[zero] add strict ddp mode

colossalai/nn/parallel/data_parallel.py

@@ -12,6 +12,7 @@
 from colossalai.logging import get_dist_logger
 from colossalai.nn.parallel.utils import get_temp_total_chunk_on_cuda
 from colossalai.tensor import ProcessGroup as ColoProcessGroup
+from colossalai.tensor import ReplicaSpec
 from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec
 from colossalai.tensor.param_op_hook import ColoParamOpHookManager
 from colossalai.utils import get_current_device, is_ddp_ignored
@@ -200,14 +201,18 @@ class ZeroDDP(ColoDDP):
         gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous momery space.
             For more details, see the API reference of ``GeminiManager``.
         pin_memory (bool): Chunks on CPU Memory use pin-memory.
-        force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16.  Defaults to False.
+        force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16.
+            Defaults to False.
+        strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated.
+            Defaults to False. Users can set it to True, when they clearly know that they only need DDP.
     """
 
     def __init__(self,
                  module: torch.nn.Module,
                  gemini_manager: GeminiManager,
                  pin_memory: bool = False,
-                 force_outputs_fp32: bool = False) -> None:
+                 force_outputs_fp32: bool = False,
+                 strict_ddp_mode: bool = False) -> None:
         super().__init__(module, process_group=ColoProcessGroup())
         self.gemini_manager = gemini_manager
         self.chunk_manager: ChunkManager = gemini_manager.chunk_manager
@@ -232,6 +237,9 @@ def __init__(self,
         for p in param_order.generate():
             assert isinstance(p, ColoParameter)
 
+            if strict_ddp_mode and not p.is_replicate():
+                p.set_dist_spec(ReplicaSpec())
+
             if is_ddp_ignored(p):
                 p.data = p.data.to(device=get_current_device(), dtype=torch.float16)
                 continue

colossalai/nn/parallel/gemini_parallel.py

@@ -17,6 +17,7 @@ def __init__(self,
                  placement_policy: str = "cpu",
                  pin_memory: bool = False,
                  force_outputs_fp32: bool = False,
+                 strict_ddp_mode: bool = False,
                  search_range_mb: int = 32,
                  hidden_dim: Optional[int] = None,
                  min_chunk_size_mb: Optional[float] = None,
@@ -54,4 +55,4 @@ def __init__(self,
                                            search_range_mb=search_range_mb,
                                            min_chunk_size_mb=min_chunk_size_mb)
         gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats)
-        super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32)
+        super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode)

examples/language/gpt/gemini/commons/model_zoo.py

@@ -53,6 +53,14 @@ def gpt2_24b(checkpoint=True):
     return GPTLMModel(hidden_size=8192, num_layers=30, num_attention_heads=16, checkpoint=checkpoint)
 
 
+def gpt2_30b(checkpoint=True):
+    return GPTLMModel(hidden_size=8192, num_layers=37, num_attention_heads=16, checkpoint=checkpoint)
+
+
+def gpt2_40b(checkpoint=True):
+    return GPTLMModel(hidden_size=8192, num_layers=50, num_attention_heads=16, checkpoint=checkpoint)
+
+
 def model_builder(model_size: str) -> callable:
     if model_size == "gpt2_medium":
         return gpt2_medium
@@ -66,6 +74,10 @@ def model_builder(model_size: str) -> callable:
         return gpt2_20b
     elif model_size == "gpt2_24b":
         return gpt2_24b
+    elif model_size == "gpt2_30b":
+        return gpt2_30b
+    elif model_size == "gpt2_40b":
+        return gpt2_40b
     else:
         raise TypeError(f"model_builder {model_size}")
 

examples/language/gpt/gemini/train_gpt_demo.py

@@ -187,17 +187,18 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
 
 
 # Gemini + ZeRO DDP
-def build_gemini(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
+def build_gemini(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto", ddp_flag: bool = True):
     fp16_init_scale = 2**5
     gpu_margin_mem_ratio_for_auto = 0
 
     if version.parse(CAI_VERSION) > version.parse("0.1.10"):
         model = GeminiDDP(model,
+                          strict_ddp_mode=ddp_flag,
                           device=get_current_device(),
                           placement_policy=placement_policy,
                           pin_memory=True,
                           hidden_dim=model.config.n_embd,
-                          search_range_mb=64)
+                          search_range_mb=128)
         # configure the const policy
         if placement_policy == 'const':
             model.gemini_manager._placement_policy.set_const_memory_boundary(2 * 1024)
@@ -279,11 +280,12 @@ def main():
         tp_pg = ProcessGroup(tp_degree=args.tp_degree)
         # Tensor Parallelism (TP)
         # You should notice that v0.1.10 is not compatible with TP degree > 1
-        tensor_parallelize(model, tp_pg)
+        if args.tp_degree > 1:
+            tensor_parallelize(model, tp_pg)
 
         # build a Gemini model and a highly optimized cpu optimizer
         # Gemini + ZeRO DP, Note it must be used after TP
-        model, optimizer = build_gemini(model, tp_pg, args.placement)
+        model, optimizer = build_gemini(model, tp_pg, args.placement, args.tp_degree == 1)
 
         logger.info(get_mem_info(prefix='After init optim, '), ranks=[0])
     else:

tests/test_tensor/test_tp_with_zero.py

@@ -93,7 +93,7 @@ def run_gpt(placement_policy, tp_init_spec_func=None):
     else:
         init_device = None
 
-    model = GeminiDDP(model, init_device, placement_policy, True, False, 32)
+    model = GeminiDDP(model, init_device, placement_policy, True, False)
     # The same as the following 3 lines
     # chunk_manager = ChunkManager(config_dict, init_device=init_device)
     # gemini_manager = GeminiManager(placement_policy, chunk_manager)