import torch from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class MotifConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MotifModel`]. It is used to instantiate a Motif model according to the specified arguments, defining the model architecture. 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 151936): Vocabulary size of the Motif model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MotifModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22016): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. 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 32): 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. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 4096): Sliding window attention (SWA) window size. If not specified, will default to `4096`. max_window_layers (`int`, *optional*, defaults to 28): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import MotifModel, MotifConfig >>> # Initializing a Motif style configuration >>> configuration = MotifConfig() >>> # Initializing a model from the Motif-102B style configuration >>> model = MotifModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "Motif" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { # Attention "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", # Dense MLP "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", # MoE experts (fused gate+up) "layers.*.moe.experts.gate_up_proj": "packed_colwise", "layers.*.moe.experts.down_proj": "rowwise", # Shared experts "layers.*.moe.shared_experts.gate_proj": "colwise", "layers.*.moe.shared_experts.up_proj": "colwise", "layers.*.moe.shared_experts.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=151936, hidden_size=4096, intermediate_size=22016, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, rope_scaling=None, use_sliding_window=False, sliding_window=4096, max_window_layers=28, sliding_window_pattern="interleave", sliding_window_period=2, attention_dropout=0.0, # Differential Attention parameters head_dim=None, num_noise_heads=0, k_ratio=1, # MoE parameters num_experts=0, experts_top_k=2, num_shared_experts=0, interleave_moe_layer_step=0, moe_intermediate_size=None, score_func="softmax", route_norm=False, route_scale=1.0, load_balance_coeff=None, score_before_experts=False, _debug_force_load_balance=False, output_router_logits=False, router_aux_loss_coef=0.0, # MHC (Manifold-constrained Hyper-Connections) parameters mhc_enabled=False, mhc_expansion_rate=4, mhc_identity_init=False, mhc_sinkhorn_iters=20, # DiffAttention V2 / Attention class diff_v2=False, attention_cls="basic", # GDLA (Grouped Differential Latent Attention) parameters q_lora_rank=0, kv_lora_rank=0, qk_rope_head_dim=None, v_head_dim=None, original_seq_len=32768, rope_factor=1.0, mscale=1.0, swa_rope_theta=None, # Attention output gating headwise_attn_output_gate=False, elementwise_attn_output_gate=False, # MoE: first N layers always dense (no MoE), regardless of interleave schedule n_dense_first_layers=0, # MTP (Multi-Token Prediction) speculative decoding num_nextn_predict_layers=0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window if use_sliding_window else None self.max_window_layers = max_window_layers self.sliding_window_pattern = sliding_window_pattern self.sliding_window_period = sliding_window_period # 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.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_dropout = attention_dropout # Differential Attention configuration self.head_dim = head_dim self.num_noise_heads = num_noise_heads self.k_ratio = k_ratio # MoE configuration self.num_experts = num_experts self.experts_top_k = experts_top_k self.num_shared_experts = num_shared_experts self.interleave_moe_layer_step = interleave_moe_layer_step self.moe_intermediate_size = moe_intermediate_size if moe_intermediate_size is not None else intermediate_size self.score_func = score_func self.route_norm = route_norm self.route_scale = route_scale self.load_balance_coeff = load_balance_coeff self.score_before_experts = score_before_experts self._debug_force_load_balance = _debug_force_load_balance self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef # MHC configuration self.mhc_enabled = mhc_enabled self.mhc_expansion_rate = mhc_expansion_rate self.mhc_identity_init = mhc_identity_init self.mhc_sinkhorn_iters = mhc_sinkhorn_iters # DiffAttention V2 / Attention class self.diff_v2 = diff_v2 self.attention_cls = attention_cls # GDLA parameters self.q_lora_rank = q_lora_rank self.kv_lora_rank = kv_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.original_seq_len = original_seq_len self.rope_factor = rope_factor self.mscale = mscale self.swa_rope_theta = swa_rope_theta # Attention output gating self.headwise_attn_output_gate = headwise_attn_output_gate self.elementwise_attn_output_gate = elementwise_attn_output_gate # MoE dense-first layers self.n_dense_first_layers = n_dense_first_layers # MTP speculative decoding self.num_nextn_predict_layers = num_nextn_predict_layers # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] # Motif applies the YaRN mscale on the attention softmax scale # (DeepSeek-style, full-attention layers only), never on the cos/sin table. # Default attention_factor=1.0 so transformers' YaRN rope init does not ALSO # scale cos/sin (which would double-apply mscale); apply_yarn_scaling=False # makes that intent explicit for the modeling code (MotifRotaryEmbedding). if self.rope_scaling is not None and self.rope_scaling.get("rope_type") == "yarn": self.rope_scaling.setdefault("attention_factor", 1.0) self.rope_scaling.setdefault("apply_yarn_scaling", False) if callable(getattr(type(self), "validate_rope", None)): self.validate_rope() # The per-expert PolyNorm activation (GroupedPolyNorm) is only correct via # the EAGER experts loop: the grouped_mm / batched_mm interfaces call # _apply_gate once over all expert-sorted tokens with no per-expert index, # so they cannot apply per-expert coefficients. Force eager dispatch for # MoE Motif models unless the caller explicitly overrides it. (This also # covers ROCm, where torch._grouped_mm has no runtime kernel.) if self.num_experts > 0 and "experts_implementation" not in kwargs: kwargs["experts_implementation"] = "eager" super().__init__( tie_word_embeddings=tie_word_embeddings, **kwargs, ) logger.info(f" kwargs : {kwargs}")