U Jc @sddlZddlmZddlmZddZddZddZd d Zd d Z ej ej ej j ejd ddZdej ej ej j ejd ddZdS)N)nncCs,t|||}t|ddtj}|S)Nr)torchroundclamptouint8)xscale zero_pointyr b/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py_quantize_per_tensor_cudasrcCs||tj|}|S)N)rrfloat32)r r r r r r r_dequantize_per_tensor_cuda srcCsvtj||jd}t|dD]6}t||ddf||||||ddf<q$t|ddtj}|S)Ndevicerr) rzerossizerrangerrrr)r r r r ir r r_quantize_per_channel_cudas 4rcCsj|tj|j}tj||jd}t|dD]0}||||ddf||||ddf<q4|S)Nrr)rrrcudar zeros_likerr)r r r r rr r r_dequantize_per_channel_cudas .rcsfddt|D}|S)Ncs g|]}tjjjdqS)rdtype)rrrr).0_all_gather_in_listr r "s z+_get_allgather_out_list..)r)r! world_sizeZout_listr r r_get_allgather_out_list!s r$) process_groupbucketreturnc s|dk r |ntjj|dk r$|nt|tj j }|| \}}t ||g j }t|tj|dd}fdd}fdd}|||S)a Applies the ``torch.quantize_per_tensor`` logic to DDP using ``allgather`` protocol. Workers first allgather the scale and zero point of their own ``GradBucket`` prior to the quantization. After all workers have that information, the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's own gradient tensor, and uses ``allgather`` to communicate these accross all workers. The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes and aggregates each quantized gradient tensor locally and returns the mean. .. warning :: This is experimental, and uses ``allgather`` protocol which is considerably slower than ``allreduce`` protocol. It works only with flattened grads. Example:: >>> # xdoctest: +SKIP >>> ddp_model.register_comm_hook(process_group, quantization_pertensor_hook) NTgroupZasync_opcsL|d}t|d|d}tjt||dd}|SNrTr()waitrdist all_gatherr$ get_futurefutall_ranks_s_and_zquantized_tensor) group_to_useranktensorr#r rquantize_and_allgatherTs   z;quantization_pertensor_hook..quantize_and_allgathercs^|d}tj|djtjd}t|D](\}}|t||d|d7}q,|SNrrr+)r,rrrr enumeraterr1Zall_ranks_quantized_tensorZaggregated_dequantized_tensorrr3r2r6r#r rdequantize_and_aggregatees  z=quantization_pertensor_hook..dequantize_and_aggregate)r-r)WORLDr5get_rankrbufferr quantizationZMinMaxObserverrrcalculate_qparamsZ FloatTensorr$r.r/then) r%r&Z myObserverszs_and_zr1r7r=r )r2r4r5r6r#rquantization_pertensor_hook-s$   rGc s|dk r |ntjj|dk r$|nt|tjj d|t |fddd d| j tj j }||\}}t||f j }t|tj|dd}fdd }fd d } ||| S) a Applies the ``torch.quantize_per_channel`` logic to DDP using ``allgather`` protocol. Compared to pertensor, the main motivation of perchannel is for considerably large tensors such as a tensor that contains 6 million elements quantizing per a bucket size of 512 (or 128) elements may significantly increase the resolution. It first splits ``GradBucket`` tensor into multiple chunks (channels) of ``bucket_size`` elements. Then, workers allgather the scales and zero points of their own ``GradBucket`` prior to the quantization. After all workers have that information, the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's own gradient tensor, and uses ``allgather`` to communicate these accross all workers. The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes, flattens, and aggregates each quantized gradient tensor locally and returns the mean. .. warning :: This is experimental, and uses ``allgather`` protocol which is considerably slower than ``allreduce`` protocol. It works only with flattened grads. Example:: >>> # xdoctest: +SKIP >>> ddp_model.register_comm_hook(process_group, quantization_perchannel_hook) NrZconstant)inputpadmodevalueTr(csX|d}t|dddf|dddf}tjt||dd}|Sr*)r,rr-r.r$r/r0)r4r5tensor_in_channelsr#r rr7s  z.quantize_and_allgathercs||d}tj|djtjd}t|D](\}}|t||d|d7}q,t|jd dSr8) r,rrrrr9rflattenrrr:r<r rr=s$   z>quantization_perchannel_hook..dequantize_and_aggregate)r-r)r>r5r?rr@rZ functionalrJlenviewrrrrAZPerChannelMinMaxObserverrBstackr$r.r/rC) r%r&Z bucket_sizeZmyPerChannelObserverZs_chZz_chrFr1r7r=r )r2r4r5r6rNr#rquantization_perchannel_hookws@    rS)rH)rZtorch.distributedZ distributedr-rrrrrr$Z ProcessGroupZ GradBucketZfuturesZFutureZTensorrGrSr r r rs"    K