# coding=utf-8 # Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved. # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2026, CENO 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. """CENO model configuration""" import re from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class CENOConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CENOModel`]. It is used to instantiate a CENO 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 CENO-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 CENO model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CENOModel`] 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 `"MMMM*MMMMM*MMMMM*MMMMM*MMMMMM"`): The pattern of the hybrid model. The pattern is a string of characters where each character represents one layer: M=Mamba, *=Attention, -=MLP, E=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. mlp_use_swiglu (`bool`, *optional*, defaults to `False`): Whether to use SwiGLU activation (gated linear unit) in MLP layers. num_experts (`int`, *optional*, defaults to 8): Number of experts for MoE layers. top_k (`int`, *optional*, defaults to 2): Number of experts to select for each token in MoE 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_in_proj_layernorm (`bool`, *optional*, defaults to `False`): Whether to apply an additional RMSNorm before the Mamba in-projection. This matches Megatron's TELayerNormColumnParallelLinear fused Mamba in_proj norm when enabled. 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. num_experts (`int`, *optional*, defaults to 8): Number of experts in MoE layers. moe_top_k (`int`, *optional*, defaults to 2): Number of experts to route to in MoE layers (renamed from top_k to avoid generation config conflicts). qkv_layernorm (`bool`, *optional*, defaults to `False`): Whether to apply an additional RMSNorm before QKV projections in attention layers. This matches Megatron's TELayerNormColumnParallelLinear fused QKV norm when enabled. intra_encoding_pattern (`str`, *optional*, defaults to `None`): Per-layer switch (length must equal `num_hidden_layers`) to control which layers use intra-sequence isolation. Characters in `{1, Y, y, S, s, T, t, +}` enable isolation; `{0, N, n, F, f, ., -}` disable. If omitted, intra-sequence isolation is disabled (legacy behavior). """ model_type = "ceno" 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="MMMM*MMMMM*MMMMM*MMMMM*MMMMMM", num_attention_heads=32, attention_head_dim=128, num_key_value_heads=8, # nemo: num_query_groups mlp_hidden_act="relu2", mlp_use_swiglu=False, num_experts=8, moe_top_k=2, moe_router_pre_softmax=False, attention_bias=False, qkv_layernorm=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=1, bos_token_id=0, eos_token_id=0, 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_in_proj_layernorm=False, mamba_chunk_size=256, rescale_prenorm_residual=True, intra_encoding_pattern=None, **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 self.qkv_layernorm = qkv_layernorm # Validate and process hybrid_override_pattern # M: Mamba, *: Attention, -: MLP, E: MoE if self.hybrid_override_pattern is None: # Default pattern with even distribution of attention layers self.hybrid_override_pattern = self._generate_default_pattern() # Pattern should be a string where each character represents one layer # No processing needed since '-' represents MLP layers, not separators # For the default pattern, adjust it if num_hidden_layers doesn't match if len(self.hybrid_override_pattern) != self.num_hidden_layers: # If using default pattern and layers don't match, regenerate if self.hybrid_override_pattern == "MMMM*MMMMM*MMMMM*MMMMM*MMMMMM": self.hybrid_override_pattern = self._generate_default_pattern() else: # Only assert if it's a user-provided pattern assert len(self.hybrid_override_pattern) == self.num_hidden_layers, f"hybrid_override_pattern length ({len(self.hybrid_override_pattern)}) must match num_hidden_layers ({self.num_hidden_layers})" assert re.match(r"^[M*\-E]+$", self.hybrid_override_pattern), "hybrid_override_pattern must only contain characters 'M', '*', '-', or 'E'" # 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.mlp_use_swiglu = mlp_use_swiglu self.num_experts = num_experts self.moe_top_k = moe_top_k # Megatron MoE routing: default is post-topk softmax (i.e., softmax after selecting top-k experts). # This flag mirrors Megatron's `--moe-router-pre-softmax`. self.moe_router_pre_softmax = moe_router_pre_softmax 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.mamba_in_proj_layernorm = mamba_in_proj_layernorm self.chunk_size = mamba_chunk_size self.rescale_prenorm_residual = rescale_prenorm_residual self.intra_encoding_pattern = intra_encoding_pattern # MoE parameters self.num_experts = num_experts self.moe_top_k = moe_top_k 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, ) def _generate_default_pattern(self): """Generate default hybrid pattern with even distribution of attention layers""" # Default to mostly Mamba with some attention layers evenly distributed pattern = ["M"] * self.num_hidden_layers # Place attention layers at roughly even intervals if self.num_hidden_layers >= 4: # For larger models, place attention every 4-5 layers attention_interval = max(4, self.num_hidden_layers // 8) for i in range(attention_interval - 1, self.num_hidden_layers, attention_interval): pattern[i] = "*" return ''.join(pattern) @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" if self.hybrid_override_pattern[i] == "-" else "moe" for i in range(self.num_hidden_layers)] @property def intra_encoding_mask(self): """ Per-layer boolean mask for intra-sequence isolation. If `intra_encoding_pattern` is provided, it is used; otherwise isolation is disabled. """ if getattr(self, "intra_encoding_pattern", None) is not None: pat = self.intra_encoding_pattern if len(pat) != self.num_hidden_layers: raise ValueError( f"intra_encoding_pattern length ({len(pat)}) must match num_hidden_layers ({self.num_hidden_layers})" ) enable_set = {"1", "Y", "y", "S", "s", "T", "t", "+"} disable_set = {"0", "N", "n", "F", "f", ".", "-"} return [c in enable_set if c in enable_set.union(disable_set) else True for c in pat] # Default/legacy: no intra-sequence isolation return [False for _ in range(self.num_hidden_layers)]