# 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)]