[docs] change placememt_policy to placement_policy (#3829)

* fix typo colossalai/autochunk auto_parallel amp

* fix typo colossalai/auto_parallel nn utils etc.

* fix typo colossalai/auto_parallel autochunk fx/passes  etc.

* fix typo docs/

* change placememt_policy to placement_policy in docs/ and examples/

docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md

@@ -175,11 +175,11 @@ In this way, users can train their models as usual.
 In our latest example, a Gemini + ZeRO DDP model is also defined to reduce overhead and improve efficiency.For the details of this part, please refer to [ZeRO](../features/zero_with_chunk.md). You can combine these two parts to understand our entire training process:
 
 ```python
-def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
     from colossalai.nn.parallel import GeminiDDP
     model = GeminiDDP(model,
                         device=get_current_device(),
-                        placement_policy=placememt_policy,
+                        placement_policy=placement_policy,
                         pin_memory=True,
                         search_range_mb=32)
     return model

docs/source/en/features/zero_with_chunk.md

@@ -185,23 +185,23 @@ def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup):
 Define a model which uses Gemini + ZeRO DDP:
 
 ```python
-def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
     cai_version = colossalai.__version__
     if version.parse(cai_version) > version.parse("0.1.10"):
         from colossalai.nn.parallel import GeminiDDP
         model = GeminiDDP(model,
                           device=get_current_device(),
-                          placement_policy=placememt_policy,
+                          placement_policy=placement_policy,
                           pin_memory=True,
                           search_range_mb=32)
     elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
         from colossalai.gemini import ChunkManager, GeminiManager
         chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
-        gemini_manager = GeminiManager(placememt_policy, chunk_manager)
+        gemini_manager = GeminiManager(placement_policy, chunk_manager)
         chunk_manager = ChunkManager(chunk_size,
                                      pg,
                                      enable_distributed_storage=True,
-                                     init_device=GeminiManager.get_default_device(placememt_policy))
+                                     init_device=GeminiManager.get_default_device(placement_policy))
         model = ZeroDDP(model, gemini_manager)
     else:
         raise NotImplemented(f"CAI version {cai_version} is not supported")

docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md

@@ -159,11 +159,11 @@ for mn, module in model.named_modules():
 在我们最新示例中还定义了一个Gemini + ZeRO DDP 的模型从而减小开销，提升效率。这一部分的详细内容可以参考[ZeRO](../features/zero_with_chunk.md)，你可以将这两部分内容结合起来看从而理解我们整个训练流程：
 
 ```python
-def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
     from colossalai.nn.parallel import GeminiDDP
     model = GeminiDDP(model,
                         device=get_current_device(),
-                        placement_policy=placememt_policy,
+                        placement_policy=placement_policy,
                         pin_memory=True,
                         search_range_mb=32)
     return model

docs/source/zh-Hans/features/zero_with_chunk.md

@@ -185,23 +185,23 @@ def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup):
 定义一个使用 Gemini + ZeRO DDP 的模型：
 
 ```python
-def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
     cai_version = colossalai.__version__
     if version.parse(cai_version) > version.parse("0.1.10"):
         from colossalai.nn.parallel import GeminiDDP
         model = GeminiDDP(model,
                           device=get_current_device(),
-                          placement_policy=placememt_policy,
+                          placement_policy=placement_policy,
                           pin_memory=True,
                           search_range_mb=32)
     elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
         from colossalai.gemini import ChunkManager, GeminiManager
         chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
-        gemini_manager = GeminiManager(placememt_policy, chunk_manager)
+        gemini_manager = GeminiManager(placement_policy, chunk_manager)
         chunk_manager = ChunkManager(chunk_size,
                                      pg,
                                      enable_distributed_storage=True,
-                                 			init_device=GeminiManager.get_default_device(placememt_policy))
+                                 			init_device=GeminiManager.get_default_device(placement_policy))
         model = ZeroDDP(model, gemini_manager)
     else:
         raise NotImplemented(f"CAI version {cai_version} is not supported")

examples/images/dreambooth/train_dreambooth_colossalai.py

@@ -340,12 +340,12 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
 
 
 # Gemini + ZeRO DDP
-def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"):
     from colossalai.nn.parallel import GeminiDDP
 
     model = GeminiDDP(model,
                       device=get_current_device(),
-                      placement_policy=placememt_policy,
+                      placement_policy=placement_policy,
                       pin_memory=True,
                       search_range_mb=64)
     return model

examples/images/dreambooth/train_dreambooth_colossalai_lora.py

@@ -342,12 +342,12 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
 
 
 # Gemini + ZeRO DDP
-def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"):
     from colossalai.nn.parallel import GeminiDDP
 
     model = GeminiDDP(model,
                       device=get_current_device(),
-                      placement_policy=placememt_policy,
+                      placement_policy=placement_policy,
                       pin_memory=True,
                       search_range_mb=64)
     return model

examples/language/palm/train.py

@@ -102,23 +102,23 @@ def get_model_size(model: nn.Module):
 
 
 # Gemini + ZeRO DDP
-def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
+def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
     cai_version = colossalai.__version__
     if version.parse(cai_version) > version.parse("0.1.10"):
         from colossalai.nn.parallel import GeminiDDP
         model = GeminiDDP(model,
                           device=get_current_device(),
-                          placement_policy=placememt_policy,
+                          placement_policy=placement_policy,
                           pin_memory=True,
                           search_range_mb=32)
     elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
         from colossalai.gemini import ChunkManager, GeminiManager
         chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
-        gemini_manager = GeminiManager(placememt_policy, chunk_manager)
+        gemini_manager = GeminiManager(placement_policy, chunk_manager)
         chunk_manager = ChunkManager(chunk_size,
                                      pg,
                                      enable_distributed_storage=True,
-                                     init_device=GeminiManager.get_default_device(placememt_policy))
+                                     init_device=GeminiManager.get_default_device(placement_policy))
         model = ZeroDDP(model, gemini_manager)
     else:
         raise NotImplemented(f"CAI version {cai_version} is not supported")