U
    9%e8P                  	   @   sB  d dl Z d dlZd dlZd dlmZ d dlm  m  m  mZ	 d dl
m  m  mZ ejjZd dlmZmZmZmZmZmZm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"m#Z# G d
d de j$Z%eeeee&ee" f ee%e%f dddZ'eeee&ee" f eeeej(e)f eej(e*f f  dddZ+eeedddZ,eee*dddZ-eee* dddZ.eee&dddZ/e#e&e#dddZ0e#ddd d!Z1d"d# Z2e2ej(ej(ej(d$d%d&Z3e2ej(ej(ej(d$d'd(Z4e2ej(ej(ej(d$d)d*Z5ee6dd+d,Z7eee*ed-d.d/Z8dS )0    N)TupleCallableDictSetListOptionalUnion)GraphModule)Node)ObserverBaseFakeQuantizeBase)getattr_from_fqn)_is_activation_post_process   )NSNodeTargetTypeNSResultsTypec                   @   s4   e Zd Ze Ze Ze Ze Ze Z	dS )NodeInputOrOutputTypeN)
__name__
__module____qualname__enumautoFP32INT8FP16UNKNOWNZFP32_OR_INT8 r   r   S/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/ao/ns/fx/utils.pyr      s
   r   )nodegm
logger_clsnode_type_to_io_type_mapreturnc                    s  |d }|d }|d }|d }|d }|d }	|d }
|d }| j d	kr| j|kr`tjtjfS | j|krvtjtjfS | j|krtjtjfS | j|krt| |d
}t|tst	t
||||\}}||fS tjtjfS n| j dkr| j dkst	t| jtst	t|| j t fdd|
D }t |ttfs@|rvt| |d
}t|ts\t	t
||||\}}||fS t fdd|D }t fdd|	D }|rtjtjfS |rtjtjfS tjtjfS n| j dkr| jdkr&t| |d
}t|ts
t	t
||||\}}|tjfS | jdkrt| |d
}t|tsNt	t
||||\}}t| |d}|tjkst	| d|tjfS | j|krt| |d
}t|tst	t
||||\}}||fS tjtjfS tjtjfS d S )NZfuns_io_type_fp32Zfuns_io_type_fp16Zfuns_io_type_int8Zfuns_io_type_fp32_or_int8Zmods_io_type_fp32Zmods_io_type_int8mods_io_type_fp32_or_int8Zmeths_io_type_fp32_or_int8call_functionr   call_modulec                 3   s   | ]}t  |V  qd S N
isinstance.0target_typemodr   r   	<genexpr>O   s    z7get_node_first_input_and_output_type.<locals>.<genexpr>c                 3   s   | ]}t  |V  qd S r&   r'   r)   r,   r   r   r.   a   s    c                 3   s   | ]}t  |V  qd S r&   r'   r)   r,   r   r   r.   d   s    call_method
dequantizetor   z handling needs to be added)optargetr   r   r   r   get_normalized_nth_inputr(   r
   AssertionError$get_node_first_input_and_output_typer   strr   anyr   r   torchZfloat16)r   r   r    r!   ZFUNS_IO_TYPE_FP32ZFUNS_IO_TYPE_FP16ZFUNS_IO_TYPE_INT8ZFUNS_IO_TYPE_FP32_OR_INT8ZMODS_IO_TYPE_FP32ZMODS_IO_TYPE_INT8MODS_IO_TYPE_FP32_OR_INT8ZMETHS_IO_TYPE_FP32_OR_INT8Z	first_argZ_prev_node_input_typeZprev_node_output_type"is_known_fp32_or_int8_input_moduleZis_known_fp32_input_moduleZis_known_int8_input_module	prev_nodeZcur_node_dtype_targetr   r,   r   r6   &   s    




         
   
   r6   )r   r   r!   r"   c                    sF  t | |d}t|tsdS |d }dd }|jdkrz|jtjkrN|||ddS |jtjtj	tj
tjfkrv|||dd	S dS |jd
krBt|jtstt||j t tjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtj tj!tj"tj#tj$tj%tjtj&tj'fr j( j)fS t* fdd|D }|rBt+|||S dS )z{
    Returns the qparams (scale, zero_point) of the first input to `node`,
    if they can be inferred from the graph.
    r   Nr#   c                 S   sl   t | ||}t | ||}t|tr.t|jts2tt|trHt|jtsLtt||j}t||j}||fS r&   )r4   r(   r
   r3   r7   r5   r   )r   r   Zscale_arg_idxZ
zp_arg_idxZ
scale_nodeZzp_nodeZ	scale_objZzp_objr   r   r    _get_scale_zp_from_function_args   s    z@get_node_input_qparams.<locals>._get_scale_zp_from_function_argsr$   r         r%   c                 3   s   | ]}t  |V  qd S r&   r'   r)   Z
module_objr   r   r.      s    z)get_node_input_qparams.<locals>.<genexpr>),r4   r(   r
   r2   r3   r9   Zquantize_per_tensortoqaddZadd_relumulZmul_relur7   r5   r   nnqZLinearZConv1dZConv2dnniqZ
ConvReLU2dZConv3dZBatchNorm2dZBatchNorm3dZConvTranspose1dZConvTranspose2dZELUZ	GroupNormZInstanceNorm1dZInstanceNorm2dZInstanceNorm3dZ	LayerNormZ	HardswishZ	LeakyReLUZReLU6ZBNReLU2dZBNReLU3dZ
ConvReLU1dZ
ConvReLU3dZ
LinearReLUscaleZ
zero_pointr8   get_node_input_qparams)r   r   r!   r<   r:   r=   r;   r   r@   r   rG      sb    	
	
rG   )r   r   r"   c                 C   s   | j dkrt|| j}t|rt| jdks0tt| jd tsDt| jd } t| jt	s^tt|| j}t|rt| jdkstt| jd tst| jd } | S )a  
    If node is not an observer, returns it.  If node is an observer,
    navigates up the graph and returns the first parent which is not an
    observer.  For example,

    graph: (node_non_obs), node = node_non_obs : returns node_non_obs
    graph: (node_non_obs -> obs0), node = obs0 : returns node_non_obs
    graph: (node_non_obs -> obs0 -> fq0), node = fq0 : returns node_non_obs
    r%   r   r   )
r2   r   r3   r   lenargsr5   r(   r
   r7   r   r   Znode_objr   r   r   return_first_non_observer_node   s    


rK   c                 C   s*   | j dkr&t|| j}t|tjr&dS dS )aO  
    Assumes that all non-param args occur first. Returns the number of
    non-param args expected for a node.  For example, for

      F.linear(x, weight, bias)

    Returns 1, because x is a non-param arg and weight and bias are params.
    For

      lstm_mod(x, hid)

    Returns 2, because both x and hid are non-param args.
    r%   r>   r   )r2   r   r3   r(   nnZLSTMrJ   r   r   r   get_number_of_non_param_args  s
    
rM   )r   r"   c                 C   s   t | jdkrg S | jdkr| jtjtjjjtjfksP| jtj	tjjj	tj	fkrg }t
dD ] }t| j| tkr\|| q\|S dgS )a-  
    Returns the indices of args of the node which we should attach
    loggers to, if input logging is enabled.

    For example,
    * for (x + y), returns [0, 1]
    * for (1 + y), returns [1]
    * for (x + 1), returns [0]
    * for (linear(x, w, b)) returns [0]
    * by default, returns [0]
    r   r$   r>   )rH   rI   r2   r3   r9   rB   ops	quantizedoperatorrC   rangetyper
   append)r   resultir   r   r    get_arg_indices_of_inputs_to_log+  s    
rV   c                 C   sP   d}| j dkrt| j}n0| j dkrLt| jts6tt|| j}t|}|S )z
    Returns a string representation of the type of the function or module
    pointed to by this node, or '' for other node types.
     )r$   r/   r%   )r2   r9   typenamer3   r(   r7   r5   r   )r   r   r+   Z
target_modr   r   r   get_target_type_strF  s    


rY   )results
model_namer"   c           	      C   s|   i }|   D ]j\}}d}| D ]:}|  D ],\}}||kr,t|sHt|d d }q,q,q,q |dk	rn|||< q|||< q|S )a	  
    Rekeys the layer name of a results dictionary to use node names
    from `model_name`.

    For example, transforms

        {'base_op_1_0': {'node_output': {'model_a':
          [{'ref_node_name': 'linear1', ...}]}}}

    into

        {'linear1': {'node_output': {'model_a':
          [{'ref_node_name': 'linear1', ...}]}}}

    Note: we cannot use these node names directly because they are not
    guaranteed to be consistent across models. This is why we extract
    the results first and rekey afterwards.
    Nr   Zref_node_name)itemsvaluesrH   r5   )	rZ   r[   Znew_resultsZold_layer_nameresult_type_to_resultsZnew_layer_namemodel_name_to_resultsZcur_model_nameZlist_of_resultsr   r   r   'rekey_logger_info_on_node_name_of_modelU  s    

r`   )rZ   r"   c           	      C   s   d}|   D ]P}|  D ]>}| D ],\}}t|dkr$|d d dk	r$|} qRq$ qXq q^q|r|   D ]`}|  D ]R}|| }| D ]<\}}||krqtt|D ]}|| d }||| d< qqqvqjdS )ay  
    If `fqn` entries are filled in for one of the models in `results`, copies
    them over to any models which do not have them filled out.

    A common use case benefitting from this is comparing a model prepared by
    quantization to a quantized model. In this case, the model prepared by
    quantization would have `fqn` entries, and the quantized model would not.
    Nr   fqn)r]   r\   rH   rQ   )	rZ   Zmodel_name_with_fqnsr^   r_   r[   Zmodel_resultsZref_model_resultsrU   ra   r   r   r   maybe_add_missing_fqns|  s(    rb   c                    s    fddS )Nc            	         s   | ^}}}t |trt |ts2t |trjt |trjg }t||D ]$\}}||f|}||| q@|S t |tjrt |tjr|jr| }|jr| }|j	tj
ks|j	tj
krd S ||f|} ||S r&   )r(   tuplelistziprS   r9   TensorZis_quantizedr0   Zdtypefloat)	rI   kwargsZa0Za1Za_otherrZ   Zel0Zel1new_argsfinnerr   r   rl     s(    
zGmaybe_dequantize_first_two_tensor_args_and_handle_tuples.<locals>.innerr   )rk   r   rj   r   8maybe_dequantize_first_two_tensor_args_and_handle_tuples  s    rm   )xyr"   c                 C   s*   t | }t | | }dt ||  S )z
    Computes the SQNR between `x` and `y`.

    Args:
        x: Tensor or tuple of tensors
        y: Tensor or tuple of tensors

    Return:
        float or tuple of floats
       )r9   Znormlog10)rn   ro   ZPsZPnr   r   r   compute_sqnr  s    
rr   c                 C   s"   t | | d  | d   S )z
    Computes the normalized L2 error between `x` and `y`.

    Args:
        x: Tensor or tuple of tensors
        y: Tensor or tuple of tensors

    Return:
        float or tuple of floats
    r>   )r9   sqrtsumrn   ro   r   r   r   compute_normalized_l2_error  s    rv   c                 C   s(   |  dd} | dd}tjj| |S )z
    Computes the cosine similarity between `x` and `y`.

    Args:
        x: Tensor or tuple of tensors
        y: Tensor or tuple of tensors

    Return:
        float or tuple of floats
    r   )Zreshaper9   rL   Z
functionalZcosine_similarityru   r   r   r   compute_cosine_similarity  s    rx   c                 C   s4   | j dkr0| jtjtjtjtjtjtjfkr0dS dS )Nr$   FT)r2   r3   r9   rB   rC   rP   catstack)r   r   r   r   op_type_supports_shadowing  s    
"r{   )r   r   idxr"   c                 C   s6  z| j |dd}|dk	rb|\}}t|t| |ks8t|t|k rN|| W S t| | W S nXt| jt| j |ks~t|t| jk r| j| W S |t| j }t| j | W S W nt tk
r0   t| jt| j |kst|t| jk r| j|  Y S |t| j }t| j |  Y S Y nX dS )zu
    Given a node, gets the n'th input to that node, normalizing
    args and kwargs to the best of its ability.
    T)Znormalize_to_only_use_kwargsN)Znormalized_argumentsrH   r5   rd   r]   rI   rh   RuntimeError)r   r   r|   Znorm_args_and_kwargsZ	norm_argsZnorm_kwargsZ
kwargs_idxr   r   r   r4     s,     
r4   )9r   rP   r9   Ztorch.nnrL   Ztorch.ao.nn.intrinsic.quantizedZaoZ	intrinsicrO   rE   Ztorch.ao.nn.quantizedrD   rN   rA   typingr   r   r   r   r   r   r   Ztorch.fxr	   Ztorch.fx.graphr
   Ztorch.ao.quantizationr   r   Ztorch.ao.quantization.utilsr   Ztorch.ao.quantization.observerr   Zns_typesr   r   Enumr   r7   r6   rf   rg   intrG   rK   rM   rV   rY   r`   rb   rm   rr   rv   rx   boolr{   r4   r   r   r   r   <module>   sb   $
}"S'"