# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Nemotron-Labs Diffusion model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.modeling_rope_utils import rope_config_validation from transformers.utils import logging logger = logging.get_logger(__name__) class NemotronLabsDiffusionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`NemotronLabsDiffusionModel`] for diffusion language models. It is used to instantiate a NemotronLabsDiffusionModel 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 131072): Vocabulary size of the Ministral model. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 34): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer. num_key_value_heads (`int`, *optional*, defaults to 8): Number of key_value heads for Grouped Query Attention. head_dim (`int`, *optional*, defaults to 128): The attention head dimension. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function. max_position_embeddings (`int`, *optional*, defaults to 262144): The maximum sequence length. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer. rms_norm_eps (`float`, *optional*, defaults to 1e-05): 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. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. rope_parameters (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Default uses YaRN scaling with factor=16, original_max_position_embeddings=16384. attention_bias (`bool`, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers. sliding_window (`int`, *optional*, defaults to None): Sliding window attention size. mask_token_id (`int`, *optional*, defaults to -1): Token ID for masking in diffusion. dlm_paradigm (`str`, *optional*, defaults to 'bidirectional'): Paradigm for diffusion ('bidirectional', 'autoregressive', 'block_diff'). block_size (`int`, *optional*, defaults to 32): Block size for block diffusion paradigms. dlm_loss_weight (`float`, *optional*): Weight for diffusion LM loss. ar_loss_weight (`float`, *optional*, defaults to 1.0): Weight for autoregressive loss in block_diff paradigm. Use 10000 to only use AR loss. dp_varying_mask_ratio (`bool`, *optional*, defaults to False): Whether to use varying mask ratio for each DP rank during sampling. """ model_type = "nemotron_labs_diffusion" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `Ministral` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=131072, hidden_size=4096, intermediate_size=14336, num_hidden_layers=34, num_attention_heads=32, num_key_value_heads=8, head_dim=128, hidden_act="silu", max_position_embeddings=262144, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=1000000.0, rope_parameters=None, attention_bias=False, attention_dropout=0.0, mlp_bias=False, sliding_window=None, attn_implementation="sdpa", mask_token_id=-1, dlm_paradigm='bidirectional', block_size=32, dlm_loss_weight=None, ar_loss_weight=1.0, dp_varying_mask_ratio=False, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_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 # 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.head_dim = head_dim self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_parameters = rope_parameters # `rope_theta` is read at the top level by transformers v4.55's yarn impl; mirror from rope_parameters when present. self.rope_theta = (rope_parameters or {}).get("rope_theta", rope_theta) # v4.55 reads rope params from `rope_scaling`; in v5.0 `rope_scaling` is a property alias for rope_parameters. self.rope_scaling = rope_parameters self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.mlp_bias = mlp_bias self.sliding_window = sliding_window rope_config_validation(self) self.attn_implementation = attn_implementation self.mask_token_id = mask_token_id self.dlm_paradigm = dlm_paradigm self.block_size = block_size self.dlm_loss_weight = dlm_loss_weight self.ar_loss_weight = ar_loss_weight self.dp_varying_mask_ratio = dp_varying_mask_ratio 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, ) __all__ = ["NemotronLabsDiffusionConfig"]