# Copyright 2024-2025 NVIDIA Corporation 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. """NemotronH model configuration""" 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 NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16). 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`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. layers_block_type (`list`, *optional*): Explicit list of layer types for each layer. Each element must be one of: "mamba", "attention", or "moe". The number of layers is determined by the length of this list. num_hidden_layers (`int`, *optional*): Number of hidden layers in the Transformer encoder. This parameter is deprecated and only kept for backward compatibility. The number of layers is now determined by the length of `layers_block_type`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions. num_logits_to_keep (`int`, *optional*, defaults to 1): Number of prompt logits to calculate during generation. If `None`, all 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. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. 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. head_dim (`int`, *optional*, defaults to 128): Dimension of each attention head. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in attention layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. sliding_window (`int`, *optional*): Sliding window attention window size. intermediate_size (`int`, *optional*, defaults to 21504): Dimension of the MLP representations. mlp_hidden_act (`str`, *optional*, defaults to `"relu2"`): The non-linear activation function in the MLP layers. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in MLP layers. use_mamba_kernels (`bool`, *optional*, defaults to `True`): Flag indicating whether or not to use the fast mamba kernels. 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, inf)`): Limits for the time step in Mamba. mamba_dt_init_floor (`float`, *optional*, defaults to 0.0001): 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 128): Size of chunks for Mamba processing. mamba_ssm_cache_dtype (`str`, *optional*, defaults to `"float32"`): Data type for Mamba SSM cache states. n_routed_experts (`int`, *optional*, defaults to 8): Number of routed experts in MoE layers. n_shared_experts (`int`, *optional*, defaults to 1): Number of shared experts that are always activated in MoE layers. moe_intermediate_size (`int`, *optional*, defaults to 7688): Dimension of the MLP representations in routed experts. moe_shared_expert_intermediate_size (`int`, *optional*, defaults to 7688): Dimension of the MLP representations in shared experts. moe_latent_size (`int`, *optional*): Latent size for MoE expert projections. If `None`, uses `hidden_size`. moe_shared_expert_overlap (`bool`, *optional*, defaults to `True`): Whether shared experts overlap with routed experts. num_experts_per_tok (`int`, *optional*, defaults to 2): The number of experts to route per token (top-k routing parameter). routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor applied to routed expert outputs. n_group (`int`, *optional*, defaults to 1): Number of groups for expert routing. topk_group (`int`, *optional*, defaults to 1): Top-k group parameter for expert selection. norm_topk_prob (`bool`, *optional*, defaults to `True`): Whether to normalize top-k probabilities in expert routing. num_nextn_predict_layers (`int`, *optional*, defaults to 0): Number of additional layers for multi-token prediction. If 0, multi-token prediction is disabled. mtp_layers_block_type (`list`, *optional*, defaults to `['attention', 'moe']`): Explicit list of layer types for multi-token prediction layers when `num_nextn_predict_layers` > 0. 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-05): The epsilon used by the layer normalization layers. residual_in_fp32 (`bool`, *optional*, defaults to `False`): Whether or not residuals should be in `float32`. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the hidden states. rescale_prenorm_residual (`bool`, *optional*, defaults to `True`): Whether to rescale the pre-normalization residual connections. ```python >>> from transformers import NemotronHModel, NemotronHConfig >>> # Initializing a NemotronH configuration >>> configuration = NemotronHConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = NemotronHModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "nemotron_h" keys_to_ignore_at_inference = ["past_key_values"] @staticmethod def _validate_layers_block_type(layers_block_type, expected_length=None, param_name="layers_block_type"): """ Validate layers_block_type list. Args: layers_block_type: List of layer types to validate expected_length: If provided, validate the list has this length param_name: Parameter name for error messages Raises: ValueError: If validation fails """ if not isinstance(layers_block_type, list): raise ValueError(f"{param_name} must be a list of strings. Got type: {type(layers_block_type)}") if expected_length is not None and len(layers_block_type) != expected_length: raise ValueError(f"{param_name} must have length {expected_length}. Got length {len(layers_block_type)}.") valid_types = {"mamba", "attention", "moe"} if not all(block_type in valid_types for block_type in layers_block_type): invalid = set(layers_block_type) - valid_types raise ValueError(f"{param_name} contains invalid types: {invalid}. Must be one of: {valid_types}") def __init__( self, # General model config vocab_size=131072, hidden_size=4096, layers_block_type=None, num_hidden_layers=None, # Deprecated, only for backward compatibility tie_word_embeddings=False, use_cache=True, num_logits_to_keep=1, # Token IDs pad_token_id=0, bos_token_id=1, eos_token_id=2, # Attention layer config num_attention_heads=32, num_key_value_heads=8, head_dim=128, max_position_embeddings=4096, attention_bias=False, attention_dropout=0.0, sliding_window=None, # MLP layer config intermediate_size=21504, mlp_hidden_act="relu2", mlp_bias=False, # Mamba layer config use_mamba_kernels=True, ssm_state_size=128, mamba_num_heads=128, mamba_n_groups=8, 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=128, mamba_ssm_cache_dtype="float32", # MoE config n_routed_experts=8, n_shared_experts=1, moe_intermediate_size=7688, moe_shared_expert_intermediate_size=7688, moe_latent_size=None, moe_shared_expert_overlap=True, num_experts_per_tok=2, routed_scaling_factor=1.0, n_group=1, topk_group=1, norm_topk_prob=True, # Multi-token prediction config num_nextn_predict_layers=0, mtp_layers_block_type=["attention", "moe"], # General training config use_bias=False, initializer_range=0.02, layer_norm_epsilon=1e-5, residual_in_fp32=False, hidden_dropout=0.0, rescale_prenorm_residual=True, **kwargs, ): # Backward compatibility: convert hybrid_override_pattern to layers_block_type # Always pop hybrid_override_pattern from kwargs to prevent it from being set as an attribute if "hybrid_override_pattern" in kwargs: pattern = kwargs.pop("hybrid_override_pattern") if layers_block_type is None: layers_block_type = self._pattern_to_list(pattern) elif layers_block_type is None: # Default layers_block_type if not provided layers_block_type = ["mamba", "moe", "attention", "moe"] # Note: num_hidden_layers is deprecated and ignored if layers_block_type is explicitly provided # It's only kept for backward compatibility when loading old configs if num_hidden_layers is not None: # Warn if num_hidden_layers is provided but doesn't match layers_block_type if len(layers_block_type) != num_hidden_layers: logger.warning( f"num_hidden_layers ({num_hidden_layers}) is deprecated and doesn't match " f"layers_block_type length ({len(layers_block_type)}). Using layers_block_type length." ) # Backward compatibility: convert mtp_hybrid_override_pattern to mtp_layers_block_type # Always pop mtp_hybrid_override_pattern from kwargs to prevent it from being set as an attribute if "mtp_hybrid_override_pattern" in kwargs: pattern = kwargs.pop("mtp_hybrid_override_pattern") if mtp_layers_block_type is None or mtp_layers_block_type == ["attention", "moe"]: mtp_layers_block_type = self._pattern_to_list(pattern) self.vocab_size = vocab_size self.tie_word_embeddings = tie_word_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_attention_heads = num_attention_heads self.head_dim = 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 layers_block_type (no longer checking length against num_hidden_layers) self._validate_layers_block_type(layers_block_type, expected_length=None, param_name="layers_block_type") self.layers_block_type = layers_block_type # 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 self.n_routed_experts = n_routed_experts self.n_shared_experts = n_shared_experts self.moe_intermediate_size = moe_intermediate_size self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size self.moe_latent_size = moe_latent_size self.moe_shared_expert_overlap = moe_shared_expert_overlap self.num_experts_per_tok = num_experts_per_tok self.routed_scaling_factor = routed_scaling_factor self.n_group = n_group self.topk_group = topk_group self.norm_topk_prob = norm_topk_prob self.mamba_ssm_cache_dtype = mamba_ssm_cache_dtype # MTP config self.num_nextn_predict_layers = num_nextn_predict_layers # Validate mtp_layers_block_type is provided when MTP is enabled if self.num_nextn_predict_layers > 0: if mtp_layers_block_type is None: raise ValueError( "mtp_layers_block_type is required when num_nextn_predict_layers > 0. " "Please provide an explicit list of layer types for MTP layers. " "Example: mtp_layers_block_type=['attention', 'moe']" ) self._validate_layers_block_type(mtp_layers_block_type, None, "mtp_layers_block_type") self.mtp_layers_block_type = mtp_layers_block_type 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 num_hidden_layers(self) -> int: """ Number of hidden layers derived from the length of layers_block_type. This property replaces the deprecated num_hidden_layers parameter. """ return len(self.layers_block_type) @num_hidden_layers.setter def num_hidden_layers(self, value): """ Setter for backward compatibility when loading configs. The value is ignored since num_hidden_layers is computed from layers_block_type. """ # Ignore the value - num_hidden_layers is always derived from layers_block_type pass @property def hybrid_override_pattern(self) -> str: """ Backward compatibility property. Returns the pattern string representation of layers_block_type. """ return self._list_to_pattern(self.layers_block_type) @property def mtp_hybrid_override_pattern(self) -> str: """ Backward compatibility property. Returns the pattern string representation of mtp_layers_block_type. """ return self._list_to_pattern(self.mtp_layers_block_type) @staticmethod def _list_to_pattern(layers_list: list) -> str: """Convert list of layer types back to pattern string (for backward compatibility).""" reverse_mapping = {"mamba": "M", "moe": "E", "attention": "*"} return "".join(reverse_mapping[layer_type] for layer_type in layers_list) @staticmethod def _pattern_to_list(pattern: str) -> list: """Convert pattern string to list of layer types (for backward compatibility).""" pattern_mapping = {"M": "mamba", "E": "moe", "*": "attention"} return [pattern_mapping[char] for char in pattern] __all__ = ["NemotronHConfig"]