Delete configuration_deepseek.py

configuration_deepseek.py

@@ -1,199 +0,0 @@
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-class DeepseekV3Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. 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-V3.
-
-    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 129280):
-            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`DeepseekV3Model`]
-        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_nextn_predict_layers (`int`, *optional*, defaults to 1):
-            Number of nextn predict layers in the DeepSeekV3 Model.
-        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.
-        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 DeepseekV3Model, DeepseekV3Config
-
-    >>> # Initializing a Deepseek-V3 style configuration
-    >>> configuration = DeepseekV3Config()
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "deepseek_v3"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=129280,
-        hidden_size=7168,
-        intermediate_size=18432,
-        moe_intermediate_size = 2048,
-        num_hidden_layers=61,
-        num_nextn_predict_layers=1,
-        num_attention_heads=128,
-        num_key_value_heads=128,
-        n_shared_experts = 1,
-        n_routed_experts = 256,
-        ep_size = 1,
-        routed_scaling_factor = 2.5,
-        kv_lora_rank = 512,
-        q_lora_rank = 1536,
-        qk_rope_head_dim = 64,
-        v_head_dim = 128,
-        qk_nope_head_dim = 128,
-        topk_method = 'noaux_tc',
-        n_group = 8,
-        topk_group = 4,
-        num_experts_per_tok = 8,
-        moe_layer_freq = 1,
-        first_k_dense_replace = 3,
-        norm_topk_prob = True,
-        scoring_func = 'sigmoid',
-        hidden_act="silu",
-        max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=None,
-        bos_token_id=0,
-        eos_token_id=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_nextn_predict_layers = num_nextn_predict_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
-        # 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.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,
-        )