o
    ia                     @   s  d dl mZmZmZ d dlZd dlmZ ddlmZ ddlm	Z	m
Z
 ddlmZ ddlmZ dd	lmZ dd
lmZ ddlmZmZ ddlmZmZ ddlmZmZ ddlmZ ddlmZm Z m!Z!m"Z" ddl#m$Z$ ddl%m&Z& ddl'm(Z( e")e*Z+dd Z,d:ddZ-dej.de/dej.fddZ0	d;dej1dej.d ej.d!ej.d"eej. d#e2d$e2d%ee fd&d'Z3G d(d) d)ej1Z4ed*G d+d, d,ej1Z5G d-d. d.ej1Z6G d/d0 d0eZ7e G d1d2 d2eZ8G d3d4 d4ej1Z9e G d5d6 d6e8Z:e G d7d8 d8e8eZ;g d9Z<dS )<    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuplelogging)deprecate_kwarg)check_model_inputs   )GraniteConfigc                 C   sH   | dd| j d d f }| d| j d d df }tj| |fddS )z*Rotates half the hidden dims of the input..N   dim)shapetorchcat)xx1x2 r'   i/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/transformers/models/granite/modeling_granite.pyrotate_half.   s   r)   c                 C   sD   | |}| |}| | t| |  }|| t||  }||fS )a  Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    )	unsqueezer)   )qkcossinposition_idsunsqueeze_dimq_embedk_embedr'   r'   r(   apply_rotary_pos_emb5   s
   

r3   hidden_statesn_repreturnc                 C   s^   | j \}}}}|dkr| S | dddddddddf |||||} | ||| ||S )z
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    r   N)r!   expandreshape)r4   r5   batchnum_key_value_headsslenhead_dimr'   r'   r(   	repeat_kvP   s
   0r=           modulequerykeyvalueattention_maskscalingdropoutkwargsc                 K   s   t || j}t || j}	t||dd| }
|d ur3|d d d d d d d |jd f }|
| }
tjj|
dtj	d
|j}
tjj|
|| jd}
t|
|	}|dd }||
fS )Nr   r   r   )r    dtype)ptrainingr   )r=   num_key_value_groupsr"   matmul	transposer!   r   
functionalsoftmaxfloat32torH   rE   rJ   
contiguous)r?   r@   rA   rB   rC   rD   rE   rF   
key_statesvalue_statesattn_weightscausal_maskattn_outputr'   r'   r(   eager_attention_forward\   s   
&rX   c                       s   e Zd ZdZddedee f fddZeddd	d
		dde	j
dee	j
e	j
f dee	j
 dee dee	j dee dee	j
e	j
f fddZ  ZS )GraniteAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNconfig	layer_idxc                    s   t    || _|| _t|d|j|j | _|j|j | _	|j
| _|j| _d| _tj|j|j| j |jd| _tj|j|j| j |jd| _tj|j|j| j |jd| _tj|j| j |j|jd| _d S )Nr<   Tbias)super__init__rZ   r[   getattrhidden_sizenum_attention_headsr<   r:   rK   attention_multiplierrD   attention_dropout	is_causalr   Linearattention_biasq_projk_projv_projo_projselfrZ   r[   	__class__r'   r(   r_   y   s(   
zGraniteAttention.__init__past_key_valuepast_key_values4.58new_nameversionr4   position_embeddingsrC   cache_positionrF   r6   c                 K   s$  |j d d }g |d| jR }| ||dd}	| ||dd}
| ||dd}|\}}t|	|
||\}	}
|d urW|||d}||
|| j	|\}
}t
}| jjdkret| jj }|| |	|
||f| jsqdn| j| jd|\}}|jg |dR   }| |}||fS )Nr   r   r   )r.   r-   rw   eagerr>   )rE   rD   )r!   r<   rh   viewrM   ri   rj   r3   updater[   rX   rZ   _attn_implementationr   rJ   rd   rD   r8   rR   rk   )rm   r4   rv   rC   rq   rw   rF   input_shapehidden_shapequery_statesrS   rT   r-   r.   cache_kwargsattention_interfacerW   rU   r'   r'   r(   forward   s8   


zGraniteAttention.forwardN)NN)__name__
__module____qualname____doc__r   r   intr_   r   r"   Tensortupler   
LongTensorr   r   r   __classcell__r'   r'   rn   r(   rY   v   s*    rY   RMSNormc                       s.   e Zd Zd fdd	Zdd Zdd Z  ZS )	GraniteRMSNormư>c                    s&   t    tt|| _|| _dS )z=
        GraniteRMSNorm is equivalent to T5LayerNorm
        N)r^   r_   r   	Parameterr"   onesweightvariance_epsilon)rm   ra   epsrn   r'   r(   r_      s   

zGraniteRMSNorm.__init__c                 C   sJ   |j }|tj}|djddd}|t|| j  }| j|| S )Nr   r   T)keepdim)	rH   rQ   r"   rP   powmeanrsqrtr   r   )rm   r4   input_dtypevariancer'   r'   r(   r      s
   zGraniteRMSNorm.forwardc                 C   s   t | jj d| j S )Nz, eps=)r   r   r!   r   )rm   r'   r'   r(   
extra_repr   s   zGraniteRMSNorm.extra_repr)r   )r   r   r   r_   r   r   r   r'   r'   rn   r(   r      s    r   c                       s$   e Zd Z fddZdd Z  ZS )
GraniteMLPc                    sx   t    || _|j| _|j| _tj| j| j|jd| _tj| j| j|jd| _	tj| j| j|jd| _
t|j | _d S )Nr\   )r^   r_   rZ   ra   intermediate_sizer   rf   mlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnrm   rZ   rn   r'   r(   r_      s   
zGraniteMLP.__init__c                 C   s$   |  | | || | }|S r   )r   r   r   r   )rm   r$   r   r'   r'   r(   r      s    zGraniteMLP.forward)r   r   r   r_   r   r   r'   r'   rn   r(   r      s    
r   c                       s   e Zd Zdedef fddZedddd							
	
				ddejde	ej de	ej
 de	e de	e de	e de	ej
 de	eejejf  deeje	eejejf  f fddZ  ZS )GraniteDecoderLayerrZ   r[   c                    sZ   t    |j| _t||d| _t|| _t|j|jd| _	t|j|jd| _
|j| _d S )N)rZ   r[   r   )r^   r_   ra   rY   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormresidual_multiplierrl   rn   r'   r(   r_      s   

zGraniteDecoderLayer.__init__rp   rq   rr   rs   NFr4   rC   r/   output_attentions	use_cacherw   rv   r6   c	                 K   s   |}
|  |}| jd||||||||d|	\}}|
|| j  }|}
| |}| |}|
|| j  }|f}|r>||f7 }|S )a  
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_values (`Cache`, *optional*): cached past key and value projection states
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence
            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                with `head_dim` being the embedding dimension of each attention head.
            kwargs (`dict`, *optional*):
                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
                into the model
        )r4   rC   r/   rq   r   r   rw   rv   Nr'   )r   r   r   r   r   )rm   r4   rC   r/   rq   r   r   rw   rv   rF   residualself_attn_weightsoutputsr'   r'   r(   r      s.   #
	



zGraniteDecoderLayer.forward)NNNFFNN)r   r   r   r   r   r_   r   r"   r   r   r   r   boolr   FloatTensorr   r   r'   r'   rn   r(   r      s:    
	r   c                   @   sH   e Zd ZU eed< dZdZdgZdgZdZ	dZ
dZdZdZeedZdS )GranitePreTrainedModelrZ   modelTr   rq   )r4   
attentionsN)r   r   r   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   rY   _can_record_outputsr'   r'   r'   r(   r   0  s   
 
r   c                       sD   e Zd ZU ejed< ddef fddZe e	dd Z
  ZS )	GraniteRotaryEmbeddinginv_freqNrZ   c                    s   t    t|drt|jtr|jd|jd| _nd| _|j| _	|j| _
|| _t| j | _| | j|\}| _| jd|dd | j| _d S )Nrope_scaling	rope_typetypedefaultr   F)
persistent)r^   r_   hasattr
isinstancer   dictgetr   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrZ   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)rm   rZ   devicer   rn   r'   r(   r_   F  s   
zGraniteRotaryEmbedding.__init__c           
      C   s   | j d d d d f  |jd dd|j}|d d d d d f  }t|jjtr6|jjdkr6|jjnd}t	j
|dd+ | |  dd}t	j||fdd	}| | j }| | j }	W d    n1 smw   Y  |j|jd
|	j|jd
fS )Nr   r   r   mpscpuF)device_typeenabledr   r   )rH   )r   floatr7   r!   rQ   r   r   r   strr"   autocastrM   r#   r-   r   r.   rH   )
rm   r$   r/   inv_freq_expandedposition_ids_expandedr   freqsembr-   r.   r'   r'   r(   r   W  s   0&zGraniteRotaryEmbedding.forwardr   )r   r   r   r"   r   r   r   r_   no_gradr   r   r   r'   r'   rn   r(   r   C  s   
 
r   c                       s   e Zd Zdef fddZee									ddeej	 deej
 deej	 dee d	eej d
ee dee dee deej	 dee defddZ  ZS )GraniteModelrZ   c                    s   t     j| _ j| _t j j| j| _t	 fddt
 jD | _t j jd| _t d| _d| _ j| _|   d S )Nc                    s   g | ]}t  |qS r'   )r   ).0r[   rZ   r'   r(   
<listcomp>p  s    z)GraniteModel.__init__.<locals>.<listcomp>r   r   F)r^   r_   pad_token_idpadding_idx
vocab_sizer   	Embeddingra   embed_tokens
ModuleListrangenum_hidden_layerslayersr   r   normr   
rotary_embgradient_checkpointingembedding_multiplier	post_initr   rn   r   r(   r_   i  s   zGraniteModel.__init__N	input_idsrC   r/   rq   inputs_embedsr   r   output_hidden_statesrw   rF   r6   c
                 K   s  |d ur|n| j j}|d ur|n| j j}|d ur|n| j j}|d u |d uA r*td| jr9| jr9|r9td d}|d u rB| 	|}|| j
 }|rS|d u rSt| j d}|	d u ro|d ur_| nd}tj|||jd  |jd}	|d u rx|	d}t| j |||	||d}|}| ||}|rd	nd }|rd	nd }| jd | j j D ]&}|r||f7 }||f||||||	|d
|
}|d }|r||d f7 }q| |}|r||f7 }t||r|nd ||dS )Nz:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.Fr   r   r   )r   )rZ   input_embedsrC   rw   rq   r/   r'   )rC   r/   rq   r   r   rw   rv   )last_hidden_staterq   r4   r   )rZ   r   r   r   
ValueErrorr   rJ   loggerwarning_oncer   r   r	   get_seq_lengthr"   aranger!   r   r*   r   r   r   r   r   r   )rm   r   rC   r/   rq   r   r   r   r   rw   rF   past_seen_tokensrV   r4   rv   all_hidden_statesall_self_attnsdecoder_layerlayer_outputsr'   r'   r(   r   z  s   


	
	


zGraniteModel.forward)	NNNNNNNNN)r   r   r   r   r_   r   r   r   r"   r   r   r   r   r   r   r   r   r   r   r'   r'   rn   r(   r   g  sH    	
r   c                       s   e Zd ZdgZddiZddgdgfiZ fddZee												dd
e	e
j de	e
j de	e
j de	eeee
j f  de	e
j de	e
j de	e de	e de	e de	e
j deee
jf dee defddZ  ZS )GraniteForCausalLMzlm_head.weightlm_headcolwise_repr4   logitsc                    s@   t  | t|| _|j| _tj|j|jdd| _| 	  d S )NFr\   )
r^   r_   r   r   r   r   rf   ra   r  r   r   rn   r'   r(   r_     s
   
zGraniteForCausalLM.__init__Nr   r   rC   r/   rq   r   labelsr   r   r   rw   logits_to_keeprF   r6   c                 K   s   |dur|n| j j}|	dur|	n| j j}	| jd||||||||	|
d	|}|j}t|tr4t| dn|}| |dd|ddf }|| j j	 }d}|dur^| j
d||| j jd|}t|||j|j|jdS )a  
        Example:

        ```python
        >>> from transformers import AutoTokenizer, GraniteForCausalLM

        >>> model = GraniteForCausalLM.from_pretrained("meta-granite/Granite-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-granite/Granite-2-7b-hf")

        >>> 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)	r   rC   r/   rq   r   r   r   r   rw   )r  r  r   )lossr  rq   r4   r   r'   )rZ   r   r   r   r   r   r   slicer  logits_scalingloss_functionr   r   rq   r4   r   )rm   r   rC   r/   rq   r   r  r   r   r   rw   r  rF   r   r4   slice_indicesr  r  r'   r'   r(   r     s<   "
zGraniteForCausalLM.forward)NNNNNNNNNNr   )r   r   r   _tied_weights_keys_tp_plan_pp_planr_   r   r   r   r"   r   r   r   r   listr   r   r   r   r   r   r   r   r'   r'   rn   r(   r    sZ    		
r  )r  r   r   )Nr   )r>   )=typingr   r   r   r"   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   masking_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   r   utils.deprecationr   utils.genericr   configuration_graniter   
get_loggerr   r   r)   r3   r   r   r=   Moduler   rX   rY   r   r   r   r   r   r   r  __all__r'   r'   r'   r(   <module>   sh   


GN$vV