[SmolLM3] Add Backbone, CausalLM + Converter for HuggingFace Weights

keras_hub/src/models/smollm3/smollm3_utils.py

@@ -0,0 +1,52 @@
+from keras import ops
+from keras import random
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return ops.concatenate((-x2, x1), axis=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, expansion_axis=1):
+    cos = ops.expand_dims(cos, expansion_axis)
+    sin = ops.expand_dims(sin, expansion_axis)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states, n_rep):
+    batch, num_key_value_heads, slen, head_dim = ops.shape(hidden_states)
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = ops.expand_dims(hidden_states, axis=2)
+    target_shape = (batch, num_key_value_heads, n_rep, slen, head_dim)
+    hidden_states = ops.broadcast_to(hidden_states, target_shape)
+    return ops.reshape(hidden_states, [batch, num_key_value_heads * n_rep, slen, head_dim])
+
+
+def eager_attention_forward(
+    module,
+    query,
+    key,
+    value,
+    attention_mask,
+    scaling: float,
+    dropout: float = 0.0,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = ops.matmul(query, ops.transpose(key_states, axes=(0, 1, 3, 2))) * scaling
+
+    # Apply attention mask if provided
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : ops.shape(key_states)[-2]]
+        attn_weights = ops.add(attn_weights, causal_mask)
+
+    attn_weights = ops.softmax(attn_weights, axis=-1)
+    attn_weights = random.dropout(attn_weights, rate=dropout)
+    attn_output = ops.matmul(attn_weights, value_states)
+    attn_output = ops.transpose(attn_output, axes=(0, 2, 1, 3))
+
+    return attn_output, attn_weights