Add warning to compile rmsnorm (#505)

as titled, add warning to compile rmsnorm as it's not fully ready yet,
i.e. this issue #497

We can remove this warning once we fix the issue

torchtitan/models/norms.py

@@ -43,6 +43,11 @@ def build_norm(norm_type: str, dim: int, eps: float = 1e-6):
     elif norm_type == "rmsnorm":
         return RMSNorm(dim, eps=eps)
     elif norm_type == "compiled_rmsnorm":
+        import warnings
+
+        warnings.warn(
+            "compiled_rmsnorm is currently experimental and not ready to use yet."
+        )
         return RMSNorm(dim, eps=eps, compile=True)
     elif norm_type == "fused_rmsnorm":
         return FusedRMSNorm(dim, eps=eps)

train_configs/debug_model.toml

@@ -21,7 +21,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama3"
 flavor = "debugmodel"
-norm_type = "compiled_rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
 # test tokenizer.model, for debug purpose only
 tokenizer_path = "./test/assets/test_tiktoken.model"
 

train_configs/llama2_13b.toml

@@ -18,7 +18,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama2"
 flavor = "13B"
-norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / fused_rmsnorm
 tokenizer_path = "./torchtitan/datasets/tokenizer/tokenizer.model"
 
 [optimizer]

train_configs/llama2_70b.toml

@@ -18,7 +18,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama2"
 flavor = "70B"
-norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / fused_rmsnorm
 tokenizer_path = "./torchtitan/datasets/tokenizer/tokenizer.model"
 
 [optimizer]

train_configs/llama2_7b.toml

@@ -17,7 +17,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama2"
 flavor = "7B"
-norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / fused_rmsnorm
 tokenizer_path = "./torchtitan/datasets/tokenizer/tokenizer.model"
 
 [optimizer]

train_configs/llama3_405b.toml

@@ -18,7 +18,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama3"
 flavor = "405B"
-norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / fused_rmsnorm
 tokenizer_path = "./torchtitan/datasets/tokenizer/original/tokenizer.model"
 
 [optimizer]

train_configs/llama3_70b.toml

@@ -18,7 +18,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama3"
 flavor = "70B"
-norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / fused_rmsnorm
 tokenizer_path = "./torchtitan/datasets/tokenizer/original/tokenizer.model"
 
 [optimizer]

train_configs/llama3_8b.toml

@@ -18,7 +18,7 @@ save_tb_folder = "tb"
 [model]
 name = "llama3"
 flavor = "8B"
-norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / compiled_rmsnorm / fused_rmsnorm
+norm_type = "rmsnorm"  # layernorm / np_layernorm / rmsnorm / fused_rmsnorm
 tokenizer_path = "./torchtitan/datasets/tokenizer/original/tokenizer.model"
 
 [optimizer]