Feature/support olmo3

paddleformers/cli/utils/llm_utils.py

@@ -89,6 +89,16 @@ def get_lora_target_modules(model):
             ".*up_proj.*",
             ".*gate_up_fused_proj.*",
         ]
+    elif model.config.model_type in ["olmo2", "olmo3"]:
+        target_modules = [
+            ".*q_proj.*",
+            ".*k_proj.*",
+            ".*v_proj.*",
+            ".*o_proj.*",
+            ".*gate_proj.*",
+            ".*up_proj.*",
+            ".*down_proj.*",
+        ]
     elif model.config.model_type == "opt":
         target_modules = [
             ".*project_in.*",

paddleformers/transformers/__init__.py

@@ -348,6 +348,21 @@
     ],
     "glm_ocr.processor": ["Glm46VProcessor"],
     "glm_ocr.image_processor": ["Glm46VImageProcessor"],
+    "olmo2.configuration": ["Olmo2Config"],
+    "olmo2.modeling": [
+        "Olmo2Model",
+        "Olmo2PretrainedModel",
+        "Olmo2ForCausalLM",
+        "Olmo2ForCausalLMPipe",
+    ],
+    "olmo3.tokenizer": ["Olmo3Tokenizer"],
+    "olmo3.configuration": ["Olmo3Config"],
+    "olmo3.modeling": [
+        "Olmo3Model",
+        "Olmo3PretrainedModel",
+        "Olmo3ForCausalLM",
+        "Olmo3ForCausalLMPipe",
+    ],
 }
 
 if TYPE_CHECKING:

paddleformers/transformers/auto/configuration.py

@@ -64,6 +64,8 @@
         ("glm_ocr", "GlmOcrConfig"),
         ("qwen3_5", "Qwen3_5Config"),
         ("qwen3_5_moe", "Qwen3_5MoEConfig"),
+        ("olmo2", "Olmo2Config"),
+        ("olmo3", "Olmo3Config"),
     ]
 )
 

paddleformers/transformers/auto/modeling.py

@@ -81,6 +81,8 @@
         ("Gemma3", "gemma3_text"),
         ("Glm4vMoe", "glm4v_moe"),
         ("GlmOcr", "glm_ocr"),
+        ("Olmo2", "olmo2"),
+        ("Olmo3", "olmo3"),
     ]
 )
 

paddleformers/transformers/olmo2/__init__.py

@@ -0,0 +1,39 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+from typing import TYPE_CHECKING
+
+from ...utils.lazy_import import _LazyModule
+
+import_structure = {
+    "configuration": ["Olmo2Config"],
+    "modeling": [
+        "Olmo2PretrainedModel",
+        "Olmo2Model",
+        "Olmo2ForCausalLM",
+        "Olmo2ForCausalLMPipe",
+    ],
+}
+
+if TYPE_CHECKING:
+    from .configuration import *
+    from .modeling import *
+else:
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        import_structure,
+        module_spec=__spec__,
+    )

paddleformers/transformers/olmo2/configuration.py

@@ -0,0 +1,149 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2025 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ..configuration_utils import PretrainedConfig
+from ..modeling_rope_utils import rope_config_validation, standardize_rope_params
+
+
+class Olmo2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Olmo2Model`].
+    It is used to instantiate an OLMo2 model according to the specified arguments,
+    defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to
+    control the model outputs. Read the documentation from [`PretrainedConfig`]
+    for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50304):
+            Vocabulary size of the Olmo2 model.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer.
+        num_key_value_heads (`int`, *optional*):
+            Number of key_value heads for Grouped Query Attention.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return last key/values attentions.
+        pad_token_id (`int`, *optional*, defaults to 1):
+            Padding token id.
+        bos_token_id (`int`, *optional*):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 50279):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the attention projection layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+    """
+
+    model_type = "olmo2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50304,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        use_cache=True,
+        pad_token_id=1,
+        bos_token_id=None,
+        eos_token_id=50279,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        rms_norm_eps=1e-5,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.rms_norm_eps = rms_norm_eps
+
+        self.diff_align_float32 = kwargs.pop("diff_align_float32", False)
+
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        self.rope_parameters = self.rope_scaling
+        standardize_rope_params(self, rope_theta=self.rope_theta)
+        rope_config_validation(self)
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """Validate the `rope_scaling` configuration."""
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                f"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")