Implement tied LM head & word embeddings for Qwen3

src/olmo_core/nn/hf/config.py

@@ -79,7 +79,7 @@ def _get_flex_olmo_config(model: MoETransformer) -> PretrainedConfig:
         rms_norm_eps=block.feed_forward_norm.eps,
         num_experts_per_tok=block.feed_forward_moe.router.top_k,
         num_experts=block.feed_forward_moe.router.num_experts,
-        tie_word_embeddings=False,
+        tie_word_embeddings=model.tie_word_embeddings,
     )
 
 
@@ -133,7 +133,7 @@ def get_hf_config(model: Transformer) -> PretrainedConfig:
         "bos_token_id": None,
         "eos_token_id": None,
         "rms_norm_eps": first_block.feed_forward_norm.eps,
-        "tie_word_embeddings": False,
+        "tie_word_embeddings": model.tie_word_embeddings,
     }
 
     # The OLMo 3 model family is identical to the OLMo 2 model family, except:
@@ -387,7 +387,7 @@ def get_hybrid_hf_config(
         "attention_bias": attn.w_out.bias is not None,
         "attention_dropout": 0.0,
         "rms_norm_eps": attn_block.feed_forward_norm.eps,  # todo: revisit
-        "tie_word_embeddings": False,
+        "tie_word_embeddings": model.tie_word_embeddings,
         # Hybrid layer configuration
         "layer_types": layer_types,
         # GDN (linear attention) parameters

src/olmo_core/nn/hf/convert.py

@@ -473,6 +473,9 @@ def convert_state_from_hf(
     if model_type == "gemma3_text":
         converted_state = _apply_gemma3_norm_transform(converted_state)
 
+    if config.tie_word_embeddings:
+        converted_state["lm_head.w_out.weight"] = converted_state["embeddings.weight"]
+
     return converted_state
 
 
@@ -537,12 +540,11 @@ def convert_state_to_hf(
         :class:`DTensor` or :class:`ShardedTensor`
     """
 
-    model_type = getattr(config, "model_type", None)
-    converter = _get_converter_to_hf(model_type)
+    converter = _get_converter_to_hf(config.model_type)
 
     converted_state = _convert_state(config, olmo_core_state, converter)
 
-    if model_type == "gemma3_text":
+    if config.model_type == "gemma3_text":
         converted_state = _apply_gemma3_norm_inverse_transform(converted_state)
 
     return converted_state

src/olmo_core/nn/transformer/config.py

@@ -328,8 +328,13 @@ class TransformerConfig(ModelConfig):
     block_pattern: Optional[List[str]] = None
     block_overrides: Optional[Dict[int, TransformerBlockConfig]] = None
     embed_scale: Optional[float] = None
+    tie_word_embeddings: bool = False
 
     def __post_init__(self):
+        if self.tie_word_embeddings and self.name == TransformerType.normalized:
+            raise OLMoConfigurationError(
+                "Tying word embeddings is not supported with the normalized transformer"
+            )
         validate_block_resolution_config(
             n_layers=self.n_layers,
             block=self.block,
@@ -380,6 +385,7 @@ def build(
                 block_overrides=self.block_overrides,
                 block_pattern=self.block_pattern,
                 embed_scale=self.embed_scale,
+                tie_word_embeddings=self.tie_word_embeddings,
             )
         elif self.name == TransformerType.normalized:
             assert self.embedding_norm is None
@@ -414,6 +420,7 @@ def build(
                 embedding_init_std=self.embedding_init_std,
                 block_overrides=self.block_overrides,
                 block_pattern=self.block_pattern,
+                tie_word_embeddings=self.tie_word_embeddings,
             )
         else:
             raise NotImplementedError(self.name)
@@ -466,6 +473,10 @@ def num_params(self) -> int:
         # LM head.
         num_params += self.lm_head.num_params(self.d_model, self.vocab_size)
 
+        # The LM head weight is shared with the embeddings when tied.
+        if self.tie_word_embeddings:
+            num_params -= self.d_model * self.vocab_size
+
         return num_params
 
     @property
@@ -487,6 +498,10 @@ def num_active_params(self) -> int:
         # LM head.
         num_active_params += self.lm_head.num_params(self.d_model, self.vocab_size)
 
+        # The LM head weight is shared with the embeddings when tied.
+        if self.tie_word_embeddings:
+            num_active_params -= self.d_model * self.vocab_size
+
         return num_active_params
 
     @property
@@ -1301,6 +1316,7 @@ def qwen3_0_6B(cls, vocab_size: int, **kwargs) -> "TransformerConfig":
             feed_forward=FeedForwardConfig(
                 hidden_size=3072, bias=False, dtype=kwargs.get("dtype", DType.float32)
             ),
+            tie_word_embeddings=kwargs.pop("tie_word_embeddings", True),
             **kwargs,
         )
 
@@ -1337,6 +1353,7 @@ def qwen3_1_7B(cls, vocab_size: int, **kwargs) -> "TransformerConfig":
             feed_forward=FeedForwardConfig(
                 hidden_size=6144, bias=False, dtype=kwargs.get("dtype", DType.float32)
             ),
+            tie_word_embeddings=kwargs.pop("tie_word_embeddings", True),
             **kwargs,
         )
 
@@ -1373,6 +1390,7 @@ def qwen3_4B(cls, vocab_size: int, **kwargs) -> "TransformerConfig":
             feed_forward=FeedForwardConfig(
                 hidden_size=9728, bias=False, dtype=kwargs.get("dtype", DType.float32)
             ),
+            tie_word_embeddings=kwargs.pop("tie_word_embeddings", True),
             **kwargs,
         )
 

src/olmo_core/nn/transformer/model.py

@@ -46,7 +46,7 @@
 from ..buffer_cache import BufferCache
 from ..functional import l2_normalize
 from ..layer_norm import LayerNormConfig
-from ..lm_head import LMHeadConfig, LMOutputWithLoss
+from ..lm_head import LMHeadConfig, LMLossImplementation, LMOutputWithLoss
 from ..moe import MoEBase
 from ..rope import RoPEBuffers, RotaryEmbeddingBase
 from ..utils import selective_checkpointing_context_fn
@@ -117,6 +117,7 @@ def __init__(
         block_overrides: Optional[Dict[int, TransformerBlockConfig]] = None,
         block_pattern: Optional[List[str]] = None,
         embed_scale: Optional[float] = None,
+        tie_word_embeddings: bool = False,
     ):
         super().__init__()
 
@@ -160,6 +161,10 @@ def __init__(
             d_model=d_model, vocab_size=vocab_size, init_device=init_device
         )
 
+        self.tie_word_embeddings = tie_word_embeddings
+        if tie_word_embeddings:
+            self._tie_weights()
+
         self.init_device = init_device
         self.init_method = InitMethod(init_method)
         self.init_seed = init_seed
@@ -183,6 +188,15 @@ def __init__(
         self.num_params
         self.num_non_embedding_params
 
+    def _tie_weights(self) -> None:
+        if self.embeddings is None or self.lm_head is None:
+            raise OLMoConfigurationError(
+                "Cannot tie word embeddings without both embeddings and an LM head"
+            )
+        if self.lm_head.w_out.bias is not None:
+            raise OLMoConfigurationError("Cannot tie word embeddings when the LM head uses a bias")
+        self.lm_head.w_out.weight = self.embeddings.weight
+
     def _validate_block(self, block: TransformerBlockBase) -> TransformerBlockBase:
         return block
 
@@ -295,6 +309,10 @@ def init_weights(
                 generator=generator,
             )
 
+        # Re-establish weight tying since `to_empty` above allocates fresh storage.
+        if self.tie_word_embeddings:
+            self._tie_weights()
+
         for block in self.blocks.values():
             # This might fail if it's wrapped.
             #  assert isinstance(block, TransformerBlock)
@@ -345,7 +363,7 @@ def init_weights(
                 if max_seq_len is not None and att.rope is not None:
                     att.rope.warmup_cache(max_seq_len, device)
 
-        if self.lm_head is not None:
+        if self.lm_head is not None and not self.tie_word_embeddings:
             self.init_method.init_final_w_out(
                 self.lm_head.w_out,
                 d_model=self.d_model,
@@ -616,6 +634,16 @@ def apply_tp(self, tp_mesh: DeviceMesh, float8_enabled: Optional[bool] = None):
         :param loss_parallel: Set to ``True`` if parallelizing the loss function as well.
         :param float8_enabled: Set this to ``True`` if training with float8 linear layers.
         """
+        if self.tie_word_embeddings and (
+            self.lm_head is None
+            or self.lm_head.loss_implementation == LMLossImplementation.fused_linear
+        ):
+            raise NotImplementedError(
+                "Tensor parallelism with tied word embeddings requires the default loss "
+                "implementation; the fused-linear loss replicates the LM head weight, which is "
+                "incompatible with the vocab-sharded embedding."
+            )
+
         if float8_enabled is None:
             float8_enabled = self.fp8_enabled
         elif not float8_enabled and self.fp8_enabled:
@@ -646,6 +674,12 @@ def apply_tp(self, tp_mesh: DeviceMesh, float8_enabled: Optional[bool] = None):
         if self.lm_head is not None:
             self.lm_head.apply_tp(tp_mesh, input_layouts=(Shard(1), Replicate()))
 
+        # The embedding (RowwiseParallel) and the LM head (ColwiseParallel) both shard their
+        # weight along the vocab dimension, so re-point the head at the embedding's sharded
+        # parameter to restore the tie that `parallelize_module` broke.
+        if self.tie_word_embeddings and self.embeddings is not None and self.lm_head is not None:
+            self._tie_weights()
+
         self._tp_enabled = True
         self._tp_mesh = tp_mesh
 
@@ -831,7 +865,9 @@ def apply_fsdp(
                 mp_policy=mp_policy,
             )
 
-        if self.embeddings is not None:
+        # When weights are tied the embeddings and LM head share a parameter, so they must
+        # stay in the same FSDP group (the root) rather than being sharded separately.
+        if self.embeddings is not None and not self.tie_word_embeddings:
             fully_shard(
                 self.embeddings,
                 reshard_after_forward=reshard_after_forward,
@@ -843,7 +879,7 @@ def apply_fsdp(
         if wrapping_strategy != TransformerDataParallelWrappingStrategy.blocks:
             if self.embedding_norm is not None:
                 fully_shard(self.embedding_norm, **fsdp_config)
-            if self.lm_head is not None:
+            if self.lm_head is not None and not self.tie_word_embeddings:
                 fully_shard(self.lm_head, reshard_after_forward=False, **fsdp_config)
 
         fully_shard(self, reshard_after_forward=reshard_after_forward, **fsdp_config)

src/olmo_core/train/train_module/transformer/config.py

@@ -93,6 +93,15 @@ def split_model(
         self, model: Transformer, *, pp_mesh: DeviceMesh, device: torch.device
     ) -> Tuple[List[PipelineStage], List[Transformer]]:
         split_points = self.get_split_points(model.n_layers)
+        num_stages = len(split_points) + 1
+
+        if num_stages > 1 and model.tie_word_embeddings:
+            raise NotImplementedError(
+                "Pipeline parallelism with tied word embeddings is not supported: the input "
+                "embeddings and LM head are placed on different pipeline stages, so they cannot "
+                "share a weight."
+            )
+
         pp_rank = pp_mesh.get_local_rank()
 
         def build_stage(
@@ -128,7 +137,6 @@ def build_stage(
             )
             return stage, model_chunk
 
-        num_stages = len(split_points) + 1
         stage_idx = pp_rank
 
         stages = []

src/test/nn/hf/convert_test.py

@@ -118,6 +118,32 @@ def test_convert_state_from_hf_and_flatten():
         )
 
 
+def test_convert_state_from_hf_ties_word_embeddings():
+    hf_config = AutoConfig.for_model(
+        "qwen3",
+        vocab_size=64,
+        hidden_size=16,
+        intermediate_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        num_key_value_heads=2,
+        max_position_embeddings=64,
+        tie_word_embeddings=True,
+    )
+
+    # A tied HF checkpoint omits `lm_head.weight`.
+    hf_state = {
+        "model.embed_tokens.weight": torch.randn(hf_config.vocab_size, hf_config.hidden_size)
+    }
+
+    converted_state = convert_state_from_hf(hf_config, hf_state, model_type="qwen3")
+
+    assert "lm_head.w_out.weight" in converted_state
+    torch.testing.assert_close(
+        converted_state["lm_head.w_out.weight"], converted_state["embeddings.weight"]
+    )
+
+
 def test_convert_state_to_hf():
     hf_config = _get_olmo2_config()
 
@@ -228,35 +254,6 @@ def test_convert_state_to_flex_olmo_hf():
         )
 
 
-def _roundtrip_norm_keys(model_id, model_type, norm_suffixes, forbidden=()):
-    hf_config = AutoConfig.from_pretrained(model_id)
-    hf_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
-    hf_state = {k: v.detach().cpu() for k, v in hf_model.state_dict().items()}
-    del hf_model
-
-    oc_state = convert_state_from_hf(hf_config, hf_state, model_type=model_type)
-    hf_roundtrip = convert_state_to_hf(hf_config, oc_state)
-
-    for i in range(hf_config.num_hidden_layers):
-        for suffix in norm_suffixes:
-            key = f"model.layers.{i}.{suffix}"
-            assert key in hf_roundtrip, f"missing {key} in round-tripped state"
-            torch.testing.assert_close(hf_roundtrip[key], hf_state[key])
-        for suffix in forbidden:
-            assert (
-                f"model.layers.{i}.{suffix}" not in hf_roundtrip
-            ), f"unexpected key model.layers.{i}.{suffix}"
-
-
-def test_qwen3_0_6b_roundtrip_pre_norm():
-    _roundtrip_norm_keys(
-        "Qwen/Qwen3-0.6B",
-        model_type="qwen3",
-        norm_suffixes=("input_layernorm.weight", "post_attention_layernorm.weight"),
-        forbidden=("post_feedforward_layernorm.weight",),
-    )
-
-
 def test_llama_tiny_roundtrip_pre_norm():
     hf_config = AutoConfig.for_model(
         "llama",
@@ -287,20 +284,14 @@ def test_llama_tiny_roundtrip_pre_norm():
         assert f"model.layers.{i}.post_feedforward_layernorm.weight" not in hf_roundtrip
 
 
-def test_gemma3_270m_roundtrip_pre_norm():
-    _roundtrip_norm_keys(
-        "google/gemma-3-270m",
-        model_type="gemma3_text",
-        norm_suffixes=(
-            "input_layernorm.weight",
-            "post_attention_layernorm.weight",
-            "pre_feedforward_layernorm.weight",
-            "post_feedforward_layernorm.weight",
-        ),
-    )
-
-
-def _assert_logprobs_match_after_roundtrip(model_id: str, model_type: str):
+@pytest.mark.parametrize(
+    "model_id, model_type",
+    [
+        pytest.param("Qwen/Qwen3-0.6B", "qwen3", id="qwen3"),
+        pytest.param("google/gemma-3-270m", "gemma3_text", id="gemma3"),
+    ],
+)
+def test_logprobs_match_after_roundtrip(model_id: str, model_type: str):
     hf_config = AutoConfig.from_pretrained(model_id)
     hf_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
     hf_model.eval()
@@ -323,11 +314,3 @@ def _assert_logprobs_match_after_roundtrip(model_id: str, model_type: str):
     rt_logprobs = torch.log_softmax(rt_logits, dim=-1)
 
     torch.testing.assert_close(rt_logprobs, ref_logprobs, rtol=1e-5, atol=1e-5)
-
-
-def test_qwen3_0_6b_logprobs_roundtrip():
-    _assert_logprobs_match_after_roundtrip("Qwen/Qwen3-0.6B", model_type="qwen3")
-
-
-def test_gemma3_270m_logprobs_roundtrip():
-    _assert_logprobs_match_after_roundtrip("google/gemma-3-270m", model_type="gemma3_text")