U Ç9%eTã@spddlmZddlZddlmZddlmZeedœdd„Zedœd d „Zedœd d „Z eeed œdd„Z dS)é)ÚListN)ÚQuantizationAnnotation)ÚNode)ÚnodeÚ input_nodecCs8|j dtƒ¡}|jdkr i|_||j|<||jd<dS©NÚquantization_annotation)ÚmetaÚgetrZinput_qspec_map)rrÚqspecr©r úd/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/ao/quantization/quantizer/utils.pyÚ_annotate_input_qspec_mapsÿ  r©rcCs$|j dtƒ¡}||_||jd<dSr)r r rZ output_qspec)rr rr r r Ú_annotate_output_qspecs ÿrcCsN|jdkr|jtjjjjkpL|jtjjjjkpL|jtjjjkpL|jtjjjkS)NZ call_function)ÚopÚtargetÚtorchZopsZatenZsym_sizeÚdefaultZ sym_numelrr r r Ú_is_sym_size_nodes ÿþýûr©rÚpartition_nodescs$t|ƒr dSt‡fdd„|jDƒƒS)aÓ This utility is used to handle cases when dynami_shape=True tracing leads to symint nodes in the pattern of linear module. In those cases, we need to distinguish between the nodes that are in input for just extracting value of some dimentions (and symint nodes) vs. the one that is activation. For example: graph(x, y, weight): size_0 = torch.ops.aten.sym_size([x], [0]) size_1 = torch.ops.aten.sym_size([y], [1]) view_size = size_0 * size_1 size_3 = torch.ops.aten.sym_size([x], [2]) vie_out = torch.ops.aten.view(x, [view_size, size_3]) return mm(view_out, weight) In the example above y node is not actual input. It exist only to extract size_1 Tc3s|]}|ˆkpt|ƒVqdS)N)r)Ú.0Úuser©rr r Ú 7sÿz/_node_only_used_for_sym_size..)rÚallZusersrr rr Ú_node_only_used_for_sym_size$s  þr) ÚtypingrrZ)torch.ao.quantization.quantizer.quantizerrZtorch.fxrrrrrr r r r Ús