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# coding=utf-8
# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved.
# Copyright (c) 2025, NVIDIA CORPORATION. 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.
"""NemotronH model configuration"""
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class NemotronHConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NemotronHModel`]. It is used to instantiate a
NemotronH model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the NemotronH-v0.1 model.
[todo](todo)
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 NemotronH model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`NemotronHModel`]
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 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 21504):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 52):
Number of hidden layers in the Transformer encoder.
hybrid_override_pattern (`str`, *optional*, defaults to `"M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-"`):
The pattern of the hybrid model. The pattern is a string of characters where each character represents M: Mamba2, *: Attention, -: MLP
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
attention_head_dim (`int`, *optional*, defaults to 128):
Dimension of each attention head.
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 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_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 (`int`, *optional*, defaults to None):
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.
ssm_state_size (`int`, *optional*, defaults to 128):
The dimension of the mamba state space latents.
mamba_num_heads (`int`, *optional*, defaults to 128):
Number of heads in Mamba layers.
mamba_n_groups (`int`, *optional*, defaults to 8):
Number of groups in Mamba layers.
mamba_head_dim (`int`, *optional*, defaults to 64):
Dimension of each Mamba head.
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.
mamba_conv_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the convolution layer of the mamba mixer block.
mamba_proj_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the input and output projections of the mamba mixer block.
mamba_chunk_size (`int`, *optional*, defaults to 256):
Size of chunks for Mamba processing.
rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
Whether to rescale the pre-normalization residual connections.
"""
model_type = "nemotron_h"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=131072,
tie_word_embeddings=False,
hidden_size=4096,
intermediate_size=21504,
num_hidden_layers=52,
hybrid_override_pattern="M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-",
num_attention_heads=32,
attention_head_dim=128,
num_key_value_heads=8, # nemo: num_query_groups
mlp_hidden_act="relu2",
attention_bias=False,
mlp_bias=False,
use_bias=False,
initializer_range=0.02, # nemo: init_method_std
layer_norm_epsilon=1e-5, # nemo: layernorm_epsilon
residual_in_fp32=False, # Megatron Core default value
use_cache=True,
num_logits_to_keep=1,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
sliding_window=None,
max_position_embeddings=4096,
attention_dropout=0.0,
hidden_dropout=0.0, # * ADDED
use_mamba_kernels=True,
ssm_state_size=128, # mamba_state_size
mamba_num_heads=128,
mamba_n_groups=8, # nemo: mamba_ssm_ngroups = num_heads
mamba_head_dim=64,
mamba_d_conv=4,
mamba_expand=2,
mamba_hidden_act="silu",
mamba_dt_min=0.001,
mamba_dt_max=0.1,
mamba_dt_limit=(0.0, float("inf")),
mamba_dt_init_floor=1e-4,
mamba_conv_bias=True,
mamba_proj_bias=False,
mamba_chunk_size=256,
rescale_prenorm_residual=True,
**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.hybrid_override_pattern = hybrid_override_pattern
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
self.sliding_window = sliding_window
self.max_position_embeddings = max_position_embeddings
self.attention_dropout = attention_dropout
self.hidden_dropout = hidden_dropout
# Validate hybrid_override_pattern
# M: Mamba2, *: Attention, -: MLP
assert len(self.hybrid_override_pattern) == self.num_hidden_layers, "hybrid_override_pattern must have the same length as num_hidden_layers"
assert re.match(r"^[*-M]+$", self.hybrid_override_pattern), "hybrid_override_pattern must only contain characters 'M', '*', or '-'"
# 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.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.layer_norm_epsilon = layer_norm_epsilon
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.num_logits_to_keep = num_logits_to_keep
self.use_mamba_kernels = use_mamba_kernels
self.n_groups = mamba_n_groups
self.mamba_head_dim = mamba_head_dim
self.ssm_state_size = ssm_state_size
self.mamba_num_heads = mamba_num_heads
self.conv_kernel = mamba_d_conv
self.expand = mamba_expand
self.mamba_hidden_act = mamba_hidden_act
self.time_step_min = mamba_dt_min
self.time_step_max = mamba_dt_max
self.time_step_limit = mamba_dt_limit
self.time_step_floor = mamba_dt_init_floor
self.use_conv_bias = mamba_conv_bias
self.mamba_proj_bias = mamba_proj_bias
self.chunk_size = mamba_chunk_size
self.rescale_prenorm_residual = rescale_prenorm_residual
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,
)
@property
def layers_block_type(self):
return [
"mamba" if self.hybrid_override_pattern[i] == "M" else
"attention" if self.hybrid_override_pattern[i] == "*" else "mlp"
for i in range(self.num_hidden_layers)] |