470 lines
22 KiB
Python
470 lines
22 KiB
Python
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# coding=utf-8
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# Copyright 2025 The LLAMA4 and HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class Llama4VisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Llama4VisionModel`]. It is used to instantiate a
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Llama4 vision model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Llama4 109B.
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e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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num_hidden_layers (`int`, *optional*, defaults to 34):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input image.
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intermediate_size (`int`, *optional*, defaults to 5632):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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vision_output_dim (`int`, *optional*, defaults to 7680):
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Dimensionality of the vision model output. Includes output of transformer
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encoder with intermediate layers and global transformer encoder.
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image_size (`int`, *optional*, defaults to 448):
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The size (resolution) of each image *tile*.
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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vision_feature_layer (``, *optional*, defaults to -1): TODO
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vision_feature_select_strategy (`int`, *optional*, defaults to `"default"`): TODO
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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pixel_shuffle_ratio (`int`, *optional*, defaults to 0.5): TODO
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projector_input_dim (`int`, *optional*, defaults to 4096): TODO
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projector_output_dim (`int`, *optional*, defaults to 4096): TODO
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multi_modal_projector_bias (`int`, *optional*, defaults to `False`): TODO
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projector_dropout (`int`, *optional*, defaults to 0.0): TODO
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attention_dropout (`int`, *optional*, defaults to 0.0): TODO
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rope_theta (`int`, *optional*, defaults to 10000): TODO
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"""
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base_model_tp_plan = {
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"model.layers.*.self_attn.q_proj": "colwise",
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"model.layers.*.self_attn.k_proj": "colwise",
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"model.layers.*.self_attn.v_proj": "colwise",
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"model.layers.*.self_attn.o_proj": "rowwise",
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"vision_adapter.mlp.fc1": "colwise",
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"vision_adapter.mlp.fc2": "rowwise",
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"patch_embedding.linear": "colwise_rep",
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}
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model_type = "llama4_vision_model"
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base_config_key = "vision_config"
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def __init__(
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self,
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hidden_size: int = 768,
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hidden_act: str = "gelu",
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num_hidden_layers: int = 34,
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num_attention_heads: int = 16,
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num_channels: int = 3,
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intermediate_size: int = 5632,
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vision_output_dim: int = 7680,
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image_size: int = 448,
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patch_size: int = 14,
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norm_eps: float = 1e-5,
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vision_feature_layer=-1,
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vision_feature_select_strategy="default",
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initializer_range: float = 0.02,
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pixel_shuffle_ratio=0.5,
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projector_input_dim=4096,
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projector_output_dim=4096,
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multi_modal_projector_bias=False,
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projector_dropout=0.0,
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attention_dropout=0.0,
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rope_theta=10000,
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**kwargs,
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):
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self.hidden_size = hidden_size
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self.hidden_act = hidden_act
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self.num_hidden_layers = num_hidden_layers
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self.num_channels = num_channels
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self.intermediate_size = intermediate_size
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self.image_size = image_size
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self.vision_output_dim = vision_output_dim
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self.patch_size = patch_size
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self.norm_eps = norm_eps
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self.num_attention_heads = num_attention_heads
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self.initializer_range = initializer_range
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self.pixel_shuffle_ratio = pixel_shuffle_ratio
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self.projector_input_dim = projector_input_dim
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self.projector_output_dim = projector_output_dim
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self.multi_modal_projector_bias = multi_modal_projector_bias
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self.projector_dropout = projector_dropout
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self.attention_dropout = attention_dropout
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self.vision_feature_layer = vision_feature_layer
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self.vision_feature_select_strategy = vision_feature_select_strategy
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self.rope_theta = rope_theta
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super().__init__(**kwargs)
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class Llama4TextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Llama4TextModel`]. It is used to instantiate a
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Llama4 text model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Llama4 109B.
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e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 202048):
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Vocabulary size of the Llama4 text model. Defines the maximum number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`Llama4TextModel`].
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hidden_size (`int`, *optional*, defaults to 5120):
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Dimensionality of the embeddings and hidden states.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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intermediate_size_mlp (`int`, *optional*, defaults to 16384): TODO
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 40):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If not
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specified, will default to `num_attention_heads`.
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head_dim (`int`, *optional*, defaults to 128): TODO
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hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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max_position_embeddings (`int`, *optional*, defaults to 131072):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions.
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pad_token_id (`int`, *optional*, defaults to 128004):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the beginning of sentence token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the end of sentence token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to `500000.0`):
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The base period of the RoPE embeddings.
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attention_dropout (`int`, *optional*, defaults to 0.0): TODO
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num_experts_per_tok (`int`, *optional*, defaults to 1): TODO
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num_local_experts (`int`, *optional*, defaults to 16): TODO
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moe_layers (`int`, *optional*): TODO
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interleave_moe_layer_step (`int`, *optional*, defaults to 1): TODO
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use_qk_norm (`int`, *optional*, defaults to `True`): TODO
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output_router_logits (`int`, *optional*, defaults to `False`): TODO
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router_aux_loss_coef (`int`, *optional*, defaults to 0.001): TODO
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router_jitter_noise (`int`, *optional*, defaults to 0.0): TODO
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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<TODO>
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<TODO>
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no_rope_layers (`list[int]`, *optional*):
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List with at least the same length as the number of layers in the model.
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A `1` at an index position indicates that the corresponding layer will use RoPE,
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while a `0` indicates that it's a NoPE layer.
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no_rope_layer_interval (`int`, *optional*, defaults to 4):
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If `no_rope_layers` is `None`, it will be created using a NoPE layer every
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`no_rope_layer_interval` layers.
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attention_chunk_size (`int`, *optional*, defaults to 8192):
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<TODO>
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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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attn_temperature_tuning (`bool`, *optional*, defaults to `True`):
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Whether to dynamically scale the attention temperature for each query token based on sequence length.
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Recommended for long sequences (e.g., >32k tokens) to maintain stable output results.
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floor_scale (`int`, *optional*, defaults to 8192): TODO
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attn_scale (`int`, *optional*, defaults to 0.1): TODO
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Example:
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"""
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model_type = "llama4_text"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.input_layernorm.weight": "sequence_parallel",
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"layers.*.post_attention_layernorm.weight": "sequence_parallel",
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"norm.weight": "sequence_parallel",
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"layers.*.feed_forward.shared_expert.gate_proj": "local_colwise",
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"layers.*.feed_forward.shared_expert.up_proj": "local_colwise",
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"layers.*.feed_forward.shared_expert.down_proj": "local_rowwise",
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"layers.*.feed_forward.experts.gate_up_proj": "local_packed_rowwise", # row because not linear
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"layers.*.feed_forward.experts.down_proj": "local_colwise", # col because not linear
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"layers.*.feed_forward.experts": "local",
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"layers.*.feed_forward.gate_proj": "local_colwise",
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"layers.*.feed_forward.up_proj": "local_colwise",
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"layers.*.feed_forward.down_proj": "local_rowwise",
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"layers.*.feed_forward": "gather",
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}
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base_model_ep_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.feed_forward.experts.gate_up_proj": "grouped_gemm", # row because not linear
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"layers.*.feed_forward.experts.down_proj": "grouped_gemm", # col because not linear
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"layers.*.feed_forward.experts": "gather", # all reduce
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"layers.*.feed_forward.gate_proj": "local_colwise",
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"layers.*.feed_forward.up_proj": "local_colwise",
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"layers.*.feed_forward.down_proj": "local_rowwise",
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"layers.*.feed_forward.router": "ep_router",
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}
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def __init__(
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self,
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vocab_size=202048,
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hidden_size=5120,
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intermediate_size=8192,
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intermediate_size_mlp=16384,
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num_hidden_layers=48,
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num_attention_heads=40,
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num_key_value_heads=8,
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head_dim=128,
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hidden_act="silu",
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max_position_embeddings=4096 * 32,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_theta=500000,
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attention_dropout=0.0,
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num_experts_per_tok=1,
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num_local_experts=16,
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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,
|
||
|
|
layer_types=None,
|
||
|
|
attn_temperature_tuning=True,
|
||
|
|
floor_scale=8192,
|
||
|
|
attn_scale=0.1,
|
||
|
|
**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
|
||
|
|
# 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
|
||
|
|
|
||
|
|
# Backwards compatibility
|
||
|
|
if no_rope_layers == []:
|
||
|
|
no_rope_layers = None
|
||
|
|
|
||
|
|
default_no_rope_layers = [
|
||
|
|
int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(self.num_hidden_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
|
||
|
|
|
||
|
|
self.layer_types = layer_types
|
||
|
|
if layer_types is None:
|
||
|
|
self.layer_types = [
|
||
|
|
"chunked_attention" if no_rope else "full_attention" for no_rope in self.no_rope_layers
|
||
|
|
]
|
||
|
|
layer_type_validation(self.layer_types)
|
||
|
|
|
||
|
|
|
||
|
|
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"
|
||
|
|
attribute_map = {
|
||
|
|
"image_token_id": "image_token_index",
|
||
|
|
"boi_token_id": "boi_token_index",
|
||
|
|
"eoi_token_id": "eoi_token_index",
|
||
|
|
}
|
||
|
|
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"]
|