U Kc @sddlmZmZmZmZmZmZmZddlZddl m Z ddl m m Z ddlmZmZddlmZddd d d d d dddddg ZeeeeefdddZeeedfdddZeeedfddd Zeeeeeeffddd Zeeeej jeej jfddd Zeeeee jeffddd Zeeeeej jfddd ZeeeefdddZ eeeefdddZ!edddZ"edd dZ#edd!dZ$dS)")DictAnyListCallableUnionTupleTypeN) BackendConfig DTypeConfig)Patternget_pattern_to_dtype_configsget_qat_module_classesget_fused_module_classes"get_pattern_to_input_type_to_index-get_root_module_to_quantized_reference_moduleget_fuser_method_mappingget_module_to_qat_module&get_fusion_pattern_to_root_node_getter)get_fusion_pattern_to_extra_inputs_getter!remove_boolean_dispatch_from_namepattern_to_human_readableentry_to_pretty_str)backend_configreturncCs&i}|jD]\}}|j||<q|SN)configsitems dtype_configs)rZpattern_to_dtype_configspatternconfigr"N/tmp/pip-unpacked-wheel-gikjz4vx/torch/ao/quantization/backend_config/utils.pyrs .cCs6g}|jD]}|jdk r||jqtt|Sr)rvalues qat_moduleappendtupleset)rZqat_module_classesr!r"r"r#rs  cCs6g}|jD]}|jdk r||jqtt|Sr)rr$Z fused_moduler&r'r()rZfused_module_classesr!r"r"r#r%s  cCs&i}|jD]\}}|j||<q|Sr)rrZ_input_type_to_index)rZpattern_to_input_type_to_indexr r!r"r"r#r,s cCs8i}|jD]$}|jdk r|jdk r|j||j<q|Sr)rr$Z root_moduleZreference_quantized_module)rmappingr!r"r"r#r2s cCs0i}|jD]\}}|jdk r|j||<q|Sr)rrZ fuser_method)rZfuser_method_mappingr r!r"r"r#r:s   cCs0i}|jD]\}}|jdk r|j||<q|Sr)rrr%)rZmodule_to_qat_moduler r!r"r"r#rAs   cCs0i}|jD]\}}|jdk r|j||<q|S)a Get a map from fusion pattern to a function that returns the root node from the fusion pattern, e.g. the most common one is: def get_root_node(node_pattern): while not isinstance(node_pattern[-1], Node): node_pattern = node_pattern[-1] return node_pattern[-1] This can work for all patterns whose root node is the "last node" in the pattern, e.g. (torch.add, MatchAllNode, (torch.ReLU, torch.Conv2d)) N)rrZ_root_node_getter)rZroot_node_getter_mappingr r!r"r"r#rHs   cCs0i}|jD]\}}|jdk r|j||<q|S)a Get a map from fusion pattern to a function that returns extra input nodes from the fusion pattern, in the order required by the root node. This is optional, if not specified, we will not copy over any extra inputs for the root node. Example: # Let's say we have the pattern (torch.add, MatchAllNode, (torch.nn.BatchNorm2d, torch.nn.Conv2d)) # and root node is torch.nn.Conv2d, and the node in MatchAllNode would be an extra # argument to the fused module, we can unpack the pattern and return the node at # MatchAllNode here # we can implement extra_inputs_getter as follows: def extra_inputs_getter(pattern) -> List[Any]: add, extra_input, conv_pattern = pattern return [extra_input] N)rrZ_extra_inputs_getter)rZextra_inputs_getter_mappingr r!r"r"r#rXs   )rcCs|tjkrdS|tjkrdS|tjkr*dS|tjkr8dS|tjkrFdS|tjkrTdS|tjkrbdS|tjkrpdSd t |kst |d d |S) z Some ops have a default string representation such as '.fn at 0x7ff1106bf280>', this function replaces them with the hardcoded function names. z)torch.nn.functional.fractional_max_pool2dz)torch.nn.functional.fractional_max_pool3dztorch.nn.functional.max_pool1dztorch.nn.functional.max_pool2dztorch.nn.functional.max_pool3dz'torch.nn.functional.adaptive_max_pool1dz'torch.nn.functional.adaptive_max_pool2dz'torch.nn.functional.adaptive_max_pool3dZboolean_dispatchz2 does not have a human readable representation in zquantization documentation) FZfractional_max_pool2dZfractional_max_pool3dZ max_pool1dZ max_pool2dZ max_pool3dZadaptive_max_pool1dZadaptive_max_pool2dZadaptive_max_pool3dstrAssertionErrorpr"r"r#rls,        cCs:t|trtdd|DSt|tr*|St|}|SdS)Ncss|]}t|VqdSr)r).0Zinner_pr"r"r# sz,pattern_to_human_readable..) isinstancer'r+rr-r"r"r#rs   cCsd}d|kr(t|d}|d|d7}d|kr|d7}|dD]<}|d7}|D]\}}|d|d |d7}qT|d 7}q@|d 7}d |kr|d 7}|d D]\}}|d|d|d7}q|d7}ddd g}|D](}||krq|d|d ||d7}q|d7}|S)zo Given a backend_config_dict entry, returns a string with the human readable representation of it. z{ r z 'pattern': z, rz 'dtype_configs': [ z { z 'z': z }, z ], Z#num_tensor_args_to_observation_typez+ 'num_tensor_args_to_observation_type': { z z: z }, z '})rr)entrysZ pattern_strZ dtype_configkvZcustom_handled_fields field_namer"r"r#rs6   )%typingrrrrrrrZtorchZtorch.nnnnZtorch.nn.functionalZ functionalr*rr r Zquantization_typesr __all__rtyperrr+intrModulerZ Sequentialrrrrrrrr"r"r"r#s@$    "