| |
| """Dragon model configuration""" |
| |
| |
|
|
| import re |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
| logger = logging.get_logger(__name__) |
|
|
| |
| class DragonConfig(PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`DragonModel`]. It is used to instantiate a |
| Dragon model according to the specified arguments, defining the model architecture. |
| 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 151936): |
| Vocabulary size of the Dragon model. Defines the number of different tokens that can be represented by the |
| `inputs_ids` passed when calling [`DragonModel`] |
| tie_word_embeddings (`bool`, *optional*, defaults to `False`): |
| Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the |
| model has a output word embedding layer. |
| hidden_size (`int`, *optional*, defaults to 2048): |
| Dimension of the hidden representations. |
| intermediate_size (`int`, *optional*, defaults to 8192): |
| Dimension of the MLP representations. |
| num_attention_heads (`int`, *optional*, defaults to 32): |
| Number of attention heads for each attention layer in the Transformer encoder. |
| num_key_value_heads (`int`, *optional*, defaults to 8): |
| 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. |
| mlp_hidden_act (`str`, *optional*, defaults to "relu2"): |
| The non-linear activation function in the MLP layers. |
| attention_bias (`bool`, *optional*, defaults to `False`): |
| Whether to use bias in attention layers. |
| mlp_bias (`bool`, *optional*, defaults to `False`): |
| Whether to use bias in MLP layers. |
| use_bias (`bool`, *optional*, defaults to `False`): |
| Whether to use bias in the model. |
| initializer_range (`float`, *optional*, defaults to 0.006): |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
| norm_epsilon (`float`, *optional*, defaults to 1e-5): |
| The epsilon used by the layer normalization layers. |
| residual_in_fp32 (`bool`, *optional*, defaults to `False`): |
| Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model. |
| 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`. |
| num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): |
| Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an |
| integer value, only last `num_logits_to_keep` logits will be calculated. |
| pad_token_id (`int`, *optional*, defaults to 0): |
| The id of the padding token. |
| bos_token_id (`int`, *optional*, defaults to 1): |
| The id of the "beginning-of-sequence" token. |
| eos_token_id (`int`, *optional*, defaults to 2): |
| The id of the "end-of-sequence" token. |
| sliding_window_size (`int`, *optional*, defaults to 1024): |
| Sliding window attention window size. |
| max_position_embeddings (`int`, *optional*, defaults to 4096): |
| The maximum sequence length that this model might ever be used with. |
| attention_dropout (`float`, *optional*, defaults to 0.0): |
| The dropout ratio for the attention probabilities. |
| hidden_dropout (`float`, *optional*, defaults to 0.0): |
| The dropout ratio for the hidden states. |
| use_mamba_kernels (`bool`, *optional*, defaults to `True`): |
| Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and |
| `causal-conv1d` are installed, and the mamba modules are running on a CUDA device. |
| mamba_d_conv (`int`, *optional*, defaults to 4): |
| The size of the mamba convolution kernel. |
| mamba_expand (`int`, *optional*, defaults to 2): |
| Expanding factor used to determine the mamba intermediate size. |
| mamba_hidden_act (`str`, *optional*, defaults to "silu"): |
| The non-linear activation function in the Mamba layers. |
| mamba_dt_min (`float`, *optional*, defaults to 0.001): |
| Minimum value for the time step in Mamba. |
| mamba_dt_max (`float`, *optional*, defaults to 0.1): |
| Maximum value for the time step in Mamba. |
| mamba_dt_limit (`tuple`, *optional*, defaults to (0.0, float("inf"))): |
| Limits for the time step in Mamba. |
| mamba_dt_init_floor (`float`, *optional*, defaults to 1e-4): |
| Floor value for time step initialization in Mamba. |
| """ |
|
|
| model_type = "dragon" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=151936, |
| tie_word_embeddings=False, |
| max_position_embeddings=8192, |
| use_uscaling=True, |
| hidden_size=2048, |
| intermediate_size=8192, |
| expand_factor=2, |
| layers_config=4*"lrdlr", |
| num_attention_heads=32, |
| num_key_value_heads=8, |
| mlp_hidden_act="relu2", |
| attention_bias=False, |
| mlp_bias=False, |
| use_bias=False, |
| initializer_range=0.006, |
| softcap_local_attn=0.0, |
| softcap_global_attn=150.0, |
| norm_epsilon=1e-6, |
| residual_in_fp32=False, |
| use_cache=True, |
| num_logits_to_keep=1, |
| pad_token_id=0, |
| bos_token_id=1, |
| eos_token_id=2, |
| sliding_window_size=1024, |
| slw_wsize=-1, |
| rope_theta_local=163., |
| uscaling_tau=0.2, |
| attention_dropout=0., |
| hidden_dropout=0., |
| gdn_d_conv=4, |
| gdn_dt_min=0.001, |
| gdn_dt_max=0.1, |
| gdn_dt_init_floor=1e-4, |
| gdn_A_init_range=(1, 16), |
| old_lns=False, |
| **kwargs, |
| ): |
|
|
| self.rope_theta = rope_theta_local |
| self.qk_norm = True |
| self.softcap_local_attn=softcap_local_attn |
| self.softcap_global_attn=softcap_global_attn |
| self.use_uscaling = use_uscaling |
| self.uscaling_tau = uscaling_tau |
| self.scalable_softmax = True |
|
|
| self.vocab_size = vocab_size |
| self.tie_word_embeddings = tie_word_embeddings |
| self.hidden_size = hidden_size |
| self.intermediate_size = intermediate_size |
| self.expand_factor = expand_factor |
| self.layers_config = layers_config |
| self.num_hidden_layers = len(layers_config) |
| self.num_attention_heads = num_attention_heads |
| self.sliding_window_size = sliding_window_size |
| self.slw_wsize = slw_wsize |
| self.attention_dropout = attention_dropout |
| self.hidden_dropout = hidden_dropout |
| self.max_position_embeddings = max_position_embeddings |
|
|
| if num_key_value_heads is None: |
| num_key_value_heads = num_attention_heads |
|
|
| self.num_key_value_heads = num_key_value_heads |
| self.mlp_hidden_act = mlp_hidden_act |
| self.attention_bias = attention_bias |
| self.mlp_bias = mlp_bias |
| self.use_bias = use_bias |
| self.initializer_range = initializer_range |
| self.norm_epsilon = norm_epsilon |
| self.residual_in_fp32 = residual_in_fp32 |
|
|
| self.use_cache = use_cache |
| self.num_logits_to_keep = num_logits_to_keep |
|
|
| self.conv_kernel = gdn_d_conv |
| self.time_step_min = gdn_dt_min |
| self.time_step_max = gdn_dt_max |
| self.time_step_floor = gdn_dt_init_floor |
| self.A_init_range = gdn_A_init_range |
|
|
| self.old_lns = old_lns |
|
|
| assert self.hidden_size % self.num_attention_heads == 0 |
| assert self.num_attention_heads % self.num_key_value_heads == 0 |
| assert self.num_attention_heads % 2 == 0, "Number of attention heads must be even for differential attention." |
| assert self.num_key_value_heads % 2 == 0, "Number of kv heads must be even for differential attention." |
|
|
| 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, |
| ) |
| |
| self.auto_map = dict(getattr(self, "auto_map", {})) |
| self.auto_map.setdefault("AutoConfig", "configuration_dragon.DragonConfig") |
| self.auto_map.setdefault("AutoModel", "modeling_dragon.DragonModel") |
| self.auto_map.setdefault("AutoModelForCausalLM", "modeling_dragon.DragonForCausalLM") |
|
|
| DragonConfig.register_for_auto_class("AutoConfig") |
| __all__ = ["DragonConfig"] |
| |
