Allow-head-dim

src/transformers/models/llama/configuration_llama.py

@@ -123,6 +123,8 @@ class LlamaConfig(PretrainedConfig):
             The dropout ratio for the attention probabilities.
         mlp_bias (`bool`, *optional*, defaults to `False`):
             Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+        head_dim (`int`, *optional*):
+            The attention head dimension. If None, it will default to hidden_size // num_heads
 
     ```python
     >>> from transformers import LlamaModel, LlamaConfig
@@ -163,6 +165,7 @@ def __init__(
         attention_bias=False,
         attention_dropout=0.0,
         mlp_bias=False,
+        head_dim=None,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -187,7 +190,7 @@ def __init__(
         self.attention_bias = attention_bias
         self.attention_dropout = attention_dropout
         self.mlp_bias = mlp_bias
-
+        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
         # Validate the correctness of rotary position embeddings parameters
         # BC: if there is a 'type' field, move it to 'rope_type'.
         if self.rope_scaling is not None and "type" in self.rope_scaling:

src/transformers/models/llama/modeling_flax_llama.py

@@ -214,12 +214,6 @@ def setup(self):
         self.k_proj = dense(self.num_key_value_heads * self.head_dim)
         self.v_proj = dense(self.num_key_value_heads * self.head_dim)
         self.o_proj = dense(self.embed_dim)
-        if (self.head_dim * self.num_heads) != self.embed_dim:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.embed_dim}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-
         self.causal_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool")
         self.rotary_emb = FlaxLlamaRotaryEmbedding(config, dtype=self.dtype)
 

src/transformers/models/llama/modeling_llama.py

@@ -340,23 +340,17 @@ def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
         self.attention_dropout = config.attention_dropout
         self.hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
+        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
         self.num_key_value_heads = config.num_key_value_heads
         self.num_key_value_groups = self.num_heads // self.num_key_value_heads
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.is_causal = True
 
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-
         self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
         self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
         self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
 
         # TODO (joao): remove in v4.45 (RoPE is computed in the model, not in the decoder layers)
         self.rotary_emb = LlamaRotaryEmbedding(config=self.config)
@@ -420,7 +414,6 @@ def forward(
 
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
-
         attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
 
         if attention_mask is not None:  # no matter the length, we just slice it