Support Tied Weights in Llama Models (#777)

The new smaller Llama 3.2 1B and 3.2 3B models have tied weights - so
Levanter throws an error currently if we try to import these models.


https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct/blob/main/config.json
![Screenshot 2024-10-24 8 17 03
PM](https://github.com/user-attachments/assets/08e79ed7-cab5-43f0-9ca6-f90e2fe73249)

This adds HF support for that argument and just switches to using
embedding.unembed when Embeddings are tied!

src/levanter/models/llama.py

@@ -64,6 +64,7 @@ class LlamaConfig(HFCompatConfig):
     activation_function: str = "silu"
     initializer_range: float = 0.02
     layer_norm_epsilon: float = 1e-5
+    tie_word_embeddings: bool = False
 
     # Attention-related config
     upcast_attn: bool = False
@@ -120,6 +121,7 @@ def from_hf_config(cls, hf_config: HfConfig):
             activation_function=hf_config.hidden_act,
             initializer_range=hf_config.initializer_range,
             layer_norm_epsilon=hf_config.rms_norm_eps,
+            tie_word_embeddings=hf_config.tie_word_embeddings,
             rope=rope_config,
         )
 
@@ -148,6 +150,7 @@ def to_hf_config(self, vocab_size: int, config_overrides: Optional[Dict] = None)
             hidden_act=self.activation_function,
             initializer_range=self.initializer_range,
             rms_norm_eps=self.layer_norm_epsilon,
+            tie_word_embeddings=self.tie_word_embeddings,
             # rope_scaling=self.rope_scaling,
             vocab_size=vocab_size,
             rope_theta=rope_theta,
@@ -504,7 +507,7 @@ def resize_embeddings(self, new_size: int, key: Optional[PRNGKeyArray] = None):
 class LlamaLMHeadModel(eqx.Module, LmHeadModel[LlamaConfig], StateDictSerializationMixin):
     transformer: LlamaTransformer
     embeddings: LlamaEmbedding
-    lm_head: hnn.Linear
+    lm_head: Optional[hnn.Linear]
 
     @property
     def config(self):
@@ -523,7 +526,11 @@ def init(cls, Vocab: Axis, config: LlamaConfig, *, key) -> "LlamaLMHeadModel":
         k_t, k_emb = jrandom.split(key, 2)
         transformer = LlamaTransformer.init(config, key=k_t)
         embeddings = LlamaEmbedding.init(Vocab, config, key=k_emb)
-        lm_head = hnn.Linear.init(In=config.Embed, Out=Vocab, key=k_emb, use_bias=False, out_first=True)
+        if config.tie_word_embeddings:
+            lm_head = None
+        else:
+            lm_head = hnn.Linear.init(In=config.Embed, Out=Vocab, key=k_emb, use_bias=False, out_first=True)
+
         return LlamaLMHeadModel(transformer, embeddings, lm_head)
 
     def __call__(
@@ -544,38 +551,45 @@ def __call__(
         k_t, k_head = maybe_rng_split(key, 2)
         x = self.embeddings.embed(input_ids)
         x = self.transformer(x, attn_mask=attn_mask, key=k_t)
-        lm_logits = self.lm_head(x, key=k_head)
+        if self.lm_head:
+            lm_logits = self.lm_head(x, key=k_head)
+        else:
+            lm_logits = self.embeddings.unembed(x)
         return lm_logits
 
     def resize_vocab(self, new_size: int, key=None) -> "LmHeadModel[LlamaConfig]":
         new_Vocab = self.Vocab.resize(new_size)
         k1, k2 = maybe_rng_split(key, 2)
         new_embeddings = self.embeddings.resize_embeddings(new_size, key=k1)
-        new_lm_matrix = hax.tree_util.resize_axis(self.lm_head.weight, self.Vocab, new_size, key=k2)
-        new_lm_head = dataclasses.replace(self.lm_head, Out=new_Vocab, weight=new_lm_matrix)
-
-        return dataclasses.replace(self, embeddings=new_embeddings, lm_head=new_lm_head)
+        if self.lm_head is not None:
+            new_lm_matrix = hax.tree_util.resize_axis(self.lm_head.weight, self.Vocab, new_size, key=k2)
+            new_lm_head = dataclasses.replace(self.lm_head, Out=new_Vocab, weight=new_lm_matrix)
+            return dataclasses.replace(self, embeddings=new_embeddings, lm_head=new_lm_head)
+        else:
+            return dataclasses.replace(self, embeddings=new_embeddings)
 
     def _state_dict_key_map(self) -> Dict[str, Optional[str]]:
         return {"transformer": "model", "embeddings": None}
 
     def from_state_dict(self, state_dict: StateDict, prefix: Optional[str] = None):
         # unflatten the linear layers of HF state_dict to match the shape of LlamaMlp
         d = state_dict.copy()
-        d.update(
-            unflatten_linear_layers(
-                apply_prefix(prefix, "lm_head"), state_dict, self.lm_head, out_dims_first_in_dict=True
+        if self.lm_head is not None:
+            d.update(
+                unflatten_linear_layers(
+                    apply_prefix(prefix, "lm_head"), state_dict, self.lm_head, out_dims_first_in_dict=True
+                )
             )
-        )
         return super().from_state_dict(d, prefix)
 
     def update_state_dict(self, state_dict: StateDict, prefix: Optional[str] = None) -> StateDict:
         my_dict: StateDict = {}
         super().update_state_dict(my_dict, prefix=prefix)
 
-        my_dict.update(
-            flatten_linear_layers(apply_prefix(prefix, "lm_head"), self.lm_head, out_dims_first_in_dict=True)
-        )
+        if self.lm_head is not None:
+            my_dict.update(
+                flatten_linear_layers(apply_prefix(prefix, "lm_head"), self.lm_head, out_dims_first_in_dict=True)
+            )
 
         state_dict.update(my_dict)
         return state_dict