Add GraniteRMSNorm (huggingface#33177)

* Add GraniteRMSNorm

* [run_slow] granite

src/transformers/models/granite/modeling_granite.py

@@ -30,6 +30,7 @@
 )
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
 from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import ALL_LAYERNORM_LAYERS
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -99,6 +100,30 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
     return causal_mask
 
 
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Granite
+class GraniteRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        GraniteRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+ALL_LAYERNORM_LAYERS.append(GraniteRMSNorm)
+
+
 class GraniteRotaryEmbedding(nn.Module):
     def __init__(self, config: GraniteConfig):
         super().__init__()
@@ -534,8 +559,8 @@ def __init__(self, config: GraniteConfig, layer_idx: int):
         self.self_attn = GRANITE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
 
         self.mlp = GraniteMLP(config)
-        self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_layernorm = GraniteRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GraniteRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
         self.residual_multiplier = config.residual_multiplier
 
@@ -749,7 +774,7 @@ def __init__(self, config: GraniteConfig):
         self.layers = nn.ModuleList(
             [GraniteDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = GraniteRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.gradient_checkpointing = False
 
         self.embedding_multiplier = config.embedding_multiplier

src/transformers/pytorch_utils.py

@@ -24,7 +24,7 @@
 from .utils import is_torch_xla_available, logging
 
 
-ALL_LAYERNORM_LAYERS = [nn.LayerNorm, nn.RMSNorm]
+ALL_LAYERNORM_LAYERS = [nn.LayerNorm]
 
 logger = logging.get_logger(__name__)