Delete configuration_deepseek_fixed.py

configuration_deepseek_fixed.py

@@ -1,206 +0,0 @@
-from typing import Optional
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-DEEPSEEK_FIXES_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-class DeepseekFixedConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a DeepseekWithConcentrationekModel`]. 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 DeepseekWithConcentration-7B.
-
-    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 [`DeepseekWithConcentrationModel`]
-        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.
-        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 DeepseekWithConcentrationModel, DeepseekWithConcentrationConfig
-
-    >>> # Initializing a DeepseekWithConcentration DeepseekWithConcentration-7b style configuration
-    >>> configuration = DeepseekWithConcentrationConfig()
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "deepseek_with_concentration"
-    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,
-            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,
-            moe_implementation="eager",
-            **kwargs,
-    ):
-        assert moe_implementation in ('eager', 'megablocks'), "Invalid moe_implementation value. Choose from 'eager' or 'megablocks'."
-        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.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._rope_scaling_validation()
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.moe_implementation = moe_implementation
-
-        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,
-        )
-
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-        "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
-        f"got {self.rope_scaling}"
-        )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
-            raise ValueError(
-        f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
-        )
-        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")