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""" Nemotron-Flash model configuration""" |
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import math |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class NemotronFlashConfig(PretrainedConfig): |
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model_type = "nemotron_flash" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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vocab_size=65536, |
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tie_word_embeddings=False, |
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hidden_size=4096, |
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intermediate_size=14336, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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num_key_value_heads=8, |
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hidden_act="silu", |
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initializer_range=0.02, |
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rms_norm_eps=1e-6, |
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use_cache=True, |
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calc_logits_for_entire_prompt=False, |
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output_router_logits=False, |
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router_aux_loss_coef=0.001, |
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pad_token_id=0, |
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bos_token_id=1, |
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eos_token_id=2, |
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sliding_window=None, |
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max_position_embeddings=262144, |
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orig_max_position_embeddings=None, |
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attention_dropout=0.0, |
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num_experts_per_tok=2, |
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num_experts=16, |
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use_mamba_kernels=True, |
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mamba_d_state=16, |
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mamba_d_conv=4, |
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mamba_expand=2, |
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mamba_dt_rank="auto", |
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mamba_conv_bias=True, |
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mamba_proj_bias=False, |
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mamba_inner_layernorms=True, |
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hybrid_decoder_layer='mamba', |
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global_attn_idx=None, |
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attn_implementation_new='flash_attention_2', |
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mamba2_headdim=64, |
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rope_type=None, |
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layer_types=None, |
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ffn_expand_ratio=None, |
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d_conv=4, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.tie_word_embeddings = tie_word_embeddings |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.sliding_window = sliding_window |
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self.max_position_embeddings = max_position_embeddings |
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self.orig_max_position_embeddings = orig_max_position_embeddings |
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self.attention_dropout = attention_dropout |
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if num_key_value_heads is None: |
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num_key_value_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.hidden_act = hidden_act |
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self.initializer_range = initializer_range |
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self.rms_norm_eps = rms_norm_eps |
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self.use_cache = use_cache |
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self.calc_logits_for_entire_prompt = calc_logits_for_entire_prompt |
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self.output_router_logits = output_router_logits |
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self.router_aux_loss_coef = router_aux_loss_coef |
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self.num_experts_per_tok = num_experts_per_tok |
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self.num_experts = num_experts |
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self.use_mamba_kernels = use_mamba_kernels |
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self.mamba_d_state = mamba_d_state |
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self.mamba_d_conv = mamba_d_conv |
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self.mamba_expand = mamba_expand |
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self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank |
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self.mamba_conv_bias = mamba_conv_bias |
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self.mamba_proj_bias = mamba_proj_bias |
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self.mamba_inner_layernorms = mamba_inner_layernorms |
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self.kq_norm = kwargs.pop("kq_norm", None) |
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self.rope = kwargs.pop("rope", False) |
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self.rope_theta = kwargs.pop("rope_theta", 10000.0) |
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self.num_memory_tokens = kwargs.pop("num_memory_tokens", 0) |
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self.attn_hidden_size = kwargs.pop("attn_hidden_size", -1) |
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self.kq_head_dim = kwargs.pop("kq_head_dim", -1) |
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self.v_head_dim = kwargs.pop("v_head_dim", -1) |
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self.new_seq_length = 2048 |
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self.hybrid_decoder_layer = hybrid_decoder_layer |
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self.global_attn_idx = global_attn_idx |
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self.attn_implementation_new = attn_implementation_new |
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self.mamba2_headdim = mamba2_headdim |
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self.rope_type = rope_type |
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self.layer_types = layer_types |
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self.ffn_expand_ratio = ffn_expand_ratio |
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self.d_conv = d_conv |
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self.mlp_hidden_act = kwargs.pop("mlp_hidden_act", "silu") |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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