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Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`MixtralConfig`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate N)Z fill_valuerYr\rrVr#Zuse_sliding_windowTr)rXrbr r fullr\rrnZget_text_configrrrr Z bitwise_or_rcloner`riZ masked_fill)rrprrYrror(rrr Zdiagonal_attend_maskZ text_configZsliding_attend_maskZ mask_lengthZ padding_maskr;r;r<r s@!  $ 6  zBMixtralModel._prepare_4d_causal_attention_mask_with_cache_position) NNNNNNNNNN)F)rArBrCr$r-rrr rrrbrrwrrrrrrr@rr r staticmethodrrYr rDr;r;r9r<rs      x  Vrc@s eZdZdS)KwargsForCausalLMN)rArBrCr;r;r;r<rs rr] gate_logitsrKcs|dus t|ts dSt|tr#|djtjfdd|Ddd}tjjj|dd}tj||dd\}}tjj ||}|durStj | dd} tj |dd} ng|j \} } |j d| | } |dddddddf | | | ||fd||}tj| |ddtj|dd} |ddddddf | | | |fd|}tj||ddtj|dd} t| | d}||S)a Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: gate_logits: Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts: Number of experts top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. 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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 AutoTokenizer, MixtralForCausalLM >>> model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```N) rrrrrrrrrr)lossaux_lossrrr>rrqr;)r(rrrrrrrslicer loss_functionrrrqrKrLrrir\rrr>r)r8rrrrrr%rrrrrr&rrr>Z slice_indicesrr'r(r;r;r<r@gsX) zMixtralForCausalLM.forward) NNNNNNNNNNNr)rArBrCZ_tied_weights_keysZ_tp_planZ_pp_planr-rrrr r#r$r rrrbrrwrrrrrrrrr@rDr;r;r9r<rCsl        ra The Mixtral Model transformer with a sequence classification head on top (linear layer). [`MixtralForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. 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If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). rrrrrrrNrr#z=Cannot handle batch sizes > 1 if no padding token is defined.rV)r\rYz will not detect padding tokens in `inputs_embeds`. 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Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. )rrrrrrr#rVrZN)r' start_logits end_logitsr>r) rrr8splitZsqueezerr*rr>r)r8rrrrrr9r:rrrrr6rr;r<r'r;r;r<r@s0 z#MixtralForQuestionAnswering.forwardr2)rArBrCrr-rrr rrrbrrwrr rrrrr@rDr;r;r9r<r7rsJ    r7)rr7rrr,r3)Nr#)r)Nr]N)Q functoolsrtypingrrrrrrbZtorch.nn.functionalrrjrdZ activationsr Z cache_utilsr r r rZ generationrZ integrationsrZmodeling_attn_mask_utilsrZmodeling_flash_attention_utilsrZmodeling_outputsrrrrrrZmodeling_rope_utilsrrZmodeling_utilsrrZprocessing_utilsrutilsrrr r!r"Zconfiguration_mixtralr$Z!torch.nn.attention.flex_attentionr%Zintegrations.flex_attentionr&Z get_loggerrArModuler'rErxrrrwrrrfrrrrrrrrrr,r3r7__all__r;r;r;r<s             C  DQ")  RVF I