lib.linux-x86_64-cpython-310/tensorrt_llm/models/gpt/config.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional, Union

from ...layers import MoeConfig
from ..modeling_utils import PretrainedConfig


class GPTConfig(PretrainedConfig):

    def __init__(self,
                 *,
                 bias: bool = True,
                 q_scaling: float = 1.0,
                 embedding_scale: Optional[float] = None,
                 apply_query_key_layer_scaling: bool = False,
                 rotary_pct: float = 1.0,
                 rotary_base: float = 10000.0,
                 rotary_scaling: Optional[dict] = None,
                 moe: Optional[Union[MoeConfig, dict]] = None,
                 **kwargs):
        self.bias = bias
        self.q_scaling = q_scaling
        self.embedding_scale = embedding_scale
        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
        self.rotary_pct = rotary_pct
        self.rotary_base = rotary_base
        self.rotary_scaling = rotary_scaling
        if moe is None:
            # Legacy MOE config fields
            moe = MoeConfig(
                num_experts=kwargs.pop('moe_num_experts', 0),
                top_k=kwargs.pop('moe_top_k', 0),
                normalization_mode=kwargs.pop(
                    'moe_normalization_mode',
                    MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE))
        elif isinstance(moe, dict):
            moe = MoeConfig.from_dict(moe)
        assert isinstance(moe, MoeConfig)
        self.moe = moe.validate()

        super().__init__(**kwargs)

    def to_dict(self):
        output = super().to_dict()
        # Serialize the fields added in GPTConfig
        output['bias'] = self.bias
        output['q_scaling'] = self.q_scaling
        output['embedding_scale'] = self.embedding_scale
        output[
            'apply_query_key_layer_scaling'] = self.apply_query_key_layer_scaling
        output['rotary_pct'] = self.rotary_pct
        output['rotary_base'] = self.rotary_base
        output['rotary_scaling'] = self.rotary_scaling
        output['moe'] = self.moe.to_dict()
        return output