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296 lines
12 KiB
296 lines
12 KiB
import torch
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from torch import nn
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from ...cache_utils import Cache, DynamicCache
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from ...configuration_utils import PreTrainedConfig
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_outputs import BaseModelOutputWithPast
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from ...modeling_rope_utils import RopeParameters
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring
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from ...utils.generic import check_model_inputs
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from ..mistral.configuration_mistral import MistralConfig
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from ..qwen2.modeling_qwen2 import (
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Qwen2Attention,
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Qwen2DecoderLayer,
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Qwen2ForCausalLM,
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Qwen2ForQuestionAnswering,
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Qwen2ForSequenceClassification,
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Qwen2ForTokenClassification,
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Qwen2MLP,
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Qwen2Model,
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Qwen2PreTrainedModel,
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Qwen2RMSNorm,
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Qwen2RotaryEmbedding,
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)
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class MinistralConfig(MistralConfig, PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MinistralModel`]. It is used to instantiate an
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Ministral 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 Ministral-8B-Instruct-2410.
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[mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)
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[mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)
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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 32000):
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Vocabulary size of the Ministral model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MinistralModel`]
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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 14336):
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Dimension 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 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
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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 `8`.
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head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
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The attention head dimension.
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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 `4096*32`):
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The maximum sequence length that this model might ever be used with. Ministral's sliding window attention
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allows sequence of up to 4096*32 tokens.
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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-06):
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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 (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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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-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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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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sliding_window (`int`, *optional*, defaults to 4096):
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Sliding window attention window size. If not specified, will default to `4096`.
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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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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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```python
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>>> from transformers import MinistralModel, MinistralConfig
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>>> # Initializing a Ministral 8B style configuration
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>>> configuration = MinistralConfig()
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>>> # Initializing a model from the Ministral 8B style configuration
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>>> model = MinistralModel(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 = "ministral"
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def __init__(
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self,
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vocab_size: int | None = 32000,
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hidden_size: int | None = 4096,
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intermediate_size: int | None = 14336,
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num_hidden_layers: int | None = 32,
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = 8,
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head_dim: int | None = None,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 4096 * 32,
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initializer_range: float | None = 0.02,
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rms_norm_eps: float | None = 1e-6,
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use_cache: bool | None = True,
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pad_token_id: int | None = None,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 2,
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tie_word_embeddings: bool | None = False,
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rope_parameters: RopeParameters | None = None,
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sliding_window: int | None = 4096,
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attention_dropout: float | None = 0.0,
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layer_types: list[str] | None = None,
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**kwargs,
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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.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_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.sliding_window = sliding_window
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self.head_dim = head_dim
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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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.layer_types = layer_types
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention" if self.sliding_window is not None else "full_attention"
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] * num_hidden_layers
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self.rope_parameters = rope_parameters
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PreTrainedConfig.__init__(self, **kwargs)
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class MinistralMLP(Qwen2MLP):
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pass
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class MinistralAttention(Qwen2Attention):
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def __init__(self, config, layer_idx: int):
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super().__init__(config, layer_idx)
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# Match Mistral: q/k/v do not have bias
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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class MinistralRMSNorm(Qwen2RMSNorm):
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pass
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class MinistralDecoderLayer(Qwen2DecoderLayer):
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pass
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class MinistralPreTrainedModel(Qwen2PreTrainedModel):
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pass
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class MinistralRotaryEmbedding(Qwen2RotaryEmbedding):
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pass
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class MinistralModel(Qwen2Model):
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def __init__(self, config: MinistralConfig):
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super().__init__(config)
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del self.has_sliding_layers
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@check_model_inputs
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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use_cache: bool | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> 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(config=self.config)
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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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"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
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}
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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 decoder_layer in self.layers[: self.config.num_hidden_layers]:
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hidden_states = decoder_layer(
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hidden_states,
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attention_mask=causal_mask_mapping[decoder_layer.attention_type],
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position_ids=position_ids,
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past_key_values=past_key_values,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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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,
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)
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class MinistralForCausalLM(Qwen2ForCausalLM):
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pass
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class MinistralForSequenceClassification(Qwen2ForSequenceClassification):
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pass
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class MinistralForTokenClassification(Qwen2ForTokenClassification):
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pass
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class MinistralForQuestionAnswering(Qwen2ForQuestionAnswering):
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pass
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__all__ = [
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"MinistralConfig",
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"MinistralPreTrainedModel",
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"MinistralModel",
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"MinistralForCausalLM",
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"MinistralForSequenceClassification",
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"MinistralForTokenClassification",
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"MinistralForQuestionAnswering",
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]
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