ci: pr-2384

src/megatron/bridge/models/conversion/model_bridge.py

@@ -358,17 +358,20 @@ def hf_config_to_provider_kwargs(self, hf_config) -> dict:
         # Map config fields using CONFIG_MAPPING
         # Supports dot notation for nested dict access (e.g., "rope_scaling.factor")
         for hf_name, megatron_name in self.CONFIG_MAPPING:
+            has_value = False
+            value = None
             if "." in hf_name:
                 # Nested dict access: "parent.child" -> getattr(config, parent).get(child)
                 parts = hf_name.split(".", 1)
                 parent = getattr(hf_config, parts[0], None)
                 if parent is not None and isinstance(parent, dict):
-                    value = parent.get(parts[1])
-                else:
-                    value = None
+                    if parts[1] in parent:
+                        value = parent[parts[1]]
+                        has_value = True
             else:
                 value = getattr(hf_config, hf_name, None)
-            if value is not None:
+                has_value = hasattr(hf_config, hf_name)
+            if has_value:
                 provider_kwargs[megatron_name] = value
 
         # Extract rotary_base via compat function (handles both legacy rope_theta
@@ -505,8 +508,9 @@ def megatron_to_hf_config(cls, provider) -> dict:
         # Map config fields using CONFIG_MAPPING (reverse direction)
         # Supports dot notation for nested dict building (e.g., "rope_scaling.factor")
         for hf_name, megatron_name in cls.CONFIG_MAPPING:
+            has_value = hasattr(provider, megatron_name)
             value = getattr(provider, megatron_name, None)
-            if value is not None:
+            if has_value:
                 if "." in hf_name:
                     # Nested dict: "parent.child" -> hf_config["parent"]["child"] = value
                     parts = hf_name.split(".", 1)
@@ -524,8 +528,9 @@ def megatron_to_hf_config(cls, provider) -> dict:
             hf_config["rope_scaling"]["rope_type"] = "yarn"
 
             for hf_key, megatron_key in cls.YARN_ROPE_SCALING_MAPPING:
+                has_value = hasattr(provider, megatron_key)
                 value = getattr(provider, megatron_key, None)
-                if value is not None:
+                if has_value:
                     hf_config["rope_scaling"][hf_key] = value
 
             yarn_correction_range_round_to_int = getattr(provider, "yarn_correction_range_round_to_int", None)

tests/unit_tests/models/deepseek/test_deepseek_bridges.py

@@ -122,6 +122,74 @@ def test_provider_bridge_maps_config(self, mock_pretrained_v2):
         assert provider.bf16 is True
         assert provider.params_dtype == torch.bfloat16
 
+    def test_hf_config_to_provider_kwargs_preserves_none_q_lora_rank(self, mock_pretrained_v2):
+        mock_pretrained_v2.config.q_lora_rank = None
+        bridge = DeepSeekV2Bridge()
+
+        provider_kwargs = bridge.hf_config_to_provider_kwargs(mock_pretrained_v2.config)
+
+        assert "q_lora_rank" in provider_kwargs
+        assert provider_kwargs["q_lora_rank"] is None
+
+    def test_provider_bridge_preserves_none_q_lora_rank(self, mock_pretrained_v2):
+        mock_pretrained_v2.config.q_lora_rank = None
+        bridge = DeepSeekV2Bridge()
+
+        provider = bridge.provider_bridge(mock_pretrained_v2)
+
+        assert provider.q_lora_rank is None
+
+    def test_megatron_to_hf_config_preserves_none_q_lora_rank(self, mock_pretrained_v2):
+        mock_pretrained_v2.config.q_lora_rank = None
+        bridge = DeepSeekV2Bridge()
+        provider = bridge.provider_bridge(mock_pretrained_v2)
+
+        hf_config = bridge.megatron_to_hf_config(provider)
+
+        assert "q_lora_rank" in hf_config
+        assert hf_config["q_lora_rank"] is None
+
+    def test_hf_config_to_provider_kwargs_nested_dot_notation(self, mock_pretrained_v2):
+        """Test that dot-notation CONFIG_MAPPING reads nested dict values (including None)."""
+        bridge = DeepSeekV2Bridge()
+        # Patch CONFIG_MAPPING with a dot-notation entry pointing into rope_scaling dict
+        original = bridge.CONFIG_MAPPING
+        bridge.CONFIG_MAPPING = list(original) + [("rope_scaling.factor", "yarn_rotary_scaling_factor")]
+        mock_pretrained_v2.config.rope_scaling = {"factor": 40, "type": "yarn"}
+
+        kwargs = bridge.hf_config_to_provider_kwargs(mock_pretrained_v2.config)
+
+        bridge.CONFIG_MAPPING = original
+        assert kwargs.get("yarn_rotary_scaling_factor") == 40
+
+    def test_hf_config_to_provider_kwargs_nested_dot_notation_none_value(self, mock_pretrained_v2):
+        """Test that dot-notation CONFIG_MAPPING preserves None values from nested dicts."""
+        bridge = DeepSeekV2Bridge()
+        original = bridge.CONFIG_MAPPING
+        bridge.CONFIG_MAPPING = list(original) + [("rope_scaling.factor", "yarn_rotary_scaling_factor")]
+        mock_pretrained_v2.config.rope_scaling = {"factor": None, "type": "yarn"}
+
+        kwargs = bridge.hf_config_to_provider_kwargs(mock_pretrained_v2.config)
+
+        bridge.CONFIG_MAPPING = original
+        assert "yarn_rotary_scaling_factor" in kwargs
+        assert kwargs["yarn_rotary_scaling_factor"] is None
+
+    def test_megatron_to_hf_config_yarn_none_value(self, mock_pretrained_v2):
+        """Test that YARN_ROPE_SCALING_MAPPING preserves None values on provider."""
+        bridge = DeepSeekV2Bridge()
+        provider = bridge.provider_bridge(mock_pretrained_v2)
+        # Ensure YARN rope_scaling block is emitted
+        provider.yarn_rotary_scaling_factor = 40
+        # Set a YARN key to None — should still appear in hf_config["rope_scaling"]
+        provider.yarn_mscale = None
+
+        hf_config = bridge.megatron_to_hf_config(provider)
+
+        assert "rope_scaling" in hf_config
+        assert "mscale" in hf_config["rope_scaling"]
+        assert hf_config["rope_scaling"]["mscale"] is None
+
 
 class TestDeepSeekV3Bridge:
     """Test cases for DeepSeekV3Bridge."""
@@ -222,6 +290,33 @@ def test_provider_bridge_maps_config(self, mock_pretrained_v3):
         assert provider.bf16 is True
         assert provider.params_dtype == torch.bfloat16
 
+    def test_hf_config_to_provider_kwargs_preserves_none_q_lora_rank(self, mock_pretrained_v3):
+        mock_pretrained_v3.config.q_lora_rank = None
+        bridge = DeepSeekV3Bridge()
+
+        provider_kwargs = bridge.hf_config_to_provider_kwargs(mock_pretrained_v3.config)
+
+        assert "q_lora_rank" in provider_kwargs
+        assert provider_kwargs["q_lora_rank"] is None
+
+    def test_provider_bridge_preserves_none_q_lora_rank(self, mock_pretrained_v3):
+        mock_pretrained_v3.config.q_lora_rank = None
+        bridge = DeepSeekV3Bridge()
+
+        provider = bridge.provider_bridge(mock_pretrained_v3)
+
+        assert provider.q_lora_rank is None
+
+    def test_megatron_to_hf_config_preserves_none_q_lora_rank(self, mock_pretrained_v3):
+        mock_pretrained_v3.config.q_lora_rank = None
+        bridge = DeepSeekV3Bridge()
+        provider = bridge.provider_bridge(mock_pretrained_v3)
+
+        hf_config = bridge.megatron_to_hf_config(provider)
+
+        assert "q_lora_rank" in hf_config
+        assert hf_config["q_lora_rank"] is None
+
     def test_export_injects_inv_freq_for_layer(self, mock_pretrained_v3):
         bridge = DeepSeekV3Bridge()
         bridge.hf_config = mock_pretrained_v3.config

tests/unit_tests/models/gemma_vl/test_gemma3_vl_bridge.py

@@ -28,7 +28,10 @@
 @pytest.fixture
 def mock_text_config():
     """Create a mock text config for Gemma3 VL."""
-    config = Mock()
+    # Use spec=[] so hasattr() only returns True for explicitly-set attributes,
+    # matching real HF config behaviour (Gemma3 text config has no MLA fields
+    # like q_lora_rank, so they must not appear in the provider kwargs).
+    config = Mock(spec=[])
     config.num_hidden_layers = 28
     config.hidden_size = 2560
     config.intermediate_size = 15360
@@ -46,13 +49,7 @@ def mock_text_config():
     config.rope_scaling = None
     config.rope_parameters = None
     config.hidden_act = "gelu_pytorch_tanh"
-    # Set MLA-specific fields to None (these are auto-mapped in CONFIG_MAPPING)
-    config.q_lora_rank = None
-    config.kv_lora_rank = None
-    config.qk_nope_head_dim = None
-    config.qk_rope_head_dim = None
-    config.v_head_dim = None
-    config.num_nextn_predict_layers = None
+    config.torch_dtype = "bfloat16"
     return config
 
 
@@ -367,7 +364,7 @@ def test_provider_bridge_with_minimal_config(self, gemma3_vl_bridge):
         minimal_config = Mock()
 
         # Create minimal text config
-        text_config = Mock()
+        text_config = Mock(spec=[])
         text_config.num_hidden_layers = 18
         text_config.hidden_size = 2048
         text_config.intermediate_size = 8192
@@ -385,13 +382,7 @@ def test_provider_bridge_with_minimal_config(self, gemma3_vl_bridge):
         text_config.rope_scaling = None
         text_config.rope_parameters = None
         text_config.hidden_act = "gelu_pytorch_tanh"
-        # Set MLA-specific fields to None
-        text_config.q_lora_rank = None
-        text_config.kv_lora_rank = None
-        text_config.qk_nope_head_dim = None
-        text_config.qk_rope_head_dim = None
-        text_config.v_head_dim = None
-        text_config.num_nextn_predict_layers = None
+        text_config.torch_dtype = "bfloat16"
 
         # Create minimal vision config
         vision_config = SiglipVisionConfig()

tests/unit_tests/models/llama/test_llama_bridge.py

@@ -23,6 +23,7 @@
 
 from megatron.bridge.models import AutoBridge
 from megatron.bridge.models.conversion.model_bridge import MegatronModelBridge
+from megatron.bridge.models.conversion.transformers_compat import rope_theta_from_hf
 from megatron.bridge.models.gpt_provider import GPTModelProvider
 from megatron.bridge.models.hf_pretrained.causal_lm import PreTrainedCausalLM
 from megatron.bridge.models.llama.llama_bridge import LlamaBridge
@@ -155,7 +156,7 @@ def test_provider_bridge_architecture_mapping(self, mock_pretrained_llama, llama
         assert result.num_attention_heads == llama_config.num_attention_heads
         assert result.num_query_groups == llama_config.num_key_value_heads
         assert result.seq_length == llama_config.max_position_embeddings
-        assert result.rotary_base == llama_config.rope_parameters["rope_theta"]
+        assert result.rotary_base == rope_theta_from_hf(llama_config)
         assert result.vocab_size == llama_config.vocab_size
         assert result.layernorm_epsilon == llama_config.rms_norm_eps
         assert result.init_method_std == llama_config.initializer_range
@@ -225,7 +226,7 @@ def test_provider_bridge_rope_scaling_params(self, mock_pretrained_llama):
         assert result.rope_scaling is True
         assert result.rope_scaling_factor == 32.0
         # Check position embedding
-        assert result.rotary_base == mock_pretrained_llama.config.rope_parameters["rope_theta"]
+        assert result.rotary_base == rope_theta_from_hf(mock_pretrained_llama.config)
 
     def test_provider_bridge_embedding_sharing(self, llama_config):
         """Test embedding sharing configuration."""
@@ -508,7 +509,7 @@ def test_roundtrip_hf_to_megatron_to_hf(self):
         assert result_hf_config["num_key_value_heads"] == hf_config_dict["num_key_value_heads"]
         assert result_hf_config["vocab_size"] == hf_config_dict["vocab_size"]
         assert result_hf_config["max_position_embeddings"] == hf_config_dict["max_position_embeddings"]
-        assert result_hf_config["rope_theta"] == hf_config_dict["rope_parameters"]["rope_theta"]
+        assert result_hf_config["rope_theta"] == rope_theta_from_hf(config)
         assert result_hf_config["rms_norm_eps"] == hf_config_dict["rms_norm_eps"]
         assert result_hf_config["tie_word_embeddings"] == hf_config_dict["tie_word_embeddings"]
         # Check new mappings are preserved

tests/unit_tests/models/mistral/test_mistral_model_bridge.py

@@ -22,6 +22,7 @@
 
 from megatron.bridge.models import AutoBridge
 from megatron.bridge.models.conversion.model_bridge import MegatronModelBridge
+from megatron.bridge.models.conversion.transformers_compat import rope_theta_from_hf
 from megatron.bridge.models.hf_pretrained.causal_lm import PreTrainedCausalLM
 from megatron.bridge.models.mistral.mistral_bridge import MistralBridge
 from megatron.bridge.models.mistral.mistral_provider import MistralModelProvider
@@ -101,7 +102,7 @@ def test_provider_bridge_basic(self, mock_pretrained_mistral, mistral_config):
         assert result.hidden_size == mistral_config.hidden_size
         assert result.num_attention_heads == mistral_config.num_attention_heads
         assert result.seq_length == mistral_config.max_position_embeddings
-        assert result.rotary_base == mistral_config.rope_parameters["rope_theta"]
+        assert result.rotary_base == rope_theta_from_hf(mistral_config)
 
     def test_provider_bridge_vocabulary(self, mock_pretrained_mistral, mistral_config):
         """Test vocabulary size mapping."""
@@ -149,7 +150,7 @@ def test_provider_bridge_position_embedding(self, mock_pretrained_mistral, mistr
         result = bridge.provider_bridge(mock_pretrained_mistral)
 
         # Check position embedding
-        assert result.rotary_base == mistral_config.rope_parameters["rope_theta"]
+        assert result.rotary_base == rope_theta_from_hf(mistral_config)
 
     def test_provider_bridge_mistral_specific_features(self, mock_pretrained_mistral):
         """Test Mistral-specific features."""

tests/unit_tests/models/nemotron_vl/test_nemotron_vl_bridge.py

@@ -27,7 +27,10 @@
 
 @pytest.fixture
 def mock_llm_config():
-    cfg = Mock()
+    # Use spec=[] so hasattr() only returns True for explicitly-set attributes,
+    # matching real HF config behaviour (Nemotron config has no MLA fields
+    # like q_lora_rank, so they must not appear in the provider kwargs).
+    cfg = Mock(spec=[])
     cfg.num_hidden_layers = 28
     cfg.hidden_size = 5120
     cfg.intermediate_size = 20480
@@ -38,14 +41,8 @@ def mock_llm_config():
     cfg.vocab_size = 262144
     cfg.max_position_embeddings = 131072
     cfg.hidden_act = "relu2"
-    # Set MLA-specific fields to None (these are auto-mapped in CONFIG_MAPPING)
-    cfg.q_lora_rank = None
-    cfg.kv_lora_rank = None
-    cfg.qk_nope_head_dim = None
-    cfg.qk_rope_head_dim = None
-    cfg.v_head_dim = None
-    cfg.num_nextn_predict_layers = None
     cfg.rope_scaling = None
+    cfg.torch_dtype = "bfloat16"
     return cfg
 
 

tests/unit_tests/models/qwen/test_qwen3_bridge.py

@@ -22,6 +22,7 @@
 
 from megatron.bridge.models import AutoBridge
 from megatron.bridge.models.conversion.model_bridge import MegatronModelBridge
+from megatron.bridge.models.conversion.transformers_compat import rope_theta_from_hf
 from megatron.bridge.models.gpt_provider import GPTModelProvider
 from megatron.bridge.models.hf_pretrained.causal_lm import PreTrainedCausalLM
 from megatron.bridge.models.qwen.qwen3_bridge import Qwen3Bridge
@@ -100,7 +101,7 @@ def test_provider_bridge_basic(self, mock_pretrained_qwen3, qwen3_config):
         assert result.hidden_size == qwen3_config.hidden_size
         assert result.num_attention_heads == qwen3_config.num_attention_heads
         assert result.seq_length == qwen3_config.max_position_embeddings
-        assert result.rotary_base == qwen3_config.rope_parameters["rope_theta"]
+        assert result.rotary_base == rope_theta_from_hf(qwen3_config)
 
     def test_provider_bridge_vocabulary(self, mock_pretrained_qwen3, qwen3_config):
         """Test vocabulary size mapping."""
@@ -148,7 +149,7 @@ def test_provider_bridge_position_embedding(self, mock_pretrained_qwen3, qwen3_c
         result = bridge.provider_bridge(mock_pretrained_qwen3)
 
         # Check position embedding
-        assert result.rotary_base == qwen3_config.rope_parameters["rope_theta"]
+        assert result.rotary_base == rope_theta_from_hf(qwen3_config)
 
     def test_provider_bridge_qwen3_specific_features(self, mock_pretrained_qwen3):
         """Test Qwen3-specific features."""

tests/unit_tests/models/qwen_vl/test_qwen25_vl_bridge.py

@@ -27,7 +27,7 @@
 @pytest.fixture
 def mock_text_config():
     """Create a mock text config for Qwen2.5-VL."""
-    text_config = Mock()
+    text_config = Mock(spec=[])
     text_config.num_hidden_layers = 32
     text_config.hidden_size = 4096
     text_config.intermediate_size = 11008
@@ -40,15 +40,10 @@ def mock_text_config():
     text_config.rope_theta = 1000000.0
     text_config.tie_word_embeddings = False
     text_config.hidden_act = "silu"
-    text_config.q_lora_rank = None
-    text_config.kv_lora_rank = None
-    text_config.qk_nope_head_dim = None
-    text_config.qk_rope_head_dim = None
-    text_config.v_head_dim = None
-    text_config.num_nextn_predict_layers = None
     text_config.rope_scaling = None
     text_config.bos_token_id = 151643
     text_config.eos_token_id = 151645
+    text_config.torch_dtype = "bfloat16"
     return text_config
 
 
@@ -318,10 +313,10 @@ class TestQwen25VLBridgeEdgeCases:
     def test_provider_bridge_with_minimal_config(self, qwen25_vl_bridge):
         """Test provider_bridge with minimal HF config."""
         minimal_pretrained = Mock(spec=PreTrainedVLM)
-        minimal_config = Mock()
+        minimal_config = Mock(spec=[])
 
         # Text config with required fields
-        text_config = Mock()
+        text_config = Mock(spec=[])
         text_config.num_hidden_layers = 24
         text_config.hidden_size = 2048
         text_config.intermediate_size = 5504
@@ -333,13 +328,8 @@ def test_provider_bridge_with_minimal_config(self, qwen25_vl_bridge):
         text_config.max_position_embeddings = 4096
         text_config.rope_theta = 1000000.0
         text_config.hidden_act = "silu"
-        text_config.q_lora_rank = None
-        text_config.kv_lora_rank = None
-        text_config.qk_nope_head_dim = None
-        text_config.qk_rope_head_dim = None
-        text_config.v_head_dim = None
-        text_config.num_nextn_predict_layers = None
         text_config.rope_scaling = None
+        text_config.torch_dtype = "bfloat16"
 
         minimal_config.text_config = text_config
         minimal_config.vision_config = Qwen2_5_VLVisionConfig()