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Use DynamicCache.r�)r;r�r4r5r�rNr9r�r6r�rCr�)rrrrr r�r�rrrr%iszOffloadedCache.update�r!Nr)r?r@rArBrrEr�r�rrrCrDr�rGr:rrrFrr%r�r�rIrrrrr�&s* �� *r�c s�eZdZdZdeddf�fdd�Z ddejdejd ed e e eefde ejejff dd�Zdd e edefdd�Zdd�Zdd�Z�ZS)�QuantizedCacheaK A quantizer cache similar to what is described in the [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache paper](https://arxiv.org/abs/2402.02750). It allows the model to generate longer sequence length without allocating too much memory for Key and Value cache by applying quantization. The cache has two types of storage, one for original precision and one for the quantized cache. A `residual length` is set as a maximum capacity for the original precision cache. When the length goes beyond maximum capacity, the original precision cache is discarded and moved into the quantized cache. The quantization is done per-channel with a set `q_group_size` for both Keys and Values, in contrast to what was described in the paper. It stores Keys and Values a list of quantized tensors (tuples in case we need to store metadata), one for each layer. Additionally, it stores the Key and Value in original precision states as a list of tensors, one for each layer. The size of each tensor is `[batch_size, num_heads, seq_len - residual_length, head_dim]` �cache_configr!Ncs\t��g|_g|_|j|_|j|_|j|_|j|_|j|_|j |_ |j |_ t��dSr)rr�_quantized_key_cache�_quantized_value_cacher}r�r�r~rr�r6�rr�rrrr�s zQuantizedCache.__init__rrrr c Cs�|dkr|j|jd7_t|j�|krtd��t|j�|kra|j�|j|��|j d��|j �|j|��|jd��|j�tj d|j|jd��|j�tj d|j|jd��||}}||fS|�|j|�}|�|j |�}||j||g}||j||g}tj|dd�}tj|dd�}|j|��dkr�|j|jdd|jkr�|j|��|j d�|j|<|j|��|jd�|j |<tj d|j|jd�|j|<tj d|j|jd�|j|<||fStj|j||gdd�|j|<tj|j||gdd�|j|<||fS) Nrr�zWQuantizedCache does not support model usage where layers are skipped. Use DynamicCache.)�axis�r�r6r�rxr)r;r�r4r5r�r�rN� _quantize� contiguousr~r�rrC�zerosr�r6r9�_dequantizer�r�r�) rrrrr Zkeys_to_returnZvalues_to_returnZdequant_keyZ dequant_valuerrrr%�s: � ��zQuantizedCache.updatercCs*t|j�|kr dS|dkr|jS|jdS)r&rr)r4r5r;r'rrrr(�szQuantizedCache.get_seq_lengthcCr")z:Quantizes a key/value using a defined quantization method.z1Make sure to implement `_quantize` in a subclass.r#)rr�r�rrrr��r-zQuantizedCache._quantizecCr")zDDequantizes back the tensor that was quantized by `self._quantize()`z3Make sure to implement `_dequantize` in a subclass.r#)rZq_tensorrrrr��r-zQuantizedCache._dequantizerr>)r?r@rArBrvrrCrDrErrrFrrr%r(r�r�rIrrrrr��s$ �� + r�c�:eZdZdZdeddf�fdd�Zdd�Zd d �Z�ZS)�QuantoQuantizedCachea� Quantized Cache class that uses `quanto` as a backend to perform quantization. Current implementation supports `int2` and `int4` dtypes only. 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Example: ```python >>> # Run pip install quanto first if you don't have it yet >>> from transformers import AutoTokenizer, AutoModelForCausalLM, QuantoQuantizedCache, QuantizedCacheConfig >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct") >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") >>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> cache_config = QuantizedCacheConfig(nbits=4) >>> past_key_values = QuantoQuantizedCache(cache_config=cache_config) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation QuantoQuantizedCache() ``` r�r!Ncs�t��|�t�r+t�tj�d��}|t�d�kr!td|�d��ddlm }m }m}|jdvr8t d|j��|jd vrEt d |j��|jd vrRt d|j��|jdkrY|n||_|�|_dS) Nzoptimum-quantoz0.2.5zzYou need optimum-quanto package version to be greater or equal than 0.2.5 to use `QuantoQuantizedCache`. Detected version �.r)�MaxOptimizer�qint2�qint4)r[rxzA`nbits` for `quanto` backend has to be one of [`2`, `4`] but got )rr�zE`axis_key` for `quanto` backend has to be one of [`0`, `-1`] but got zG`axis_value` for `quanto` backend has to be one of [`0`, `-1`] but got rx)rrrr �parse� importlib�metadata�ImportError�optimum.quantor�r�r�r}r�r~r�qtype� optimizer)rr�Zoptimum_quanto_versionr�r�r�rrrrs$ � �zQuantoQuantizedCache.__init__cCsHt�r"ddlm}|�||j||j�\}}|||j||||j�}|SdS)Nr)�quantize_weight)rr�r�r�r�r�)rr�r�r��scaleZ zeropoint�qtensorrrrr�'s�zQuantoQuantizedCache._quantizecCs|��Sr)� dequantize)rr�rrrr�0sz QuantoQuantizedCache._dequantize� r?r@rArBrJrr�r�rIrrrrr��s r�cr�)�HQQQuantizedCachea� Quantized Cache class that uses `HQQ` as a backend to perform quantization. Current implementation supports `int2`, `int4`, `int8` dtypes. Parameters: cache_config (`QuantizedCacheConfig`): A configuration containing all the arguments to be used by the quantizer, including axis, qtype and group size. Example: ```python >>> # Run pip install hqq first if you don't have it yet >>> from transformers import AutoTokenizer, AutoModelForCausalLM, HQQQuantizedCache, QuantizedCacheConfig >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct") >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") >>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> cache_config = QuantizedCacheConfig(nbits=4, axis_key=1, axis_value=1) >>> past_key_values = HQQQuantizedCache(cache_config=cache_config) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation HQQQuantizedCache() ``` r�r!Ncsdt��|�|jdvrtd|j��|jdvr td|j��|jdvr-td|j��t|_dS)Nr�zM`nbits` for `HQQ` backend has to be one of [`1`, `2`, `3`, `4`, `8`] but got )rrzA`axis_key` for `HQQ` backend has to be one of [`0`, `1`] but got zC`axis_value` for `HQQ` backend has to be one of [`0`, `1`] but got )rrr}r�r~r�HQQQuantizer� quantizerr�rrrrPs � zHQQQuantizedCache.__init__cCsJ|jj|||j|j|j|jd�\}}|j|d<|jj|||jd�||fS)N)r�r6r�r}� group_sizer�)�metar6)r�quantizer6r�r}r�r�)rr�r�r�rrrrr�_s � zHQQQuantizedCache._quantizecCs|\}}|j�||�}|Sr)rr�)rr�Zquant_tensorrr�rrrr�lszHQQQuantizedCache._dequantizer�rrrrr4s rc s�eZdZdZdZdededdf�fdd�Zed d ��Zde j de j d e j de j fdd�Zde j de j d e j dee j e j ffdd�Z ddeedefdd�Zdeefdd�Z dde j de j dedeeeefdee j e j ff dd�Z�ZS)� SinkCachea� A cache that as described in the [Attention Sinks paper](https://arxiv.org/abs/2309.17453). It allows the model to generate beyond the length of its context window, without losing fluency in the conversation. As it discards past tokens, the model will lose the ability to generate tokens that depend on the context that was discarded. It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is `[batch_size, num_heads, seq_len, head_dim]`. Parameters: window_length (`int`): The length of the context window. num_sink_tokens (`int`): The number of sink tokens. See the original paper for more information. Example: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SinkCache >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct") >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") >>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> past_key_values = SinkCache(window_length=256, num_sink_tokens=4) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation SinkCache() ``` T� window_length�num_sink_tokensr!Ncs>t��g|_g|_||_||_i|_d|_d|_d|_ dSr�) rrr5r9rr�cos_sin_rerotation_cache� _cos_cache� _sin_cacher;)rrrrrrr�s zSinkCache.__init__cCsH|dd|jdd�f}|d|jddd�f}tj||fdd�S)N.r�r[r�)r�rCr�)�x�x1�x2rrr�_rotate_half�szSinkCache._rotate_halfr�cos�sincCs|||�|�|}|Sr)r)rrrrZrotated_key_statesrrr�_apply_key_rotary_pos_emb�sz#SinkCache._apply_key_rotary_pos_embc Cs�|jd|jvrk|�tj�}|�tj�}||j|jdd�}||j|jd�}||j|jdd�}||j|jd�}||||}||||} |�|j��d�| �|j��d�f|j|jd<|j|jdS)Nr�r)r�r r8rC�float32rr�� unsqueeze) rrrrZoriginal_cosZshifted_cosZoriginal_sinZshifted_sin�rerotation_cos�rerotation_sinrrr�_get_rerotation_cos_sin�s�z!SinkCache._get_rerotation_cos_sinrrcCs"t|j�|kr dS|j|jdSr�r�r'rrrr(�szSinkCache.get_seq_lengthcC�|jS)zfReturns the maximum sequence length of the cache object, in case of SinkCache it is the window length.)rrrrrr+�szSinkCache.get_max_cache_shaperr cCs�|�d�}|�d�}|�d�}|duo|du}|dkr%|j|jd7_|rk|dkrk|��dkr8||_||_n3|jdurH|d|_|d|_n#|jjd|jkrktj|j|dgdd �|_tj|j|dgdd �|_t |j �|kr|j �|�|j�|�n�|jd|� |�|jkr�tj|j ||gdd �|j |<tj|j||gdd �|j|<n�|j |dd�dd�|j|j|jdd�f} |�r|�||jd|j�|jd|j��\} }|dur�| d d|�f| d |d�f} }|�| | |�} |du�rtj| |fdd �} |j |dd�dd�d|j�f} tj| | |gdd �|j |<|j|dd�dd�d|j�f}|j|dd�dd�|j|j|jdd�f}tj|||gdd �|j|<|j ||j|fS)a; Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. Parameters: key_states (`torch.Tensor`): The new key states to cache. value_states (`torch.Tensor`): The new value states to cache. layer_idx (`int`): The index of the layer to cache the states for. cache_kwargs (`Dict[str, Any]`, `optional`): Additional arguments for the cache subclass. The following arguments can be used in `SinkCache`: `sin`, `cos` and `partial_rotation_size`. These arguments are used with models using RoPE, to recompute the rotation as the tokens are shifted. Return: A tuple containing the updated key and value states. rr�partial_rotation_sizeNrr�r[)r.r�.r�)�getr;r�r�r rrrCr�r4r5rNr9r(rrr)rrrrr rrrZ using_ropeZkeys_to_keeprrZ keys_passZ sink_keysZsink_valuesZvalues_to_keeprrrr%�sX (�� ""(�zSinkCache.updater>r)r?r@rArB� is_slidingrEr�staticmethodrrCrDrrrrr(r+rrFrr%rIrrrrrrsL �� rcs�eZdZdZdddejddfdedededejdej de ed e eeee ejeffd df�fdd� Z dd ejdejdede ee efd eejejff dd�Zdde ed efdd�Zd e efdd�Zdd�Zedd��Z�ZS)�StaticCachea Static Cache class to be used with `torch.compile(model)` and `torch.export()`. Parameters: config (`PretrainedConfig`): The configuration file defining the shape-related attributes required to initialize the static cache. batch_size (`int`): The batch size with which the model will be used. Note that a new instance must be instantiated if a smaller batch size is used. If you are manually setting the batch size, make sure to take into account the number of beams if you are running beam search max_cache_len (`int`): The maximum sequence length with which the model will be used. device (`torch.device` or `str`): The device on which the cache should be initialized. Should be the same as the layer. dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): The default `dtype` to use when initializing the layer. layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`): Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`. Example: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache >>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf") >>> inputs = tokenizer(text="My name is Llama", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate >>> max_generated_length = inputs.input_ids.shape[1] + 10 >>> past_key_values = StaticCache(config=model.config, batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation StaticCache() ``` NrSr�r�r6r��max_batch_size�layer_device_mapr!c s�t��|durt�d|jj�d��|p||_|dur |jn||_t |d�r+|j n|j|j|_ ||_ t|dd�dur@|jn|j|_g|_g|_|j|j|j|j f}t|j�D]h} |durd|| } n|} tj||j | d�}tj||j | d�}t�s�|�d| ��tj||| d��|�d| ��tj||| d��t|d| ��}t|d| ��}tj�|�tj�|�|j�|�|j�|�qYdS)N�The 'batch_size' argument of �l is deprecated and will be removed in v4.49. Use the more precisely named 'max_batch_size' argument instead.�head_dim�num_key_value_headsr�Z key_cache_Zvalue_cache_)rrr)r*rr?r�max_position_embeddingsr�r<r"�hidden_size�num_attention_headsr��getattrr#r5r9r�r3r�rCr�r�register_buffer�_dynamo�mark_static_addressrN) rrSr�r�r6r�rr�cache_shaper��layer_device�new_layer_key_cache�new_layer_value_cacherrrrVsB � �� zStaticCache.__init__rrrr cCs�|�d�}|j|}|j|}|�|j�}|�|j�}|dur-|�|�|�|�||fSz|�d||�|�d||�W||fStyb||dd�dd�|f<||dd�dd�|f<Y||fSw)a' Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. It is VERY important to index using a tensor, otherwise you introduce a copy to the device. Parameters: key_states (`torch.Tensor`): The new key states to cache. value_states (`torch.Tensor`): The new value states to cache. layer_idx (`int`): The index of the layer to cache the states for. cache_kwargs (`Dict[str, Any]`, `optional`): Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input to know how where to write in the cache. Return: A tuple containing the updated key and value states. �cache_positionNr[)rr5r9r8r��copy_�index_copy_r$)rrrrr r/�k_out�v_outrrrr%�s$ ��zStaticCache.updatercCs|j|djdd��S)�MReturns the sequence length of the cached states that were seen by the model.�rrr�r�)r5�any�sumr'rrrr(�szStaticCache.get_seq_lengthcCrr�r�rrrrr+��zStaticCache.get_max_cache_shapecC�4tt|j��D]}|j|��|j|��qdS�z4Resets the cache values while preserving the objectsN�r3r4r5�zero_r9r'rrr�reset��zStaticCache.resetcC�t�d|jj�d��|jS�NzThe 'batch_size' attribute of zr is deprecated and will be removed in v4.49. Use the more precisely named 'self.max_batch_size' attribute instead.�r)r*rr?rrrrrr��zStaticCache.batch_sizerr>)r?r@rArBrCrrrEr6r�rrrrFrrDrrr%r(r+r>rHr�rIrrrrr-sT+�� ?�� 2rcs�eZdZdZdZdddejddfdedededej dej d eed eeee eej effddf�fdd � Z ddejdejdedeeeefdeejf dd�Zdeefdd�Zdd�Z�ZS)�SlidingWindowCachea� Sliding Window Cache class to be used with `torch.compile` for models like Mistral that support sliding window attention. Every time when we try to update the cache, we compute the `indices` based on `cache_position >= self.config.sliding_window - 1`, if true(which means the cache can not hold all the old key value states and new states together because of the sliding window constraint), we need to do a cycle shift based on `indices` to replace the oldest states by the new key value states passed in. The `to_shift` is only true once we are above sliding_window. Thus with `sliding_window==64`: indices = (slicing + to_shift[-1].int()-1) % self.config.sliding_window tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 0]) We overwrite the cache using these, then we always write at cache_position (clamped to `sliding_window`) Parameters: config (`PretrainedConfig`): The configuration file defining the shape-related attributes required to initialize the static cache. batch_size (`int`): The batch size with which the model will be used. Note that a new instance must be instantiated if a smaller batch size is used. max_cache_len (`int`): The maximum sequence length with which the model will be used. device (`torch.device` or `str`): The device on which the cache should be initialized. Should be the same as the layer. dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): The default `dtype` to use when initializing the layer. layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`): Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`. Example: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SlidingWindowCache >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") >>> inputs = tokenizer(text="My name is Mistral", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate >>> max_generated_length = inputs.input_ids.shape[1] + 10 >>> past_key_values = SlidingWindowCache(config=model.config, batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation SlidingWindowCache() ``` TNrSr�r�r6r�rrr!c sFt|d�r |jdurtd��t|j|�}t�j|||||||d�dS)N�sliding_window��Setting `cache_implementation` to 'sliding_window' requires the model config supporting sliding window attention, please check if there is a `sliding_window` field in the model config and it's not set to None.)rSr�r�r6r�rr)r<rEr��minrr)rrSr�r�r6r�rrrrrrs � �zSlidingWindowCache.__init__rrrr cCs�|�d�}|j|}|j|}|jd|jkrQ|dd�dd�|jd�dd�f}|dd�dd�|jd�dd�f}|j||7<|j||7<||fStj|jtj|jd�� d�}|� d|jd�}||jdk} || d��d|j} |dd�dd�| f}|dd�dd�| f}z|�d||�|�d||�Wnt y�||dd�dd�|f<||dd�dd�|f<Ynw|j|��|j|��|j||7<|j||7<||fS)Nr/rr�rr�r[)rr5r9r�r�rC�ones�longr6�cumsum�clamprEr1r$r=)rrrrr r/r2r3�slicing�to_shiftr�rrrr%/s6 $$�zSlidingWindowCache.updatecCrrr8rrrrr+^r9z&SlidingWindowCache.get_max_cache_shapecCs4tt|j��D]}|j|��|j|��qdSrr<r'rrrr>as�zSlidingWindowCache.resetr)r?r@rArBrrCrrrEr6r�rrrrFrrDrrr%r+r>rIrrrrrD�sP4�� /rDc sZeZdZdZdedef�fdd�Zdedeee j fdd �Zd d�Zdeee j ee j ffdd �Z e d1deeee jddfdd��Zd2deedefdd�Zdd�Zde jfdd�Zdefdd�Zdefdd�Zed d!d"� d1d#ed$ed edd%fd&d'��Zeed d!d"� d1d(edd eddfd)d*��Zd+efd,d-�Zd.e j fd/d0�Z�ZS)3�EncoderDecoderCachea Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and cross-attention caches. Example: ```python >>> from transformers import AutoProcessor, AutoModelForCausalLM, DynamicCache, EncoderDecoderCache >>> model = AutoModelForCausalLM.from_pretrained("openai/whisper-small") >>> processor = AutoProcessor.from_pretrained("openai/whisper-small") >>> inputs = processor(audio=YOUR-AUDIO, return_tensors="pt") >>> # Prepare cache classes for encoder and decoder and pass it to model's forward >>> self_attention_cache = DynamicCache() >>> cross_attention_cache = DynamicCache() >>> past_key_values = EncoderDecoderCache(self_attention_cache, cross_attention_cache) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation EncoderDecoderCache() ``` �self_attention_cache�cross_attention_cachecsLt��||_||_i|_tt|j��D]}t|� |�dk�|j|<qdSr�) rrrOrP� is_updatedr3r4r5�boolr()rrOrPrrrrr�s �zEncoderDecoderCache.__init__rr!cCsP|t|�kr|jj||jj||jj||jj|fStdt|��d|��r�)r4rOr5r9rPr�r'rrrr��s �zEncoderDecoderCache.__getitem__cCr�r�)r4rOrrrrr��r�zEncoderDecoderCache.__len__cCsRd}t|j�dkr"t|j��|j��D]\}}|||f7}q|S|j��}|S)z\Converts the `EncoderDecoderCache` instance into its equivalent in the legacy cache format.rr)r4rP�ziprOr�)rr�� self_attn� cross_attnrrrr��s� �z#EncoderDecoderCache.to_legacy_cacheNr�cCs�|t�t�d�}|durFtt|��D]3}||dd�\}}|j�|||�t||�dkrE||dd�\}}|j�|||�d|j|<q|S)zUConverts a cache in the legacy cache format into an equivalent `EncoderDecoderCache`.)rOrPNr[T)r�r3r4rOr%rPrQ)rPr�r�rrrrrrr��s� �z%EncoderDecoderCache.from_legacy_cachercCs|j�|�S)r&)rOr(r'rrrr(�sz"EncoderDecoderCache.get_seq_lengthcCs�t|jd�r|j��t|jd�r|j��nt|jd�s4t|jd�s4td|j��d|j��d��|jD]}d|j|<q7dS)Nr>z�Neither self nor cross-attention cache have valid `.reset()` methods. `.reset()` should only be called on compatible cache classes, such as `StaticCache` or `SlidingWindowCache`. Got �" for the self attention cache and � for the cross attention cache.F)r<rOr>rPr��__str__rQr'rrrr>�s �� zEncoderDecoderCache.resetr1cCs|j�|�|j�|�dS)r2N)rOr:rPr�rrrr:�sz!EncoderDecoderCache.reorder_cache�methodcCsDt|jt�rt|jt�s td|�d|j��d|j��d��dS)N�`z)` is only defined for dynamic cache, got rVrW)� isinstancerOr�rPr�rX)rrYrrr�check_dynamic_cache�s � ��z'EncoderDecoderCache.check_dynamic_cache�maximum_lengthcCs|�|jj�|j�|�dS)z�Crop the past key values up to a new `maximum_length` in terms of tokens. `maximum_length` can also be negative to remove `maximum_length` tokens. This is used in assisted decoding and contrastive search.N)r\r�r?rO)rr]rrrr��szEncoderDecoderCache.cropr�r�r r�r�zList[EncoderDecoderCache]c CsV|�|jj�|j�||�}|j�||�}g}t||�D]\}}|�t||��q|S)r�)r\r�r?rOrPrSrNrN) rr�r�r�rOrPr�rTrUrrrr��szEncoderDecoderCache.batch_splitr�cs�t�}t�}tt|d��D]H�tj�fdd�|D�dd�}tj�fdd�|D�dd�}|�||��tj�fdd�|D�dd�}tj�fdd�|D�dd�}|�||��q|||�S)r�rc�g|]}|jj��qSr)rOr5r�r�rrr��z9EncoderDecoderCache.from_batch_splits.<locals>.<listcomp>r�cr^r)rOr9r�r�rrr�r_cr^r)rPr5r�r�rrr�r_cr^r)rPr9r�r�rrr�r_)r�r3r4rCr�r%)rPr�r�rOrPr�r�rr�rr��s z%EncoderDecoderCache.from_batch_splitsr�cC�*|�|jj�|j�|�|j�|�dS)r�N)r\r�r?rOrP)rr�rrrr��z+EncoderDecoderCache.batch_repeat_interleaver�cCr`)r�N)r\r�r?rOrP)rr�rrrr�raz(EncoderDecoderCache.batch_select_indicesrr>)r?r@rArBrrrErrrCrDr�r�r�rsrr�r�r(r>rGr:rFr\r�rr�r�r�r�rIrrrrrNhsR "�� rNcs�eZdZdZdddejddfdedededeej e fdejd eed ee eee ej effddf�fdd � Zdd�Zdd�Z d!dejdejdedee e efdeejf dd�Zdeefdd�Zd"deefdd�Zdd�Zedd ��Z�ZS)#�HybridCachea� Hybrid Cache class to be used with `torch.compile` for Gemma2 models that alternate between a local sliding window attention and global attention in every other layer. Under the hood, Hybrid Cache leverages ["SlidingWindowCache"] for sliding window attention and ["StaticCache"] for global attention. For more information, see the documentation of each subcomponeent cache class. Parameters: config (`PretrainedConfig): The configuration file defining the shape-related attributes required to initialize the static cache. batch_size (`int`): The batch size with which the model will be used. Note that a new instance must be instantiated if a smaller batch size is used. max_cache_len (`int`): The maximum sequence length with which the model will be used. device (`torch.device` or `str`, *optional*, defaults to `"cpu"`): The device on which the cache should be initialized. Should be the same as the layer. dtype (torch.dtype, *optional*, defaults to `torch.float32`): The default `dtype` to use when initializing the layer. layer_device_map(`Dict[int, Union[str, torch.device, int]]]`, `optional`): Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`. Example: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, HybridCache >>> model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b") >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b") >>> inputs = tokenizer(text="My name is Gemma", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate >>> max_generated_length = inputs.input_ids.shape[1] + 10 >>> past_key_values = HybridCache(config=model.config, batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values # access cache filled with key/values from generation HybridCache() ``` Nr{rSr�r�r6r�rrr!cs|t��|durt�d|jj�d��t|d�r|jdur"td��||_ |p(||_ t|d�r2|jn|j|j |_||_|jdurD|j n|j|_tjdd�t|j�D�tj|d�|_g|_g|_|j|j||jf}|j|jt|j|�|jf} t|j�D]@} |dur�|| }n|}|j| s�|n| }tj||j|d�} tj||j|d�}tj�| �tj�|�|j�| �|j�|�q{dS) Nr r!rErFr"cSsg|] }t|d��qS)r[)rR)rnr�rrrr�fsz(HybridCache.__init__.<locals>.<listcomp>r�)rrr)r*rr?r<rEr�r�rr"r%r&r�r#rCr�r3r�rRrr5r9r�rGr�r)r*rN)rrSr�r�r6r�rrZglobal_cache_shapeZsliding_cache_shaper�r,r+r-r.rrrrDsN �� zHybridCache.__init__cCsl|jd|kr?|dd�dd�|d�dd�f}|dd�dd�|d�dd�f}|j||7<|j||7<||fStj|tj|jd��d�}|�d|d�}||dk} || d� �d|} |dd�dd�| f}|dd�dd�| f}||dd�dd�|f<||dd�dd�|f<|j|� �|j|� �|j||7<|j||7<||fS)Nrr�rr�)r�r5r9rCrHrIr6rJrKrEr=)rr/rrrr2r3r�rLrMr�rrr�_sliding_update�s&""zHybridCache._sliding_updatecCsH||dd�dd�|f<||dd�dd�|f<||j|<||j|<||fSr)r5r9)rr/rrrr2r3r�rrr�_static_update�s zHybridCache._static_updaterrrr c CsT|�d�}|�d�}|j|}|j|}|r|j} n|j} | |||||||jd�S)Nr/rEr[)rr5r9rcrdr�) rrrrr r/rEr2r3� update_fnrrrr%�s �zHybridCache.updatecCrrr8rrrrr+�r9zHybridCache.get_max_cache_shapercCs*|dkrtd��|j|djdd��S)Nrz�`get_seq_length` on `HybridCache` may get inconsistent results depending on the layer index. Using the `layer_idx` argument is not supported.r5r�r�)r�r5r6r7r'rrrr(�s �zHybridCache.get_seq_lengthcCr:r;r<r'rrrr>�r?zHybridCache.resetcCr@rArBrrrrr��rCzHybridCache.batch_sizerr>)r?r@rArBrCrrrErr6rFr�rrrrcrdrDrrr%r+r(r>rHr�rIrrrrrbsX-�� < �� rbc@s�eZdZdZdejddfdededejde e ejefde ef dd �Z d edejdejd ejfdd�Zd edejfdd�Zdd�Zedd��ZdS)� MambaCacheaV Cache for mamba model which does not have attention mechanism and key value states. Arguments: config (`PretrainedConfig): The configuration file defining the shape-related attributes required to initialize the static cache. batch_size (`int`): The batch size with which the model will be used. Note that a new instance must be instantiated if a smaller batch size is used. dtype (`torch.dtype`, *optional*, defaults to `torch.float16`): The default `dtype` to use when initializing the layer. device (`torch.device` or `str`, *optional*): The device on which the cache should be initialized. Should be the same as the layer. Attributes: dtype: (`torch.dtype`): The default `dtype` used to initializing the cache. intermediate_size: (`int`): Model's intermediate_size taken from config. ssm_state_size: (`int`): Model's state_size taken from config. conv_kernel_size: (`int`): Model's convolution kernel size taken from config conv_states: (`torch.Tensor`): A tensor of shape `[layer_idx, batch_size, intermediate_size, conv_kernel_size]` that holds convolutional states. ssm_states: (`torch.Tensor`): A tensor of shape `[layer_idx, batch_size, intermediate_size, ssm_state_size]` that holds ssm states Example: ```python >>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> past_key_values = MambaCache(config=model.config, batch_size=1, device=model.device, dtype=model.dtype) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> outputs.past_key_values MambaCache() ``` NrSr�r�r6rcCs�|durt�d|jj�d��||_|p||_|j|_|j|_|j |_ tj|j |j|j|j ||d�|_tj|j |j|j|j||d�|_tj�|j�tj�|j�dS)Nr r!�r6r�)r)r*rr?r�r�intermediate_size� state_sizeZssm_state_size�conv_kernel�conv_kernel_sizerCr�r��conv_states� ssm_statesr)r*)rrSr�r�r6rrrrrs6� �� zMambaCache.__init__r�new_conv_stater/r!cCsx|j|}|�d|jd�}|jddd�}|j|j|jd�|dd�dd�|f<|j|��|j||7<|j|S)Nrrr�)�shifts�dimsrg)rlrKrk�rollr8r6r�r=)rrrnr/Z conv_staterrr�update_conv_state3s $ zMambaCache.update_conv_state� new_ssm_statecCs|�|jj�|j|<|j|Sr)r8rmr6)rrrsrrr�update_ssm_state?s zMambaCache.update_ssm_statecCs|j��|j��dSr)rlr=rmrrrrr>Cs zMambaCache.resetcCr@rArBrrrrr�GrCzMambaCache.batch_size)r?r@rArBrCr�rrEr�rrr6rFrrDrGrrrtr>rHr�rrrrrf�s<2�� '�� rfc@sReZdZdZde�d�dfdededeede e ejfdeejd e e ejfd eeee e ejeffddfdd �Z d%dejdejdedeee efdeejejff dd�Zd&deedefdd�Zdeefdd�Zd'dd�Zedefdd��Zdeedfdejdeejejffdd �Zdeddfd!d"�Zdeddfd#d$�ZdS)(�OffloadedStaticCachea Static cache class to be used with `torch.compile(model)` that offloads to the CPU or another device. Args: config (`PretrainedConfig): The configuration file defining the shape-related attributes required to initialize the static cache. max_batch_size (`int`): The maximum batch size with which the model will be used. max_cache_len (`int`): The maximum sequence length with which the model will be used. device (`Union[str, torch.device]`): The device on which the cache should be initialized. Should be the same as the layer device. dtype (`torch.dtype`, *optional*): The default `dtype` to use when initializing the cache. offload_device (`Union[str, torch.device]`, *optional*, defaults to `cpu`): The device to offload to. Defaults to CPU. layer_device_map (`Dict[int, Union[str, torch.device, int]]`, *optional*): Mapping between the layers and its device. This is required when you are manually initializing the cache and the model is splitted between differents gpus. You can know which layers mapped to which device by checking the associated device_map: `model.hf_device_map`. Attributes: key_cache (`List[torch.Tensor]`): Off-loaded key cache tensors. First one will be on device, where-as the others are off-loaded. value_cache (`List[torch.Tensor]`): Off-loaded value cache tensors. First one will be on device, where-as the others are off-loaded. max_batch_size (`int`): The maximum batch size with which this cache can be used. max_cache_len (`int`): The maximum sequence length with which this cache can be used. device (`torch.device`): The device on which the cache is used. offload_device (`torch.device`): The device used to offload to. dtype (`torch.dtype`): The `dtype` used to initializing the cache. Example: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, OffloadedStaticCache >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt") >>> # Prepare a cache class and pass it to model's forward >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate >>> max_generated_length = inputs.input_ids.shape[1] + 10 >>> past_key_values = OffloadedStaticCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype) >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation ``` Nr{rSrr�r6r��offload_devicerr!cCsd||_|dur |jn||_|durt�|�n|d|_t�|�|_|dur'|ntj|_t|d�r3|j n|j |j}t|dd�durD|jn|j } || |j|f} g|_g|_t|j�D] }|dkrb|jn|j}|�| |�\}} |j�|�|j�| �qYg|_g|_td�D]}|�| |j�\}} |j�|�|j�| �q�d|_|jjdkr�tj��|_dSd|_dS)Nrr"r#r[r�)rr$r�rCr6rvrr�r<r"r%r&r'r#r5r9r3r��_create_key_value_cache_tensorsrN�_device_key_cache�_device_value_cacher;�typer�r��_prefetch_stream)rrSrr�r6r�rvrr"r#r+r�r5r9rrrr�s4 ��&zOffloadedStaticCache.__init__rrrr cCs|dkr|j|jd7_|jd}|jd}n|jdur)tj�|j�� |j�|j |d@}|j|d@}|�|d�|durG|� d�nd}|durw|�|�|�|�|dkrs|j|�|�|j��|j|�|�|j��||fSz|�d||�|�d||�Wnty�||dd�dd�|f<||dd�dd�|f<Ynw|dkr�|�|j�}|�|j�}|�|j�}z|j|�d||�|j|�d||�W||fSty�||j|dd�dd�|f<||j|dd�dd�|f<Y||fSw||fS)a0 Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. It is VERY important to index using a tensor, otherwise you introduce a copy to the device. Parameters: key_states (`torch.Tensor`): The new key states to cache. value_states (`torch.Tensor`): The new value states to cache. layer_idx (`int`): The index of the layer to cache the states for. cache_kwargs (`Dict[str, Any]`, *optional*): Additional arguments for the cache subclass. The `OffloadedStaticCache` needs the `cache_position` input to know how where to write in the cache. Return: A tuple containing the updated key and value states. rr�Nrr/r[)r;r�r5r9r{rCr��default_streamr6�wait_streamrxry�_prefetch_layerrr0r8rvr1r$)rrrrr r2r3r/rrrr%�sL ��zOffloadedStaticCache.updatercCr)r4�r;r'rrrr(�z#OffloadedStaticCache.get_seq_lengthcCr)z9Returns the maximum sequence length of the cached states.r8rrrrr+sz(OffloadedStaticCache.get_max_cache_shapecCs:d|_tt|j��D]}|j|��|j|��q dS)z5Resets the cache values while preserving the objects.rN)r;r3r4r5r=r9r'rrrr>$s �zOffloadedStaticCache.resetcCrrrrrrrr=1r�z OffloadedStaticCache.seen_tokensr�.cCsV|t�d�k}tj||j||d�}tj||j||d�}tj�|�tj�|�||fS)a8Creates K/V cache tensors on a device. Pins memory for CPU tensors. Marks them as static addresses for non-CPU tensors. Args: shape (`Tuple[int, ...]`): Shape. device (`torch.device`): Device. Returns: Key and value cache tensors as a tuple. r{)r�r6� pin_memory)rCr6r�r�r)r*)rr�r6� is_cpu_devicer5r9rrrrw7sz4OffloadedStaticCache._create_key_value_cache_tensorscCsh|t|j�kr dS|jdur-tj�|j��|�|�Wd�dS1s&wYdS|�|�dS)zMPrefetch a layer to the device. Needs to be called in order of layer indices.N)r4r5r{rCr�r��_prefetch_layer_in_contextr'rrrr~Qs "�z$OffloadedStaticCache._prefetch_layercCs@|j|d@j|j|dd�|j|d@j|j|dd�dS)z6Performs the actual copy of the layer to device cache.rTr�N)rxr0r5ryr9r'rrrr�_s"z/OffloadedStaticCache._prefetch_layer_in_contextrr>r�)r?r@rArBrCr6rrErrrFr�rrrDrrr%r(r+r>rHr=rwr~r�rrrrruPs`B�� >�� S �� ru)2rf�importlib.metadatar�rart�dataclassesr�typingrrrrrrrC� packagingr �configuration_utilsr�utilsr rrrZutils.deprecationrZhqq.core.quantizerr� get_loggerr?r)�nn�ModulerrJrvr�r�r�r�r�rrrrDrNrbrfrurrrr�<module>sN W`b'IrZB><0 2Dt