Fix kimi yarn settings for draft model

configs/draft_models/kimi_k25_eagle3.json

@@ -18,8 +18,16 @@
   "num_hidden_layers": 1,
   "num_key_value_heads": 64,
   "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
+  "rope_scaling": {
+    "beta_fast": 32.0,
+    "beta_slow": 1.0,
+    "factor": 64.0,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 4096,
+    "type": "yarn"
+  },
+  "rope_theta": 50000.0,
   "sliding_window": null,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",

tests/test_draft_model_config.py

@@ -0,0 +1,128 @@
+from types import SimpleNamespace
+
+from torchspec.config.utils import generate_draft_model_config
+
+
+def test_generate_draft_model_config_preserves_rope_fields(monkeypatch):
+    rope_scaling = {
+        "type": "yarn",
+        "factor": 64.0,
+        "original_max_position_embeddings": 4096,
+        "beta_fast": 32.0,
+        "beta_slow": 1.0,
+        "mscale": 1.0,
+        "mscale_all_dim": 1.0,
+    }
+    text_config = SimpleNamespace(
+        vocab_size=32000,
+        hidden_size=4096,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        intermediate_size=14336,
+        max_position_embeddings=262144,
+        rope_theta=5000000,
+        rope_scaling=rope_scaling,
+        rms_norm_eps=1e-6,
+        hidden_act="silu",
+        bos_token_id=1,
+        eos_token_id=2,
+        torch_dtype="bfloat16",
+    )
+    target_config = SimpleNamespace(text_config=text_config)
+
+    class DummyTokenizer:
+        def __len__(self):
+            return 32000
+
+    monkeypatch.setattr(
+        "torchspec.config.utils.AutoConfig.from_pretrained",
+        lambda *args, **kwargs: target_config,
+    )
+    monkeypatch.setattr(
+        "torchspec.config.utils.AutoTokenizer.from_pretrained",
+        lambda *args, **kwargs: DummyTokenizer(),
+    )
+
+    draft_config = generate_draft_model_config("dummy-model")
+
+    assert draft_config["max_position_embeddings"] == 262144
+    assert draft_config["rope_theta"] == 5000000
+    assert draft_config["rope_scaling"] == rope_scaling
+
+
+def test_generate_draft_model_config_copies_rope_scaling(monkeypatch):
+    rope_scaling = {"type": "yarn", "factor": 8.0, "original_max_position_embeddings": 8192}
+    text_config = SimpleNamespace(
+        vocab_size=32000,
+        hidden_size=2048,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        intermediate_size=8192,
+        max_position_embeddings=65536,
+        rope_theta=1000000,
+        rope_scaling=rope_scaling,
+        rms_norm_eps=1e-6,
+        hidden_act="silu",
+        bos_token_id=1,
+        eos_token_id=2,
+        torch_dtype="bfloat16",
+    )
+    target_config = SimpleNamespace(text_config=text_config)
+
+    class DummyTokenizer:
+        def __len__(self):
+            return 32000
+
+    monkeypatch.setattr(
+        "torchspec.config.utils.AutoConfig.from_pretrained",
+        lambda *args, **kwargs: target_config,
+    )
+    monkeypatch.setattr(
+        "torchspec.config.utils.AutoTokenizer.from_pretrained",
+        lambda *args, **kwargs: DummyTokenizer(),
+    )
+
+    draft_config = generate_draft_model_config("dummy-model")
+    rope_scaling["factor"] = 999.0
+
+    assert draft_config["rope_scaling"]["factor"] == 8.0
+
+
+def test_generate_draft_model_config_fills_yarn_defaults(monkeypatch):
+    rope_scaling = {"type": "yarn", "factor": 8.0, "original_max_position_embeddings": 8192}
+    text_config = SimpleNamespace(
+        vocab_size=32000,
+        hidden_size=2048,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        intermediate_size=8192,
+        max_position_embeddings=65536,
+        rope_theta=1000000,
+        rope_scaling=rope_scaling,
+        rms_norm_eps=1e-6,
+        hidden_act="silu",
+        bos_token_id=1,
+        eos_token_id=2,
+        torch_dtype="bfloat16",
+    )
+    target_config = SimpleNamespace(text_config=text_config)
+
+    class DummyTokenizer:
+        def __len__(self):
+            return 32000
+
+    monkeypatch.setattr(
+        "torchspec.config.utils.AutoConfig.from_pretrained",
+        lambda *args, **kwargs: target_config,
+    )
+    monkeypatch.setattr(
+        "torchspec.config.utils.AutoTokenizer.from_pretrained",
+        lambda *args, **kwargs: DummyTokenizer(),
+    )
+
+    draft_config = generate_draft_model_config("dummy-model")
+
+    assert draft_config["rope_scaling"]["beta_fast"] == 32.0
+    assert draft_config["rope_scaling"]["beta_slow"] == 1.0
+    assert draft_config["rope_scaling"]["mscale"] == 1.0
+    assert draft_config["rope_scaling"]["mscale_all_dim"] == 0.0

tests/test_eagle3_loss.py

@@ -294,6 +294,42 @@ def test_losses_match_cuda(self):
             )
 
 
+class TestRotaryConfigWiring(unittest.TestCase):
+    """Model config should fully wire RoPE settings into rotary embeddings."""
+
+    def test_yarn_uses_rope_theta_as_base(self):
+        config = LlamaConfig(
+            hidden_size=128,
+            num_attention_heads=4,
+            num_key_value_heads=4,
+            intermediate_size=512,
+            max_position_embeddings=262144,
+            vocab_size=256,
+            hidden_act="silu",
+            rms_norm_eps=1e-6,
+            rope_theta=50000.0,
+            rope_scaling={
+                "type": "yarn",
+                "factor": 64.0,
+                "original_max_position_embeddings": 4096,
+                "beta_fast": 32.0,
+                "beta_slow": 1.0,
+                "mscale": 1.0,
+                "mscale_all_dim": 1.0,
+            },
+            pretraining_tp=1,
+            pad_token_id=0,
+        )
+        config.draft_vocab_size = 256
+
+        model = LlamaForCausalLMEagle3(config, attention_backend="sdpa")
+        rotary = model.midlayer.self_attn.rotary_emb
+
+        self.assertEqual(rotary.base, 50000.0)
+        self.assertEqual(rotary.original_max_position_embeddings, 4096)
+        self.assertEqual(rotary.scaling_factor, 64.0)
+
+
 def _make_mask_patterns(BT):
     """Return (name, valid_idx) pairs covering diverse masking patterns."""
     patterns = []

torchspec/config/utils.py

@@ -18,6 +18,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
+import copy
 import json
 import logging
 import warnings
@@ -28,6 +29,35 @@
 logger = logging.getLogger(__name__)
 
 
+def _copy_config_value(value):
+    if hasattr(value, "to_dict"):
+        return value.to_dict()
+    return copy.deepcopy(value)
+
+
+def _normalize_rope_scaling(rope_scaling):
+    if rope_scaling is None:
+        return None
+
+    normalized = _copy_config_value(rope_scaling)
+    if not isinstance(normalized, dict):
+        return normalized
+
+    scaling_type = normalized.get("rope_type", normalized.get("type"))
+    if scaling_type == "yarn":
+        yarn_defaults = {
+            "beta_fast": 32.0,
+            "beta_slow": 1.0,
+            "mscale": 1.0,
+            "mscale_all_dim": 0.0,
+        }
+        for key, default in yarn_defaults.items():
+            if normalized.get(key) is None:
+                normalized[key] = default
+
+    return normalized
+
+
 def generate_draft_model_config(
     target_model_path: str, template_config_path: str = None, cache_dir: str = None
 ):
@@ -85,6 +115,8 @@ def generate_draft_model_config(
         "num_key_value_heads": "num_key_value_heads",
         "intermediate_size": "intermediate_size",
         "max_position_embeddings": "max_position_embeddings",
+        "rope_theta": "rope_theta",
+        "rope_scaling": "rope_scaling",
         "rms_norm_eps": "rms_norm_eps",
         "hidden_act": "hidden_act",
         "bos_token_id": "bos_token_id",
@@ -101,6 +133,10 @@ def generate_draft_model_config(
             continue
         if target_param == "torch_dtype" and isinstance(value, torch.dtype):
             value = str(value).replace("torch.", "")
+        else:
+            value = _copy_config_value(value)
+            if target_param == "rope_scaling":
+                value = _normalize_rope_scaling(value)
         draft_config[draft_param] = value
 
     draft_config["num_hidden_layers"] = 1

torchspec/models/draft/llama3_eagle.py

@@ -1071,6 +1071,7 @@ def rope_get(key, default=None):
                 self.rotary_emb = LlamaYarnRotaryEmbedding(
                     self.head_dim,
                     max_position_embeddings=self.max_position_embeddings,
+                    base=getattr(self.config, "rope_theta", 10000),
                     original_max_position_embeddings=rope_get("original_max_position_embeddings"),
                     scaling_factor=scaling_factor,
                     beta_fast=rope_get("beta_fast"),