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430 lines
19 KiB
430 lines
19 KiB
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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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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"""Blt model configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class BltLocalEncoderConfig(PreTrainedConfig):
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"""
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Configuration class for the Blt Local Encoder component.
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"""
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model_type = "blt_local_encoder"
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default_theta = 500000.0
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def __init__(
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self,
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vocab_size: int | None = 260,
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cross_attn_all_layers: bool | None = False,
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cross_attn_k: int | None = 2,
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hidden_size_global: int | None = 2048,
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hidden_size: int | None = 1024,
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num_attention_heads: int | None = 16,
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num_key_value_heads: int | None = None,
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num_hidden_layers: int | None = 1,
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rms_norm_eps: float | None = 1e-5,
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dropout: float | None = 0.0,
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max_position_embeddings: int | None = 24576,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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hidden_act: str | None = "silu",
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intermediate_size: int | None = 2816,
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initializer_range: float | None = 0.02,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.cross_attn_all_layers = cross_attn_all_layers
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self.cross_attn_k = cross_attn_k
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self.hidden_size_global = hidden_size_global
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads or num_attention_heads
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self.head_dim = hidden_size // num_attention_heads
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self.intermediate_size = intermediate_size or int(8 * hidden_size / 3)
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self.num_hidden_layers = num_hidden_layers
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self.rms_norm_eps = rms_norm_eps
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self.dropout = dropout
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self.max_position_embeddings = max_position_embeddings
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rope_parameters = rope_parameters
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# Remove tie_word_embeddings from kwargs to avoid duplicate parameter error
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kwargs.pop("tie_word_embeddings", None)
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super().__init__(**kwargs, tie_word_embeddings=False)
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class BltLocalDecoderConfig(PreTrainedConfig):
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"""
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Configuration class for the Blt Local Decoder component.
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"""
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model_type = "blt_local_decoder"
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default_theta = 500000.0
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def __init__(
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self,
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vocab_size: int | None = 260,
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cross_attn_all_layers: bool | None = True,
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cross_attn_k: int | None = 2,
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hidden_size_global: int | None = 2048,
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hidden_size: int | None = 1024,
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num_attention_heads: int | None = 16,
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num_key_value_heads: int | None = None,
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num_hidden_layers: int | None = 9,
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rms_norm_eps: float | None = 1e-5,
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dropout: float | None = 0.0,
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max_position_embeddings: int | None = 24576,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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hidden_act: str | None = "silu",
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intermediate_size: int | None = 2816,
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initializer_range: float | None = 0.02,
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pad_token_id: int | None = None,
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bos_token_id: int | None = None,
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eos_token_id: int | None = None,
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tie_word_embeddings: bool | None = False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.cross_attn_all_layers = cross_attn_all_layers
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self.cross_attn_k = cross_attn_k
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self.hidden_size_global = hidden_size_global
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads or num_attention_heads
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self.head_dim = hidden_size // num_attention_heads
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self.intermediate_size = intermediate_size or int(8 * hidden_size / 3)
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self.num_hidden_layers = num_hidden_layers
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self.rms_norm_eps = rms_norm_eps
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self.dropout = dropout
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self.max_position_embeddings = max_position_embeddings
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.tie_word_embeddings = False # Force-set to False for BC
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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class BltGlobalTransformerConfig(PreTrainedConfig):
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"""
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Configuration class for the Blt Global Transformer component.
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"""
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model_type = "blt_global_transformer"
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default_theta = 500000.0
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def __init__(
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self,
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hidden_size: int | None = 2048,
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num_attention_heads: int | None = 16,
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num_key_value_heads: int | None = None,
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num_hidden_layers: int | None = 25,
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rms_norm_eps: float | None = 1e-5,
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dropout: float | None = 0.0,
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max_position_embeddings: int | None = 4096,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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hidden_act: str | None = "silu",
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intermediate_size: int | None = 5632,
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initializer_range: float | None = 0.02,
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tie_word_embeddings: bool | None = False,
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**kwargs,
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):
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads or num_attention_heads
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self.head_dim = hidden_size // num_attention_heads
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self.intermediate_size = intermediate_size or int(8 * hidden_size / 3)
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self.num_hidden_layers = num_hidden_layers
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self.rms_norm_eps = rms_norm_eps
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self.dropout = dropout
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self.max_position_embeddings = max_position_embeddings
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.tie_word_embeddings = False
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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class BltPatcherConfig(PreTrainedConfig):
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r"""
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Configuration class for the Blt Patcher/Entropy model component.
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Args:
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vocab_size (`int`, *optional*, defaults to 260):
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Vocabulary size of the Blt patcher model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling the patcher model.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimension of the hidden representations.
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num_hidden_layers (`int`, *optional*, defaults to 14):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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max_position_embeddings (`int`, *optional*, defaults to 8192):
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The maximum sequence length that this model might ever be used with.
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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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dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimension of the MLP representations.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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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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"""
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model_type = "blt_patcher"
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def __init__(
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self,
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vocab_size: int | None = 260,
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hidden_size: int | None = 768,
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num_hidden_layers: int | None = 14,
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num_attention_heads: int | None = 12,
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num_key_value_heads: int | None = None,
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max_position_embeddings: int | None = 8192,
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rms_norm_eps: float | None = 1e-5,
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dropout: float | None = 0.0,
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intermediate_size: int | None = 2048,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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initializer_range: float | None = 0.02,
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tie_word_embeddings: bool | None = False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.head_dim = hidden_size // num_attention_heads
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self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.rms_norm_eps = rms_norm_eps
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self.dropout = dropout
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self.hidden_act = "silu" # Blt uses silu activation
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self.intermediate_size = intermediate_size or int(8 * self.hidden_size / 3)
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self.initializer_range = initializer_range
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self.rope_parameters = rope_parameters
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self.tie_word_embeddings = False
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super().__init__(**kwargs)
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class BltConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BltModel`]. It is used to instantiate a
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Blt model according to the specified arguments, defining the model architecture.
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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 260):
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Vocabulary size of the Blt model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`BltModel`].
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with.
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patch_in_forward (`bool`, *optional*, defaults to `True`):
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Whether to perform patching during the forward pass.
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patch_size (`int`, *optional*, defaults to 4):
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Size of the patches used in the patching mechanism.
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patching_mode (`str`, *optional*, defaults to `"entropy"`):
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The mode used for patching, such as entropy-based patching.
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patching_threshold (`float`, *optional*, defaults to 1.34):
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Threshold value used for determining when to apply patches.
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patching_batch_size (`int`, *optional*, defaults to 1):
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Batch size used during the patching process.
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max_patch_length (`int`, *optional*):
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Maximum length of patches that can be generated.
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cross_attn_k (`int`, *optional*, defaults to 2):
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Number of cross-attention heads used in the model.
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encoder_hash_byte_group_size (`list`, *optional*):
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List of byte group sizes used in the encoder hash function.
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encoder_hash_byte_group_vocab (`int`, *optional*, defaults to 500002):
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Vocabulary size for the encoder hash byte groups.
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encoder_hash_byte_group_nb_functions (`int`, *optional*, defaults to 1):
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Number of hash functions used in the encoder byte grouping.
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patcher_config (`BltPatcherConfig`, *optional*):
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Configuration for the patcher component of the model.
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encoder_config (`BltLocalEncoderConfig`, *optional*):
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Configuration for the local encoder component of the model.
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decoder_config (`BltLocalDecoderConfig`, *optional*):
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Configuration for the local decoder component of the model.
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global_config (`BltGlobalTransformerConfig`, *optional*):
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Configuration for the global transformer component of the model.
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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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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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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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```python
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>>> from transformers import BltModel, BltConfig
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>>> # Initializing a Blt configuration
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>>> configuration = BltConfig()
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>>> # Initializing a model from the configuration
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>>> model = BltModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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Checkpoint: [facebook/blt](https://huggingface.co/facebook/blt)
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"""
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model_type = "blt"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 500000.0
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sub_configs = {
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"patcher_config": BltPatcherConfig,
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"encoder_config": BltLocalEncoderConfig,
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"decoder_config": BltLocalDecoderConfig,
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"global_config": BltGlobalTransformerConfig,
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}
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def __init__(
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self,
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vocab_size: int | None = 260,
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max_position_embeddings: int | None = 4096,
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patch_in_forward: bool | None = True,
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patch_size: int | None = 4,
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patching_mode: str | None = "entropy",
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patching_threshold: float | None = 1.335442066192627,
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patching_batch_size: int | None = 1,
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max_patch_length: int | None = None,
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cross_attn_k: int | None = 2,
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encoder_hash_byte_group_size: int | None = None,
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encoder_hash_byte_group_vocab: int | None = 500002,
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encoder_hash_byte_group_nb_functions: int | None = 1,
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patcher_config: dict | None = None,
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encoder_config: dict | None = None,
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decoder_config: dict | None = None,
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global_config: dict | None = None,
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tie_word_embeddings: bool | None = False,
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pad_token_id: int | None = None,
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bos_token_id: int | None = None,
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eos_token_id: int | None = None,
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initializer_range: float | None = 0.02,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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**kwargs,
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):
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# Basic model configuration
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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# Patching configuration
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self.patch_in_forward = patch_in_forward
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self.patch_size = patch_size
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self.patching_mode = patching_mode
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self.patching_threshold = patching_threshold
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self.patching_batch_size = patching_batch_size
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self.max_patch_length = max_patch_length
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self.patching_device = kwargs.get("patching_device", "cuda")
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self.realtime_patching = kwargs.get("realtime_patching", True)
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self.patching_threshold_add = kwargs.get("patching_threshold_add")
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self.monotonicity = kwargs.get("monotonicity", False)
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# Cross attention configurations
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self.cross_attn_k = cross_attn_k
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# Encoder configurations
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self.encoder_hash_byte_group_size = encoder_hash_byte_group_size or [3, 4, 5, 6, 7, 8]
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self.encoder_hash_byte_group_vocab = encoder_hash_byte_group_vocab
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self.encoder_hash_byte_group_nb_functions = encoder_hash_byte_group_nb_functions
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# Initialize component configurations
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if patcher_config is None:
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self.patcher_config = BltPatcherConfig(initializer_range=initializer_range)
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logger.info("patcher_config is None, using default Blt patcher config")
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elif isinstance(patcher_config, dict):
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patcher_config.setdefault("initializer_range", initializer_range)
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self.patcher_config = BltPatcherConfig(**patcher_config)
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elif isinstance(patcher_config, BltPatcherConfig):
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self.patcher_config = patcher_config
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if encoder_config is None:
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self.encoder_config = BltLocalEncoderConfig(initializer_range=initializer_range)
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logger.info("encoder_config is None, using default Blt encoder config")
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elif isinstance(encoder_config, dict):
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encoder_config.setdefault("initializer_range", initializer_range)
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self.encoder_config = BltLocalEncoderConfig(**encoder_config)
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elif isinstance(encoder_config, BltLocalEncoderConfig):
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self.encoder_config = encoder_config
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if decoder_config is None:
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self.decoder_config = BltLocalDecoderConfig(initializer_range=initializer_range)
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logger.info("decoder_config is None, using default Blt decoder config")
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elif isinstance(decoder_config, dict):
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decoder_config.setdefault("initializer_range", initializer_range)
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self.decoder_config = BltLocalDecoderConfig(**decoder_config)
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elif isinstance(decoder_config, BltLocalDecoderConfig):
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self.decoder_config = decoder_config
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if global_config is None:
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self.global_config = BltGlobalTransformerConfig(initializer_range=initializer_range)
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logger.info("global_config is None, using default Blt global config")
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elif isinstance(global_config, dict):
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global_config.setdefault("initializer_range", initializer_range)
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self.global_config = BltGlobalTransformerConfig(**global_config)
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elif isinstance(global_config, BltGlobalTransformerConfig):
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self.global_config = global_config
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# Determine if token embedding projection is needed based on dimension mismatch (7b)
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encoder_cross_output_size = self.encoder_config.hidden_size * self.cross_attn_k
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self.global_config.encoder_cross_output_size = (
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encoder_cross_output_size if encoder_cross_output_size != self.global_config.hidden_size else None
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)
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.tie_word_embeddings = tie_word_embeddings
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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__all__ = [
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"BltConfig",
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"BltPatcherConfig",
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"BltLocalEncoderConfig",
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"BltLocalDecoderConfig",
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"BltGlobalTransformerConfig",
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]
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