Name	Size	Mode	Actions
graph_matcher.cpython-36.pyc	12023	0644	editdlrm
graph_passes.cpython-36.pyc	15354	0644	editdlrm
mappings.cpython-36.pyc	7209	0644	editdlrm
ns_types.cpython-36.pyc	809	0644	editdlrm
pattern_utils.cpython-36.pyc	3795	0644	editdlrm
utils.cpython-36.pyc	10942	0644	editdlrm
weight_utils.cpython-36.pyc	6573	0644	editdlrm
__init__.cpython-36.pyc	150	0644	editdlrm

3 Eg\D� @sdd
lZdd
l
Z
dd
lZdd
ljZdd
ljjjZdd
l jjZ ejjZddl mZmZmZmZmZmZmZ
ddlmZ
ddlmZ
ddlmZmZ
ddlmZ
ddlmZ
dd l m!Z!m"Z"
Gd d�dej#�Z$eeeee%ee!fee$e$fd�d d�Z&eeee%ee!feeeej'e(feej'e)ffd�dd�Z*eeed�dd�Z+eee)d�dd�Z,eee)d�dd�Z-eee%d�dd�Z.e"e%e"d�dd�Z/e"d
d�d d!�Z0d"d#�Z1e1ej'ej'ej'd$�d%d&��
Z2e1ej'ej'ej'd$�d'd(��
Z3e1ej'ej'ej'd$�d)d*��
Z4d
S)+�N)�Tuple�Callable�Dict�Set�List�Optional�Union)
�GraphModule)
�Node)�ObserverBase�FakeQuantizeBase)
�getattr_from_fqn)
�is_activation_post_process�
)�NSNodeTargetType� NSResultsTypec
@s4eZ
dZej�Zej�Zej�Zej�Zej�Z d
S)�NodeInputOrOutputTypeN) �__name__� __module__�__qualname__�enum�auto�FP32�INT8�FP16�UNKNOWN�FP32_OR_INT8�rr�@/usr/local/lib64/python3.6/site-packages/torch/ao/ns/fx/utils.pyrs 



r)�node�gm� logger_cls�node_type_to_io_type_map�returncs�|d
}|d}|d}|d}|d}|d} |d} |d}|jd kr�|j
|kr`tjtjfS|j
|krvtjtjfS|j
|kr�tjtjfS|j
|kr�tjtjfStjtjfS�
n�|jd k�
r�|jd ks�t�
t |j
t �s�t�
t|
|j
��t �|tt f��
r.|jd}t |t��
st�
t||
||�\} }||fSt�f
dd �|D�
�
}t�f
dd �| D�
�
}t�f
dd �| D�
�
}|�
r�tjtjfS|�
r�tjtjfS|�
r�tjtjfStjtjfSn�|jdk�r�|j
dk�r|jd}t |t��
s�t�
t||
||�\} }|tjfS|j
dk�rh|jd}t |t��s(t�
t||
||�\} }|jd}|tjk�s^t|�d��
�
|tjfS|j
|k�r�tjtjfStjtjfStjtjfSdS)NZfuns_io_type_fp32Zfuns_io_type_fp16Zfuns_io_type_int8Zfuns_io_type_fp32_or_int8Zmods_io_type_fp32Zmods_io_type_int8�mods_io_type_fp32_or_int8Zmeths_io_type_fp32_or_int8� call_function�call_moduler
c
3s|]}
t�|
�V
qdS)
N)
� isinstance)�.0�target_type)
�modrr� Wsz7get_node_first_input_and_output_type..c
3s|]}
t�|
�V
qdS)
N)
r')r(r))
r*rrr+Zsc
3s|]}
t�|
�V
qdS)
N)
r')r(r))
r*rrr+]s�call_method� dequantizeZtorz handling needs to be added)�op�targetrrrrrr�AssertionErrorr'�strr rr�argsr �$get_node_first_input_and_output_type�any�torchZfloat16)rr r!r"ZFUNS_IO_TYPE_FP32ZFUNS_IO_TYPE_FP16ZFUNS_IO_TYPE_INT8ZFUNS_IO_TYPE_FP32_OR_INT8ZMODS_IO_TYPE_FP32ZMODS_IO_TYPE_INT8�MODS_IO_TYPE_FP32_OR_INT8ZMETHS_IO_TYPE_FP32_OR_INT8Z first_argZ_prev_node_input_typeZprev_node_output_typeZis_known_fp32_input_moduleZis_known_int8_input_module�"is_known_fp32_or_int8_input_module� prev_nodeZcur_node_dtype_targetr)
r*rr3&s�
































 

 
 
r3)rr r"r#csD
|jd
}t
|t�sdS|d}dd�}|jdkrx|jtjkrL|||
dd�S|jtjtj tj tjfkrt|||
dd �SdS|jd k�
r@t
|jt�s�t �
t|
|j��t
�tjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtj tj!tj"tj#tj$tj%tjtj&tj'f��
r�j(�j)fSt*�f
dd�|D�
�
}|�
r@t+||
|�SdS) z{ Returns the qparams (scale, zero_point) of the first input to `node`, if they can be inferred from the graph. r
Nr$cSsj|j||j|}}t
|t�r,t
|jt�s0t�
t
|t�rFt
|jt�sJt�
t|
|j�}t|
|j�}||fS)
N)r2r'r r/r1r0r )rr Z scale_arg_idxZ zp_arg_idxZ scale_nodeZzp_nodeZ scale_objZzp_objrrr� _get_scale_zp_from_function_args�s





z@get_node_input_qparams.._get_scale_zp_from_function_argsr%r��r&c
3s|]}
t�|
�V
qdS)
N)
r')r(r))
� module_objrrr+�sz)get_node_input_qparams..),r2r'r r.r/r5Zquantize_per_tensor�toq�addZadd_relu�mulZmul_relur1r0r �nnqZLinearZConv1dZConv2d�nniqZ ConvReLU2dZConv3dZBatchNorm2dZBatchNorm3dZConvTranspose1dZConvTranspose2dZELUZ GroupNormZInstanceNorm1dZInstanceNorm2dZInstanceNorm3dZ LayerNormZ HardswishZ LeakyReLUZReLU6ZBNReLU2dZBNReLU3dZ ConvReLU1dZ ConvReLU3dZ LinearReLUZscaleZ zero_pointr4�get_node_input_qparams)rr r"r8r6r9r7r)
r<rrB�s\ 
 






























rB)rr r#cCs�|jd
kr�t
|
|j�}t|�
r�t|j�
dks0t�
t|jdt�sDt�
|jd}t|jt �s^t�
t
|
|j�}t|�
r�t|j�
dks�t�
t|jdt�s�t�
|jd}|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&rr
) r.r r/r�lenr2r0r'r r1)rr �node_objrrr�return_first_non_observer_node�s










rEcCs*|jd
kr&t
|
|j�}t|tj�r&dSdS)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)r.r r/r'�nnZLSTM)rr rDrrr�get_number_of_non_param_args
s 


rG)rr#c
Cs�t|j
�
d
krgS|jdkr�|jtjtjjjtjfksP|jtj tjjj tj fkr�g}
x,t d�
D] }t|j
|�
tkr^|
j |�

q^W|
Sd
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:)rCr2r.r/r5r>�ops� quantized�operatorr?�range�typer �append)r�result�
irrr� get_arg_indices_of_inputs_to_log"
s







rPcCsPd
}|jdkrt
j|j�
}n0|jdkrLt|jt�s6t�
t|
|j�}t
j|�
}|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&)r%r,)r.r5�typenamer/r'r1r0r )rr r)Z target_modrrr�get_target_type_str=
s






rS)�results� model_namer#c Cs�i}x�|j�D]v\}}d
}xN|j�D]B\}}x8|j�D],\}} ||
kr6t
| �
sRt�
| dd}q6q6q6Wq$W|d
k r||||<q|||<qW|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)�itemsrCr0) rTrUZnew_resultsZold_layer_name�result_type_to_resultsZnew_layer_nameZ_result_type�model_name_to_resultsZcur_model_nameZlist_of_resultsrrr�'rekey_logger_info_on_node_name_of_modelL
s









rY)rTr#c
Cs�d
}
xf|j�D]Z\}}xN|j�D]B\}}x6|j�D]*\}}t
|�
dkr2|ddd
k r2|}
Pq2WPq WPqW|
r�x�|j�D]t\}}xj|j�D]^\}}||
}xL|j�D]@\}}||
kr�q�x,tt
|�
�
D]} || d} | || d<q�Wq�Wq�WqzWd
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)rVrCrK)rTZmodel_name_with_fqnsZ layer_namerWZresult_typerXrUZ model_resultsZref_model_resultsrOrZrrr�maybe_add_missing_fqnss
s(

















r[c

s��
fd
d��
�
S)Nc s�|^}}}t|t
�rt|t
�s2t|t�rnt|t�rng}x2t||�D]$\}}||f|�}|j�
||
�
�

qBW|St|tj�r�t|tj�r�|jr�|j�}|jr�|j�}|j tj ks�|j tj kr�dS||f|�}�||
�
S)
N)r'�tuple�list�ziprMr5�TensorZis_quantizedr-Zdtype�float) r2�kwargsZa0Za1Za_otherrTZel0Zel1Znew_args)�
f�innerrrrc�
s"











zGmaybe_dequantize_first_two_tensor_args_and_handle_tuples..innerr)
rbr)rbrcr�8maybe_dequantize_first_two_tensor_args_and_handle_tuples�
s
rd)�
x�
yr#cCs*tj
|�
}tj
||
�
}d
tj||�
S)N�)r5Znorm�log10)rerfZPsZPnrrr�compute_sqnr�
s

ricCs"tj
||
d
j�|d
j��
S)Nr:)r5�sqrt�sum)rerfrrr�compute_normalized_l2_error�
srlcCs(|jd
d�}|
jd
d�}
t
jjj||
�S)Nr�����rm)Zreshaper5rFZ functionalZcosine_similarity)rerfrrr�compute_cosine_similarity�
s

rn)5rrJr5Ztorch.nnrFZtorch.nn.intrinsic.quantizedZ intrinsicrIrAZtorch.nn.quantizedr@rHr=�typingrrrrrrrZtorch.fxr Ztorch.fx.graphr Ztorch.quantizationrrZtorch.quantization.utilsr Ztorch.quantization.quantizerZns_typesrr�Enumrr1r3r_r`�intrBrErGrPrSrYr[rdrirlrnrrrr�
sT




$






p


.N





$"