| from transformers import PretrainedConfig |
| import copy |
|
|
| class Florence2VisionConfig(PretrainedConfig): |
|
|
| model_type = "florence2_vision" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| drop_path_rate=0.1, |
| patch_size=[7, 3, 3, 3], |
| patch_stride=[4, 2, 2, 2], |
| patch_padding=[3, 1, 1, 1], |
| patch_prenorm=[False, True, True, True], |
| enable_checkpoint=False, |
| dim_embed=[256, 512, 1024, 2048], |
| num_heads=[8, 16, 32, 64], |
| num_groups=[8, 16, 32, 64], |
| depths=[1, 1, 9, 1], |
| window_size=12, |
| projection_dim=1024, |
| visual_temporal_embedding=None, |
| image_pos_embed=None, |
| image_feature_source=["spatial_avg_pool", "temporal_avg_pool"], |
| **kwargs, |
| ): |
| self.drop_path_rate = drop_path_rate |
| self.patch_size = patch_size |
| self.patch_stride = patch_stride |
| self.patch_padding = patch_padding |
| self.patch_prenorm = patch_prenorm |
| self.enable_checkpoint = enable_checkpoint |
| self.dim_embed = dim_embed |
| self.num_heads = num_heads |
| self.num_groups = num_groups |
| self.depths = depths |
| self.window_size = window_size |
| self.projection_dim = projection_dim |
| self.visual_temporal_embedding = visual_temporal_embedding |
| self.image_pos_embed = image_pos_embed |
| self.image_feature_source = image_feature_source |
|
|
| super().__init__(**kwargs) |
|
|
|
|
|
|
| class Gemma2Config(PretrainedConfig): |
|
|
| model_type = "gemma2" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=256000, |
| hidden_size=3072, |
| intermediate_size=24576, |
| num_hidden_layers=28, |
| num_attention_heads=16, |
| num_key_value_heads=16, |
| head_dim=256, |
| hidden_activation="gelu_pytorch_tanh", |
| max_position_embeddings=8192, |
| initializer_range=0.02, |
| rms_norm_eps=1e-6, |
| use_cache=True, |
| pad_token_id=0, |
| eos_token_id=1, |
| bos_token_id=2, |
| tie_word_embeddings=True, |
| rope_theta=10000.0, |
| attention_bias=False, |
| attention_dropout=0.0, |
| final_logit_softcapping=30.0, |
| attn_logit_softcapping=50.0, |
| query_pre_attn_scalar=224, |
| sliding_window=4096, |
| **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.head_dim = head_dim |
| self.num_key_value_heads = num_key_value_heads |
| self.hidden_activation = hidden_activation |
| self.initializer_range = initializer_range |
| self.rms_norm_eps = rms_norm_eps |
| self.use_cache = use_cache |
| self.rope_theta = rope_theta |
| self.attention_bias = attention_bias |
| self.attention_dropout = attention_dropout |
| self.attn_logit_softcapping = attn_logit_softcapping |
|
|
| 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.final_logit_softcapping = final_logit_softcapping |
| self.query_pre_attn_scalar = query_pre_attn_scalar |
| self.sliding_window = sliding_window |
| self.cache_implementation = "hybrid" |
|
|
|
|
|
|
| class FeynModelConfig(PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`FeynModel`]. It is used to instantiate a FeynModel |
| 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 Gemma2-2B + Florence-2-Base + FeynModel V0.1.0. |
| ```python |
| >>> from transformers import FeynModel, FeynModelConfig |
| >>> # Initializing a FeynModel style configuration |
| >>> configuration = FeynModelConfig() |
| >>> # Initializing a model |
| >>> model = FeynModel(configuration) |
| >>> # Accessing the model configuration |
| >>> configuration = model.config |
| ```""" |
|
|
| |
| |
| model_type = "FeynModel" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vision_config=None, |
| text_config=None, |
| ignore_index=-100, |
| vocab_size=256000, |
| projection_dim=1024, |
| **kwargs, |
| ): |
| self.ignore_index = ignore_index |
| self.vocab_size = vocab_size |
| self.projection_dim = projection_dim |
| self.vision_config = vision_config |
| self.vocab_size = self.vocab_size |
|
|
| self.text_config = text_config |
| |
| |
| |
| if text_config is not None: |
| for attr, value in text_config.items(): |
| setattr(self, attr, value) |
|
|
|
|
| super().__init__(**kwargs) |
|
|
|
|
|
|
| |
