deepseek_ai_deepseek_coder_v2_lite_instruct-nl2sqlpp-16bit-v4.0-cw-4K

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

logger = logging.get_logger(name)

DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class DeepseekV2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [DeepseekV2Model]. It is used to instantiate an DeepSeek model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DeepSeek-V2.

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 102400):
        Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
        `inputs_ids` passed when calling [`DeepseekV2Model`]
    hidden_size (`int`, *optional*, defaults to 4096):
        Dimension of the hidden representations.
    intermediate_size (`int`, *optional*, defaults to 11008):
        Dimension of the MLP representations.
    moe_intermediate_size (`int`, *optional*, defaults to 1407):
        Dimension of the MoE 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 in the Transformer decoder.
    n_shared_experts (`int`, *optional*, defaults to None):
        Number of shared experts, None means dense model.
    n_routed_experts (`int`, *optional*, defaults to None):
        Number of routed experts, None means dense model.
    routed_scaling_factor (`float`, *optional*, defaults to 1.0):
        Scaling factor or routed experts.
    topk_method (`str`, *optional*, defaults to `gready`):
        Topk method used in routed gate.
    n_group (`int`, *optional*, defaults to None):
        Number of groups for routed experts.
    topk_group (`int`, *optional*, defaults to None):
        Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
    num_experts_per_tok (`int`, *optional*, defaults to None):
        Number of selected experts, None means dense model.
    moe_layer_freq (`int`, *optional*, defaults to 1):
        The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
    first_k_dense_replace (`int`, *optional*, defaults to 0):
        Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                        \--k dense layers--/
    norm_topk_prob (`bool`, *optional*, defaults to False):
        Whether to normalize the weights of the routed experts.
    scoring_func (`str`, *optional*, defaults to 'softmax'):
        Method of computing expert weights.
    aux_loss_alpha (`float`, *optional*, defaults to 0.001):
        Auxiliary loss weight coefficient.
    seq_aux = (`bool`, *optional*, defaults to True):
        Whether to compute the auxiliary loss for each individual sample.
    num_key_value_heads (`int`, *optional*):
        This is the number of key_value heads that should be used to implement Grouped Query Attention. If
        `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
        `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
        converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
        by meanpooling all the original heads within that group. For more details checkout [this
        paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
        `num_attention_heads`.
    hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
        The non-linear activation function (function or string) in the decoder.
    max_position_embeddings (`int`, *optional*, defaults to 2048):
        The maximum sequence length that this model might ever be used with.
    initializer_range (`float`, *optional*, defaults to 0.02):
        The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
    rms_norm_eps (`float`, *optional*, defaults to 1e-06):
        The epsilon used by the rms normalization layers.
    use_cache (`bool`, *optional*, defaults to `True`):
        Whether or not the model should return the last key/values attentions (not used by all models). Only
        relevant if `config.is_decoder=True`.
    pad_token_id (`int`, *optional*):
        Padding token id.
    bos_token_id (`int`, *optional*, defaults to 1):
        Beginning of stream token id.
    eos_token_id (`int`, *optional*, defaults to 2):
        End of stream token id.
    pretraining_tp (`int`, *optional*, defaults to 1):
        Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
        document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
        necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
        issue](https://github.com/pytorch/pytorch/issues/76232).
    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. Currently supports two scaling
        strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
        `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
        `max_position_embeddings` to the expected new maximum.
    attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
        Whether to use a bias in the query, key, value and output projection layers during self-attention.
    attention_dropout (`float`, *optional*, defaults to 0.0):
        The dropout ratio for the attention probabilities.

```python
>>> from transformers import DeepseekV2Model, DeepseekV2Config

>>> # Initializing a Deepseek-V2 style configuration
>>> configuration = DeepseekV2Config()

>>> # Accessing the model configuration
>>> configuration = model.config
```"""

model_type = "deepseek_v2"
keys_to_ignore_at_inference = ["past_key_values"]

def __init__(
    self,
    vocab_size=102400,
    hidden_size=4096,
    intermediate_size=11008,
    moe_intermediate_size = 1407,
    num_hidden_layers=30,
    num_attention_heads=32,
    num_key_value_heads=32,
    n_shared_experts = None,
    n_routed_experts = None,
    ep_size = 1,
    routed_scaling_factor = 1.0,
    kv_lora_rank = 512,
    q_lora_rank = 1536,
    qk_rope_head_dim = 64,
    v_head_dim = 128,
    qk_nope_head_dim = 128,
    topk_method = 'gready',
    n_group = None,
    topk_group = None,
    num_experts_per_tok = None,
    moe_layer_freq = 1,
    first_k_dense_replace = 0,
    norm_topk_prob = False,
    scoring_func = 'softmax',
    aux_loss_alpha = 0.001,
    seq_aux = True,
    hidden_act="silu",
    max_position_embeddings=2048,
    initializer_range=0.02,
    rms_norm_eps=1e-6,
    use_cache=True,
    pad_token_id=None,
    bos_token_id=100000,
    eos_token_id=100001,
    pretraining_tp=1,
    tie_word_embeddings=False,
    rope_theta=10000.0,
    rope_scaling=None,
    attention_bias=False,
    attention_dropout=0.0,
    **kwargs,
):
    self.vocab_size = vocab_size
    self.max_position_embeddings = max_position_embeddings
    self.hidden_size = hidden_size
    self.intermediate_size = intermediate_size
    self.moe_intermediate_size = moe_intermediate_size
    self.num_hidden_layers = num_hidden_layers
    self.num_attention_heads = num_attention_heads
    self.n_shared_experts = n_shared_experts
    self.n_routed_experts = n_routed_experts
    self.ep_size = ep_size
    self.routed_scaling_factor = routed_scaling_factor
    self.kv_lora_rank = kv_lora_rank
    self.q_lora_rank = q_lora_rank
    self.qk_rope_head_dim = qk_rope_head_dim
    self.v_head_dim = v_head_dim
    self.qk_nope_head_dim = qk_nope_head_dim
    self.topk_method = topk_method
    self.n_group = n_group
    self.topk_group = topk_group
    self.num_experts_per_tok = num_experts_per_tok
    self.moe_layer_freq = moe_layer_freq
    self.first_k_dense_replace = first_k_dense_replace
    self.norm_topk_prob = norm_topk_prob
    self.scoring_func = scoring_func
    self.aux_loss_alpha = aux_loss_alpha
    self.seq_aux = seq_aux
    # 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.rms_norm_eps = rms_norm_eps
    self.pretraining_tp = pretraining_tp
    self.use_cache = use_cache
    self.rope_theta = rope_theta
    self.rope_scaling = rope_scaling
    self.attention_bias = attention_bias
    self.attention_dropout = attention_dropout

    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,
    )