following external parameter scenarios:
 .. autofunction:: deepspeed.zero.unregister_external_parameter
 
 
+.. `Module.apply <https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=module+apply#torch.nn.Module.apply>`_
+Overriding Module.apply
+===============================
+A convenient mechanism for customizing model initialization is `Module.apply <https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=module+apply#torch.nn.Module.apply>`_.
+With ZeRO stage 3, ``Module.apply`` implementations must account for parameter partitioning by ``zero.Init`` during model initialization. The default behavior of ZeRO stage 3 is to automatically
+handle this issue by overriding ``Module.apply`` to ensure that parameters are gathered before access by ``Module.apply``. The benefit of this approach is development convenience, since
+users are saved the burden of manual parameter coordination in ``Module.apply``. However, the downside is slow model initialization, since all the model parameters (e.g., billions) are gathered
+even though the common usage of ``Module.apply`` is to customize a few parameters. Developers can disable this default behavior by setting the ``override_module_apply`` configuration knob to ``False``,
+for faster model initialization at the cost of manually handling partitioned parameters in their ``Module.apply`` implementations.
+
+
 Memory-Centric Tiling
 ---------------------
 

requirements/requirements-dev.txt

+accelerate
 clang-format==16.0.2
 coverage
 docutils<0.18

tests/unit/runtime/zero/test_zero_nesting_init.py

@@ -7,6 +7,9 @@ import torch
 
 from unit.common import DistributedTest
 
+from transformers import VisionEncoderDecoderModel
+from transformers.deepspeed import HfDeepSpeedConfig
+
 import deepspeed
 
 
@@ -44,3 +47,26 @@ class TestShutdownInNestingInit(DistributedTest):
         # ensure that zero3 processed the parameter
         assert hasattr(model2.weight, "ds_id")
         deepspeed_engine2, *_ = deepspeed.initialize(model=model2, config_params=ds_config)
+
+
+class TestNestedParallelInit(DistributedTest):
+    world_size = 1
+
+    # Testing a model with composed and nested zero.Inits, with 3 zero.Init contexts, 1 parent and 2 children.
+    # The skeleton of the model is like so
+    #
+    # class VisionEncoderDecoderModel(...)::
+    #     def __init__(self):
+    #             encoder = AutoModel.from_config(config.encoder)
+    #             decoder = AutoModelForCausalLM.from_config(config.decoder)
+    #
+    # And the user calls like below:
+    # VisionEncoderDecoderModel.from_pretrained(...)
+    # which calls this constructor inside zero.Init
+
+    def test_nested_parallel_init(self):
+        ds_config = dict(train_batch_size=1, zero_optimization=dict(stage=3))
+        dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive
+        model = VisionEncoderDecoderModel.from_pretrained(
+            "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-vit-gpt2")
+        assert all([hasattr(p, 'ds_id') for p in model.parameters()])