84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers.configuration_utils import PretrainedConfig
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class Olmo3Config(PretrainedConfig):
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model_type = "olmo3"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=50304,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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use_cache=True,
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pad_token_id=1,
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bos_token_id=None,
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eos_token_id=50279,
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tie_word_embeddings=False,
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rope_parameters=None,
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attention_bias=False,
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attention_dropout=0.0,
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rms_norm_eps=1e-5,
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sliding_window=4096,
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layer_types=None,
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**kwargs,
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):
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# This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
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# in vLLM.
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if "architectures" not in kwargs:
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kwargs["architectures"] = ["Olmo2ForCausalLM"]
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elif "Olmo3ForCausalLM" in kwargs["architectures"]:
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kwargs["architectures"].remove("Olmo3ForCausalLM")
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kwargs["architectures"].append("Olmo2ForCausalLM")
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
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rope_scaling = kwargs.pop("rope_scaling", None)
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rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
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rope_theta = kwargs.pop("rope_theta", 10000.0)
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if "rope_theta" not in rope_parameters:
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rope_parameters["rope_theta"] = rope_theta
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self.rope_parameters = rope_parameters
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.rms_norm_eps = rms_norm_eps
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self.sliding_window = sliding_window
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self.layer_types = layer_types
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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