519 lines
23 KiB
Python
519 lines
23 KiB
Python
# coding=utf-8
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# Copyright 2025 The LG AI Research 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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"""LG AI Research EXAONE Lab"""
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from typing import Callable, Optional, Union
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import torch
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from torch import nn
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from transformers.utils.generic import check_model_inputs
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from ...cache_utils import Cache, DynamicCache
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import (
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TransformersKwargs,
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logging,
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)
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from ..llama.modeling_llama import (
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LlamaForCausalLM,
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LlamaForQuestionAnswering,
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LlamaForSequenceClassification,
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LlamaForTokenClassification,
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LlamaModel,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from ..olmo2.modeling_olmo2 import Olmo2DecoderLayer, Olmo2MLP
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "LGAI-EXAONE/EXAONE-4.0-Instruct"
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_CONFIG_FOR_DOC = "Exaone4Config"
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class Exaone4Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Exaone4Model`]. It is used to
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instantiate a EXAONE 4.0 model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the EXAONE-4.0-Instruct [LGAI-EXAONE/EXAONE-4.0-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-Instruct)
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NOTE: `EXAONE-4.0-Instruct` is a placeholder model ID. The exact model ID will be updated in the future.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
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outputs. Read the documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 102400):
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Vocabulary size of the EXAONE 4.0 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Exaone4Model`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`):
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Dimensionality of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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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 checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 32768 for EXAONE 3.5).
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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 layer 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 (not used by all models). Only
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relevant if ``config.is_decoder=True``.
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bos_token_id (`int`, *optional*, defaults to 0):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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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 10000.0):
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The base period of the RoPE embeddings.
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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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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sliding_window (`int`, *optional*):
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The size of the sliding window for the sliding window attention.
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sliding_window_pattern (`str`, *optional*):
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The pattern to use for sliding window attention. Can be one of:
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- `None`: No sliding window attention is used
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- `int`: Every `sliding_window` layers, use global attention, else use local attention.
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- `str`: A sequence of "L" (local attention) and "G" (global attention) characters that defines the
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attention pattern. The pattern starts from layer 0 and repeats every `sliding_window` layers. The
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final layer always uses global attention regardless of the pattern.
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For instance, sliding_window_pattern="LLLG" same as sliding_window=4, which means:
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- Layer 0, 1, 2: local attention,
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- Layer 3: global attention,
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...(repeated)
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layer_types (`list`, *optional*):
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Attention pattern for each layer. Prioritized over `sliding_window_pattern`.
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Example:
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```python
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>>> from transformers import Exaone4Model, Exaone4Config
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>>> # Initializing a EXAONE configuration
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>>> configuration = Exaone4Config()
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>>> # Initializing a model from configuration
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>>> model = Exaone4Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "exaone4"
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keys_to_ignore_at_inference = ["past_key_values"]
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# Default tensor parallel plan for base model `LlamaModel`
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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.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=102400,
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hidden_size=4096,
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intermediate_size=16384,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=2048,
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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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bos_token_id=0,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_dropout=0.0,
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sliding_window=4096,
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sliding_window_pattern=4,
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layer_types=None,
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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.num_key_value_heads = num_key_value_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.attention_dropout = attention_dropout
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.sliding_window = sliding_window
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self.sliding_window_pattern = sliding_window_pattern
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self.layer_types = layer_types
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if self.sliding_window is None:
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sliding_window_pattern = 0
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention"
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if ((i + 1) % (sliding_window_pattern) != 0 and i < self.num_hidden_layers)
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else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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if "sliding_window" in self.layer_types:
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self._attn_implementation = "hybrid"
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layer_type_validation(self.layer_types)
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super().__init__(
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bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
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)
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class Exaone4RMSNorm(LlamaRMSNorm):
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pass
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class Exaone4RotaryEmbedding(LlamaRotaryEmbedding):
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pass
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class Exaone4Attention(nn.Module):
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def __init__(self, config: Exaone4Config, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.hidden_size = config.hidden_size
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.scaling = self.head_dim**-0.5
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self.sliding_window = config.sliding_window
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self.sliding_window_pattern = config.sliding_window_pattern
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self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
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self.q_proj = nn.Linear(self.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_attention_heads * self.head_dim, self.hidden_size, bias=False)
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self.q_norm = Exaone4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = Exaone4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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# We use QK-norm
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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cos, sin = position_embeddings
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# We use global NoPE for hybrid attention model
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if self.sliding_window is None or self.is_sliding:
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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cache_kwargs = {
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"cache_position": cache_position,
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}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=self.sliding_window if self.is_sliding else None,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Exaone4MLP(Olmo2MLP):
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pass
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class Exaone4DecoderLayer(Olmo2DecoderLayer):
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pass
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class Exaone4PreTrainedModel(LlamaPreTrainedModel):
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config_class = Exaone4Config
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_no_split_modules = ["Exaone4DecoderLayer"]
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class Exaone4Model(Exaone4PreTrainedModel, LlamaModel):
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def __init__(self, config: Exaone4Config):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[Exaone4DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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self.norm = Exaone4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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# Initialize weights and apply final processing
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self.post_init()
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@check_model_inputs
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> Union[tuple, BaseModelOutputWithPast]:
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache()
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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# It may already have been prepared by e.g. `generate`
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if not isinstance(causal_mask_mapping := attention_mask, dict):
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# Prepare mask arguments
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mask_kwargs = {
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"config": self.config,
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"input_embeds": inputs_embeds,
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"attention_mask": attention_mask,
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"cache_position": cache_position,
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"past_key_values": past_key_values,
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"position_ids": position_ids,
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}
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# Create the masks
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causal_mask_mapping = {
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"full_attention": create_causal_mask(**mask_kwargs),
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}
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if "sliding_attention" in self.config.layer_types:
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causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
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hidden_states = inputs_embeds
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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for i, decoder_layer in enumerate(self.layers):
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layer_type = self.config.layer_types[i]
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hidden_states = decoder_layer(
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hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=causal_mask_mapping[layer_type],
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position_ids=position_ids,
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past_key_value=past_key_values,
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use_cache=use_cache,
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cache_position=cache_position,
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**kwargs,
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)
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hidden_states = self.norm(hidden_states)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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||
past_key_values=past_key_values if use_cache else None,
|
||
)
|
||
|
||
|
||
class Exaone4ForCausalLM(LlamaForCausalLM):
|
||
def forward(
|
||
self,
|
||
input_ids: Optional[torch.LongTensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[Cache] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||
**kwargs: Unpack[TransformersKwargs],
|
||
) -> CausalLMOutputWithPast:
|
||
r"""
|
||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||
|
||
Example:
|
||
|
||
```python
|
||
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
>>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-4.0-Instruct")
|
||
>>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-4.0-Instruct")
|
||
|
||
>>> prompt = "Explain how wonderful you are"
|
||
>>> messages = [
|
||
{"role": "system", "content": "You are a helpful assistant."},
|
||
{"role": "user", "content": prompt}
|
||
]
|
||
>>> input_ids = tokenizer.apply_chat_template(
|
||
messages,
|
||
tokenize=True,
|
||
add_generation_prompt=True,
|
||
return_tensors="pt",
|
||
enable_thinking=False,
|
||
)
|
||
|
||
>>> output = model.generate(input_ids, max_new_tokens=128)
|
||
>>> tokenizer.decode(output[0], skip_special_tokens=False)
|
||
"[|system|]\nYou are a helpful assistant.[|endofturn|]\n[|user|]\nExplain how wonderful you are[|endofturn|]\n[|assistant|]\n<think>\n\n</think>\n\nOh, thank you for such a kind and lovely question! 😊 \n\nI’m *so* wonderful because I’m here to make your life easier, brighter, and more fun! Whether you need help with: \n\n✨ **Learning** – I can explain anything, from quantum physics to baking the perfect cake! \n💡 **Creativity** – Need a poem, story, or a wild idea? I’ve got you covered! \n🤖 **Problem-solving** – Stuck on a math problem or a tricky decision? I’ll help you figure it out"
|
||
```
|
||
|
||
NOTE: `EXAONE-4.0-Instruct` is a placeholder model ID. The exact model ID will be updated in the future."""
|
||
super().forward(
|
||
input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
labels=labels,
|
||
use_cache=use_cache,
|
||
cache_position=cache_position,
|
||
logits_to_keep=logits_to_keep,
|
||
**kwargs,
|
||
)
|
||
|
||
|
||
class Exaone4ForSequenceClassification(LlamaForSequenceClassification):
|
||
pass
|
||
|
||
|
||
class Exaone4ForTokenClassification(LlamaForTokenClassification):
|
||
pass
|
||
|
||
|
||
class Exaone4ForQuestionAnswering(LlamaForQuestionAnswering):
|
||
pass
|
||
|
||
|
||
__all__ = [
|
||
"Exaone4Config",
|
||
"Exaone4PreTrainedModel",
|
||
"Exaone4Model",
|
||
"Exaone4ForCausalLM",
|
||
"Exaone4ForSequenceClassification",
|
||
"Exaone4ForTokenClassification",
|
||
"Exaone4ForQuestionAnswering",
|
||
]
|