RoPE: relaxed rope validation (#32182)

* relaxed rope check

* lets also accept rope_type=None, defaulting to the original implementation

* type and rope_type can coexist

src/transformers/modeling_rope_utils.py

@@ -354,41 +354,46 @@ def _compute_llama3_parameters(
 
 def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
     """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
+    # BC: "rope_type" was originally "type" -- let's gracefully handle it
+    if "rope_type" not in received_keys and "type" in received_keys:
+        received_keys -= {"type"}
+        received_keys.add("rope_type")
+
     missing_keys = required_keys - received_keys
     if missing_keys:
         raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
 
     if optional_keys is not None:
         unused_keys = received_keys - required_keys - optional_keys
     else:
-        unused_keys = received_keys - received_keys
+        unused_keys = received_keys - required_keys
     if unused_keys:
-        raise KeyError(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
+        logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
 
 
 def _validate_default_rope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys)
 
 
 def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
-        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
 
 def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
     optional_keys = {"original_max_position_embeddings"}
@@ -397,43 +402,43 @@ def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
-        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
 
 def _validate_yarn_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
-        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
     attention_factor = rope_scaling.get("attention_factor")
     if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
-        raise ValueError(
+        logger.warning(
             f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
         )
     beta_fast = rope_scaling.get("beta_fast")
     if beta_fast is not None and not isinstance(beta_fast, float):
-        raise ValueError(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
+        logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
     beta_slow = rope_scaling.get("beta_slow")
     if beta_slow is not None and not isinstance(beta_slow, float):
-        raise ValueError(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
+        logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
 
     if (beta_fast or 32) < (beta_slow or 1):
-        raise ValueError(
+        logger.warning(
             f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
             f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
         )
 
 
 def _validate_longrope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "short_factor", "long_factor"}
     # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
     optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
@@ -445,15 +450,15 @@ def _validate_longrope_parameters(config: PretrainedConfig):
 
     short_factor = rope_scaling.get("short_factor")
     if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
-        raise ValueError(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
+        logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
     if not len(short_factor) == dim // 2:
-        raise ValueError(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
+        logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
 
     long_factor = rope_scaling.get("long_factor")
     if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
-        raise ValueError(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
+        logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
     if not len(long_factor) == dim // 2:
-        raise ValueError(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
+        logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
 
     # Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
     # `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
@@ -468,48 +473,48 @@ def _validate_longrope_parameters(config: PretrainedConfig):
     else:
         factor = rope_scaling.get("factor")
         if factor is None:
-            raise ValueError("Missing required keys in `rope_scaling`: 'factor'")
+            logger.warning("Missing required keys in `rope_scaling`: 'factor'")
         elif not isinstance(factor, float) or factor < 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+            logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
         attention_factor = rope_scaling.get("attention_factor")
         if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
-            raise ValueError(
+            logger.warning(
                 f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
             )
 
 
 def _validate_llama3_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
-        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
     low_freq_factor = rope_scaling["low_freq_factor"]
     high_freq_factor = rope_scaling["high_freq_factor"]
     if low_freq_factor is None or not isinstance(low_freq_factor, float):
-        raise ValueError(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+        logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
     if high_freq_factor is None or not isinstance(high_freq_factor, float):
-        raise ValueError(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+        logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
     if high_freq_factor < low_freq_factor:
-        raise ValueError(
+        logger.warning(
             "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
             f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
         )
 
     original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
     if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
-        raise ValueError(
+        logger.warning(
             "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
             f"{original_max_position_embeddings}"
         )
     if original_max_position_embeddings >= config.max_position_embeddings:
-        raise ValueError(
+        logger.warning(
             "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
             f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
         )
@@ -534,17 +539,12 @@ def rope_config_validation(config: PretrainedConfig):
     if rope_scaling is None:
         return
 
-    possible_rope_types = set(ROPE_INIT_FUNCTIONS.keys())
-    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
-    if rope_type is None:
-        raise ValueError(
-            f"rope_scaling must contain a non-None 'rope_type' field. Possible options are {possible_rope_types}"
-        )
-
+    # BC: "rope_type" was originally "type"
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
     validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
     if validation_fn is not None:
         validation_fn(config)
     else:
-        raise ValueError(
+        logger.warning(
             f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
         )

src/transformers/models/llama/configuration_llama.py

@@ -189,6 +189,9 @@ def __init__(
         self.mlp_bias = mlp_bias
 
         # 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:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
         rope_config_validation(self)
 
         super().__init__(

tests/models/llama/test_modeling_llama.py

@@ -526,6 +526,60 @@ def test_rope_class_retrocompatibility(self):
         torch.testing.assert_close(old_cos_long, new_cos_long)
         torch.testing.assert_close(old_sin_long, new_sin_long)
 
+    def test_model_loading_old_rope_configs(self):
+        def _reinitialize_config(base_config, new_kwargs):
+            # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation
+            # steps.
+            base_config_dict = base_config.to_dict()
+            new_config = LlamaConfig.from_dict(config_dict={**base_config_dict, **new_kwargs})
+            return new_config
+
+        # from untouched config -> ✅
+        base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        original_model = LlamaForCausalLM(base_config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with the expected rope configuration -> ✅
+        config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0}})
+        original_model = LlamaForCausalLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with the old rope configuration ('type' instead of 'rope_type')  -> ✅ we gracefully handle BC
+        config = _reinitialize_config(base_config, {"rope_scaling": {"type": "linear", "factor": 10.0}})
+        original_model = LlamaForCausalLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with both 'type' and 'rope_type'  -> ✅ they can coexist (and both are present in the config)
+        config = _reinitialize_config(
+            base_config, {"rope_scaling": {"type": "linear", "rope_type": "linear", "factor": 10.0}}
+        )
+        self.assertTrue(config.rope_scaling["type"] == "linear")
+        self.assertTrue(config.rope_scaling["rope_type"] == "linear")
+        original_model = LlamaForCausalLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning
+        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
+            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": -999.0}})
+            original_model = LlamaForCausalLM(config).to(torch_device)
+            original_model(**model_inputs)
+            self.assertEqual(len(logs.output), 1)
+            self.assertIn("factor field", logs.output[0])
+
+        # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning
+        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
+            config = _reinitialize_config(
+                base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}}
+            )
+            original_model = LlamaForCausalLM(config).to(torch_device)
+            original_model(**model_inputs)
+            self.assertEqual(len(logs.output), 1)
+            self.assertIn("Unrecognized keys", logs.output[0])
+
+        # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception
+        with self.assertRaises(KeyError):
+            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}})  # missing "factor"
+
     @require_flash_attn
     @require_torch_gpu
     @require_bitsandbytes