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