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# 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"]
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