srt/configs/olmo3.py

# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. 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
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"""Olmo3 model configuration"""

import enum

from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging

logger = logging.get_logger(__name__)


class Olmo3LayerType(enum.Enum):
    full_attention = "full_attention"
    sliding_attention = "sliding_attention"


class Olmo3Config(PretrainedConfig):

    model_type = "olmo3"
    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,
        sliding_window=4096,
        layer_types=None,
        **kwargs,
    ):
        # This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
        # in sglang.
        if "architectures" not in kwargs:
            kwargs["architectures"] = ["Olmo2ForCausalLM"]
        elif "Olmo3ForCausalLM" in kwargs["architectures"]:
            kwargs["architectures"].remove("Olmo3ForCausalLM")
            kwargs["architectures"].append("Olmo2ForCausalLM")

        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,
        )
        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

        # for backward compatibility
        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.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        rope_config_validation(self)
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout

        self.rms_norm_eps = rms_norm_eps

        self.sliding_window = sliding_window
        self.layer_types = layer_types
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
                for i in range(self.num_hidden_layers)
            ]