Delete configuration_deepseek.py

configuration_deepseek.py

@@ -1,206 +0,0 @@
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-class DeepseekConfig(PretrainedConfig):
- r"""
- This is the configuration class to store the configuration of a [`DeepseekModel`]. 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 [`DeepseekModel`]
- 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 DeepseekModel, DeepseekConfig
-
- >>> # Initializing a Deepseek style configuration
- >>> configuration = DeepseekConfig()
-
- >>> # Accessing the model configuration
- >>> configuration = model.config
- ```"""
-
- model_type = "deepseek"
- 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,
- )