allow customized head_dim

Pull Request resolved: #6872

This is for resolving the ask in this [post](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1574875706716050/).

Similar change in HF: huggingface/transformers#32502
ghstack-source-id: 254176606

Differential Revision: [D65974454](https://our.internmc.facebook.com/intern/diff/D65974454/)

examples/models/llama/llama_transformer.py

@@ -85,6 +85,7 @@ class ModelArgs:
     n_kv_heads: Optional[int] = None
     vocab_size: int = -1  # defined later by tokenizer
     hidden_dim: Optional[int] = None
+    head_dim: Optional[int] = None
     multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
     ffn_dim_multiplier: Optional[float] = None
     norm_eps: float = 1e-5
@@ -142,6 +143,9 @@ def __post_init__(self):
                 hidden_dim = int(self.ffn_dim_multiplier * hidden_dim)
             self.hidden_dim = find_multiple(hidden_dim, multiple_of)
 
+        if self.head_dim is None:
+            self.head_dim = self.dim // self.n_heads
+
 
 class KVCache(nn.Module):
     def __init__(
@@ -272,7 +276,7 @@ def __init__(self, args: ModelArgs, layer_id: int):
         self.n_local_heads = self.n_heads // model_parallel_size
         self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
         self.n_rep = self.n_local_heads // self.n_local_kv_heads
-        self.head_dim = args.dim // self.n_heads
+        self.head_dim = args.head_dim
         self.max_batch_size = args.max_batch_size
         self.max_seq_len = args.max_seq_len
         self.dim = args.dim
@@ -304,7 +308,7 @@ def __init__(self, args: ModelArgs, layer_id: int):
             )
             self.SDPA = SDPA(
                 kv_cache=self.kv_cache,
-                dim=self.dim,
+                dim=self.n_local_heads * self.head_dim,
                 head_dim=self.head_dim,
                 n_rep=self.n_rep,
                 max_seq_len=self.max_seq_len,
@@ -425,7 +429,7 @@ def __init__(self, layer_id: int, args: ModelArgs):
         self.use_kv_cache = args.use_kv_cache
         self.n_heads = args.n_heads
         self.dim = args.dim
-        self.head_dim = args.dim // args.n_heads
+        self.head_dim = args.head_dim
         self.attention = Attention(args, layer_id)
         if args.moe:
             self.block_sparse_moe = MOEFeedForward(args)
@@ -472,7 +476,7 @@ def __init__(self, params: ModelArgs):
                 precompute_freqs_cis, use_scaled=params.use_scaled_rope
             )
         freqs_cos, freqs_sin = self.precompute_freqs_cis(
-            params.dim // params.n_heads,
+            params.head_dim,
             (
                 params.max_seq_len  # Normal llama2.
                 if params.ffn_dim_multiplier is None