# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2025 SGLang Team # 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. # ============================================================================== # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/configs/nemotron_h.py """NemotronH model configuration""" import copy from typing import Any from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from sglang.srt.configs.mamba_utils import ( Mamba2CacheParams, Mamba2StateShape, mamba2_state_dtype, ) from sglang.srt.runtime_context import get_parallel logger = logging.get_logger(__name__) MAMBA = "M" ATTENTION = "*" MLP = "-" MOE = "E" DEFAULT_LAYERS_BLOCK_TYPE = ["mamba", "moe", "attention", "moe"] DEFAULT_MTP_LAYERS_BLOCK_TYPE = ["attention", "moe"] DEFAULT_MAMBA_CHUNK_SIZE = 256 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. 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 an 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*): Deprecated. Kept only for backward compatibility. The effective layer count is derived from `layers_block_type`. 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-"`): Deprecated compatibility field. Pattern string where each character represents Mamba2 (`M`), Attention (`*`), MLP (`-`), or MoE (`E`). layers_block_type (`list[str]`, *optional*): Canonical layer layout. Each entry is one of: `"mamba"`, `"attention"`, `"mlp"`, `"moe"`. 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"] @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", "mlp", "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}" ) @staticmethod def _resolve_layers_block_type( layers_block_type, hybrid_override_pattern, kwargs ) -> list[str]: """Resolve canonical layers_block_type from new and legacy config fields.""" # Prefer explicit kwargs override first (legacy HF path), otherwise use # the function argument value from config fields. pattern = kwargs.pop("hybrid_override_pattern", hybrid_override_pattern) if layers_block_type is None: if pattern is not None: layers_block_type = NemotronHConfig._pattern_to_list(pattern) else: # Last-resort fallback to preserve compatibility when neither # canonical nor legacy pattern fields are provided. layers_block_type = DEFAULT_LAYERS_BLOCK_TYPE return layers_block_type @staticmethod def _resolve_mtp_layers_block_type(mtp_layers_block_type, kwargs) -> list[str]: """Resolve canonical mtp_layers_block_type from new and legacy config fields.""" 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 = NemotronHConfig._pattern_to_list(pattern) return mtp_layers_block_type @staticmethod def _resolve_mamba_chunk_size(mamba_chunk_size, kwargs) -> int: """Resolve canonical mamba_chunk_size from new and legacy config fields.""" chunk_size = kwargs.pop("chunk_size", None) if ( mamba_chunk_size is not None and chunk_size is not None and mamba_chunk_size != chunk_size ): logger.warning( "Both chunk_size=%s and mamba_chunk_size=%s were provided. " "Using mamba_chunk_size.", chunk_size, mamba_chunk_size, ) if mamba_chunk_size is None: mamba_chunk_size = chunk_size if mamba_chunk_size is None: mamba_chunk_size = DEFAULT_MAMBA_CHUNK_SIZE return mamba_chunk_size def __init__( self, *, vocab_size=131072, tie_word_embeddings=False, hidden_size=4096, intermediate_size=21504, num_hidden_layers=None, # Deprecated, only for backward compatibility 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-", layers_block_type=None, num_attention_heads=32, 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=None, rescale_prenorm_residual=True, n_routed_experts=8, n_shared_experts=1, moe_intermediate_size=7688, moe_shared_expert_intermediate_size=7688, moe_latent_size=None, num_experts_per_tok=2, routed_scaling_factor=1.0, n_group=1, topk_group=1, norm_topk_prob=True, num_nextn_predict_layers=0, mtp_layers_block_type=DEFAULT_MTP_LAYERS_BLOCK_TYPE, **kwargs, ): mamba_chunk_size = self._resolve_mamba_chunk_size(mamba_chunk_size, kwargs) # Compatibility parsing: normalize legacy pattern fields into canonical list fields. layers_block_type = self._resolve_layers_block_type( layers_block_type, hybrid_override_pattern, kwargs ) mtp_layers_block_type = self._resolve_mtp_layers_block_type( mtp_layers_block_type, kwargs ) # num_hidden_layers is deprecated and ignored as a source of truth. if ( num_hidden_layers is not None and 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." ) # Core model attributes. 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 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 # Mamba attributes. self.use_mamba_kernels = use_mamba_kernels self.mamba_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.mamba_chunk_size = mamba_chunk_size self.rescale_prenorm_residual = rescale_prenorm_residual # MoE attributes. 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.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 # MTP attributes. self.num_nextn_predict_layers = num_nextn_predict_layers 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 mamba_layer_ids(self): return [ i for i in range(self.num_hidden_layers) if self.hybrid_override_pattern[i] == MAMBA ] @property def full_attention_layer_ids(self): return [ i for i in range(self.num_hidden_layers) if self.hybrid_override_pattern[i] == ATTENTION ] @property def mamba2_cache_params(self) -> Mamba2CacheParams: shape = Mamba2StateShape.create( tp_world_size=get_parallel().attn_tp_size, intermediate_size=self.mamba_num_heads * self.mamba_head_dim, n_groups=self.n_groups, num_heads=self.mamba_num_heads, head_dim=self.mamba_head_dim, state_size=self.ssm_state_size, conv_kernel=self.conv_kernel, ) return Mamba2CacheParams( shape=shape, layers=self.mamba_layer_ids, dtype=mamba2_state_dtype(self) ) @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. """ 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) @hybrid_override_pattern.setter def hybrid_override_pattern(self, value): """ Setter for backward compatibility when loading configs. """ self.layers_block_type = self._pattern_to_list(value) @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) @mtp_hybrid_override_pattern.setter def mtp_hybrid_override_pattern(self, value): """Setter for backward compatibility when loading configs.""" self.mtp_layers_block_type = self._pattern_to_list(value) @staticmethod def _list_to_pattern(layers_list: list[str]) -> str: """Convert list of layer types back to pattern string (for backward compatibility).""" reverse_mapping = { "mamba": MAMBA, "moe": MOE, "attention": ATTENTION, "mlp": MLP, } return "".join(reverse_mapping[layer_type] for layer_type in layers_list) @staticmethod def _pattern_to_list(pattern: str) -> list[str]: """Convert pattern string to list of layer types (for backward compatibility).""" if any(char not in {MAMBA, MOE, ATTENTION, MLP} for char in pattern): raise ValueError( "Pattern must only contain characters 'M', '*', '-' or 'E'. " f"Got: {pattern}" ) pattern_mapping = { MAMBA: "mamba", MOE: "moe", ATTENTION: "attention", MLP: "mlp", } return [pattern_mapping[char] for char in pattern] def get_nemotron_h_config_for_layer(self, layer_idx: int) -> "NemotronHConfig": return self def get_mtp_config(self) -> "NemotronHConfig": return self @property def max_n_routed_experts(self) -> int: return self.n_routed_experts class NemotronHPuzzleConfig(NemotronHConfig): model_type = "nemotron_h_puzzle" has_no_defaults_at_init = True def __init__( self, *, block_configs: list[dict[str, Any]], mtp_block_configs: list[dict[str, Any]] | None = None, **kwargs, ): super().__init__(**kwargs) self.block_configs = block_configs self.mtp_block_configs = mtp_block_configs def get_nemotron_h_config_for_layer(self, layer_idx: int) -> NemotronHConfig: layer_config = copy.copy(self) for key, value in self.block_configs[layer_idx].items(): setattr(layer_config, key, value) return layer_config def get_mtp_config(self) -> NemotronHConfig: assert self.mtp_block_configs mtp_config = copy.copy(self) mtp_config.block_configs = self.mtp_block_configs return mtp_config @property def max_n_routed_experts(self) -> int: block_n_routed_experts = [ block["n_routed_experts"] for block in self.block_configs if block["block_type"] == "moe" ] max_experts = max(block_n_routed_experts) assert max_experts > 0 return max_experts