# coding=utf-8 # Copyright 2025 The LLAMA4 and 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. from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class Llama4VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Llama4VisionModel`]. It is used to instantiate a Llama4 vision 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. num_hidden_layers (`int`, *optional*, defaults to 34): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input image. intermediate_size (`int`, *optional*, defaults to 5632): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. vision_output_dim (`int`, *optional*, defaults to 7680): Dimensionality of the vision model output. Includes output of transformer encoder with intermediate layers and global transformer encoder. image_size (`int`, *optional*, defaults to 448): The size (resolution) of each image *tile*. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. vision_feature_layer (``, *optional*, defaults to -1): TODO vision_feature_select_strategy (`int`, *optional*, defaults to `"default"`): TODO initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. pixel_shuffle_ratio (`int`, *optional*, defaults to 0.5): TODO projector_input_dim (`int`, *optional*, defaults to 4096): TODO projector_output_dim (`int`, *optional*, defaults to 4096): TODO multi_modal_projector_bias (`int`, *optional*, defaults to `False`): TODO projector_dropout (`int`, *optional*, defaults to 0.0): TODO attention_dropout (`int`, *optional*, defaults to 0.0): TODO rope_theta (`int`, *optional*, defaults to 10000): TODO """ base_model_tp_plan = { "model.layers.*.self_attn.q_proj": "colwise", "model.layers.*.self_attn.k_proj": "colwise", "model.layers.*.self_attn.v_proj": "colwise", "model.layers.*.self_attn.o_proj": "rowwise", "vision_adapter.mlp.fc1": "colwise", "vision_adapter.mlp.fc2": "rowwise", "patch_embedding.linear": "colwise_rep", } model_type = "llama4_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size: int = 768, hidden_act: str = "gelu", num_hidden_layers: int = 34, num_attention_heads: int = 16, num_channels: int = 3, intermediate_size: int = 5632, vision_output_dim: int = 7680, image_size: int = 448, patch_size: int = 14, norm_eps: float = 1e-5, vision_feature_layer=-1, vision_feature_select_strategy="default", initializer_range: float = 0.02, pixel_shuffle_ratio=0.5, projector_input_dim=4096, projector_output_dim=4096, multi_modal_projector_bias=False, projector_dropout=0.0, attention_dropout=0.0, rope_theta=10000, **kwargs, ): self.hidden_size = hidden_size self.hidden_act = hidden_act self.num_hidden_layers = num_hidden_layers self.num_channels = num_channels self.intermediate_size = intermediate_size self.image_size = image_size self.vision_output_dim = vision_output_dim self.patch_size = patch_size self.norm_eps = norm_eps self.num_attention_heads = num_attention_heads self.initializer_range = initializer_range self.pixel_shuffle_ratio = pixel_shuffle_ratio self.projector_input_dim = projector_input_dim self.projector_output_dim = projector_output_dim self.multi_modal_projector_bias = multi_modal_projector_bias self.projector_dropout = projector_dropout self.attention_dropout = attention_dropout self.vision_feature_layer = vision_feature_layer self.vision_feature_select_strategy = vision_feature_select_strategy self.rope_theta = rope_theta super().__init__(**kwargs) class Llama4TextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Llama4TextModel`]. It is used to instantiate a Llama4 text 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) 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 202048): Vocabulary size of the Llama4 text model. Defines the maximum number of different tokens that can be represented by the `inputs_ids` passed when calling [`Llama4TextModel`]. hidden_size (`int`, *optional*, defaults to 5120): Dimensionality of the embeddings and hidden states. intermediate_size (`int`, *optional*, defaults to 8192): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. intermediate_size_mlp (`int`, *optional*, defaults to 16384): TODO num_hidden_layers (`int`, *optional*, defaults to 48): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 40): 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. If not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 128): TODO hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 131072): 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-05): 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. pad_token_id (`int`, *optional*, defaults to 128004): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 2): The id of the end of sentence token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to `500000.0`): The base period of the RoPE embeddings. attention_dropout (`int`, *optional*, defaults to 0.0): TODO num_experts_per_tok (`int`, *optional*, defaults to 1): TODO num_local_experts (`int`, *optional*, defaults to 16): TODO moe_layers (`int`, *optional*): TODO interleave_moe_layer_step (`int`, *optional*, defaults to 1): TODO use_qk_norm (`int`, *optional*, defaults to `True`): TODO output_router_logits (`int`, *optional*, defaults to `False`): TODO router_aux_loss_coef (`int`, *optional*, defaults to 0.001): TODO router_jitter_noise (`int`, *optional*, defaults to 0.0): TODO 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 no_rope_layers (`int`, *optional*): TODO no_rope_layer_interval (`int`, *optional*, defaults to 4): TODO attention_chunk_size (`int`, *optional*, defaults to 8192): attn_temperature_tuning (`int`, *optional*, defaults to 4): TODO floor_scale (`int`, *optional*, defaults to 8192): TODO attn_scale (`int`, *optional*, defaults to 0.1): TODO cache_implementation (``, *optional*, defaults to `"hybrid"`): Example: """ model_type = "llama4_text" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.input_layernorm.weight": "sequence_parallel", "layers.*.post_attention_layernorm.weight": "sequence_parallel", "norm.weight": "sequence_parallel", "layers.*.feed_forward.shared_expert.gate_proj": "local_colwise", "layers.*.feed_forward.shared_expert.up_proj": "local_colwise", "layers.*.feed_forward.shared_expert.down_proj": "local_rowwise", "layers.*.feed_forward.experts.gate_up_proj": "local_packed_rowwise", # row because not linear "layers.*.feed_forward.experts.down_proj": "local_colwise", # col because not linear "layers.*.feed_forward.experts": "local", "layers.*.feed_forward.gate_proj": "local_colwise", "layers.*.feed_forward.up_proj": "local_colwise", "layers.*.feed_forward.down_proj": "local_rowwise", "layers.*.feed_forward": "gather", } def __init__( self, vocab_size=202048, hidden_size=5120, intermediate_size=8192, intermediate_size_mlp=16384, num_hidden_layers=48, num_attention_heads=40, num_key_value_heads=8, head_dim=128, hidden_act="silu", max_position_embeddings=4096 * 32, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=500000, attention_dropout=0.0, num_experts_per_tok=1, num_local_experts=16, moe_layers=None, interleave_moe_layer_step=1, use_qk_norm=True, output_router_logits=False, router_aux_loss_coef=0.001, router_jitter_noise=0.0, rope_scaling=None, no_rope_layers=None, no_rope_layer_interval=4, attention_chunk_size=8192, attn_temperature_tuning=4, floor_scale=8192, attn_scale=0.1, cache_implementation="hybrid", **kwargs, ): 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, ) self.attn_temperature_tuning = attn_temperature_tuning self.attn_scale = attn_scale self.floor_scale = floor_scale self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.intermediate_size_mlp = intermediate_size_mlp self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.rope_scaling = rope_scaling self.attention_bias = False self.cache_implementation = cache_implementation # 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.attention_dropout = attention_dropout self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads self.use_qk_norm = use_qk_norm self.num_experts_per_tok = num_experts_per_tok self.num_local_experts = num_local_experts self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef self.router_jitter_noise = router_jitter_noise default_no_rope_layers = [ int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(self.num_hidden_layers) ] # no_rope_layers == [] is invalid as we cannot have 0 layers self.no_rope_layers = no_rope_layers if no_rope_layers else default_no_rope_layers self.interleave_moe_layer_step = interleave_moe_layer_step self.moe_layers = ( moe_layers if moe_layers is not None else list(range(interleave_moe_layer_step - 1, num_hidden_layers, interleave_moe_layer_step)) ) self.attention_chunk_size = attention_chunk_size class Llama4Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Llama4Model`]. It is used to instantiate an Llama4 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`Llama4VisionConfig`, *optional*): The Llama4 Vision config. text_config (`Llama4TextConfig`, *optional*): The Llama4 Text config. boi_token_index (`int`, *optional*, defaults to 200080): The begin-of-image token index to wrap the image prompt. eoi_token_index (`int`, *optional*, defaults to 200081): The end-of-image token index to wrap the image prompt. image_token_index (`int`, *optional*, defaults to 200092): The image token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. ```python >>> from transformers import Llama4Model, Llama4Config >>> # Initializing a Llama4 7B style configuration >>> configuration = Llama4Config() >>> # Initializing a model from the Llama4 7B style configuration >>> model = Llama4Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "llama4" sub_configs = {"text_config": Llama4TextConfig, "vision_config": Llama4VisionConfig} base_model_tp_plan = { "multi_modal_projector.linear_1": "colwise_rep", } def __init__( self, vision_config=None, text_config=None, boi_token_index=200080, eoi_token_index=200081, image_token_index=200092, tie_word_embeddings=False, **kwargs, ): if vision_config is None: self.vision_config = Llama4VisionConfig() logger.info("vision_config is None, using default llama4 vision config") elif isinstance(vision_config, dict): self.vision_config = Llama4VisionConfig(**vision_config) elif isinstance(vision_config, Llama4VisionConfig): self.vision_config = vision_config self.boi_token_index = boi_token_index self.eoi_token_index = eoi_token_index self.image_token_index = image_token_index if text_config is None: self.text_config = Llama4TextConfig() logger.info("text_config is None, using default llama4 text config") elif isinstance(text_config, dict): self.text_config = Llama4TextConfig(**text_config) elif isinstance(text_config, Llama4TextConfig): self.text_config = text_config super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) __all__ = ["Llama4Config", "Llama4TextConfig", "Llama4VisionConfig"]