o i- @s`ddlZddlZddlmZddlmZddlmZmZddl Z ddl m Z ddl m Z ddlmZddlmZddlmZeeZeejeejZed ZeeeeGd d d e eZ! d d e j"de#de#de$fddZ%Gddde j&Z'ifde j&fddZ(GdddZ)eGdddeZ*ee*de j j&de*de j j&fddZ+dS)!N) dataclass)Enum)OptionalUnion)quantize_per_channel_group) AOBaseConfig)'_choose_qparams_and_quantize_affine_hqq) register_quantize_module_handlerz4%(asctime)s - %(name)s - %(levelname)s - %(message)sc@seZdZdZ dZ dZdS)UIntxChooseQParamsAlgorithmz[Variant of quantization algorithm to calculate scale and zero_point for UIntx quantization.min_maxhqqN)__name__ __module__ __qualname____doc__MIN_MAXHQQrrR/home/ubuntu/.local/lib/python3.10/site-packages/torchao/experimental/quant_api.pyr 's r Tvals group_sizenbithas_weight_zerosc Cs |dkr|dks J|rd|d> }d|d>d}nd}d|>d}|j\}}|d|}tj|dd\} } tj|dd\} } | | ||} |sQt| } n |t| | } |||}| |d} | |d} t|| | |||rwtjntj |d}|sd} || | fS)Nr)axis)inputscales zero_points quant_min quant_maxdtyper) shapereshapetorchminmax zeros_likeroundrint8uint8)rrrrsignedqminqmaxnkvmins_vmaxs group_scales group_zeros group_qvalsrrr _quantize9s:         r7csHeZdZ d deejffdd ZejfdefddZ dd Z Z S) UIntxWeightOnlyQuantizedLinearNbiascsBt||_||_|durtj|dd|_dS|dddS)NF requires_gradr9)super__init___pack_weights_op _linear_opnn Parameterr9register_parameter)selfpack_weight_op linear_opr9 __class__rrr=ds z'UIntxWeightOnlyQuantizedLinear.__init__uintx_choose_qparams_algorithmc Cs||_||_|tjkr)t|||d|j|jddd\}}}}|tj }| |}n |tj krBt ||j|jddd\}}}| |}nt d|t j|dd|_t j|dd|_||j|jd} t j| dd|_dS) NrFT)nbitsrr compute_dtypedeviceverbose raw_output)rr,z,Unsupported uintx_choose_qparams_algorithm: r:)rK)rrr rrr"rKtor%r+rr7 ValueErrorr@rA weight_scales weight_zerosr>cpupacked_weights) rCweightsrrrH weight_qvalsrPrQr2rSrrrquantize_and_pack_weightsrs<    z8UIntxWeightOnlyQuantizedLinear.quantize_and_pack_weightscCs|dksJ|dkr'|||j|j|j|j}|jdur%||j}|S|jdd}|jd}|jjd}||d||j|j|j|jjg||R}|jdur\||j}|S)Nrr) dimr?rSrrPrQr9r#r$)rCxoutput lead_shaper0r/rrrforwards>        z&UIntxWeightOnlyQuantizedLinear.forward)N) r rrrr%Tensorr=r rrVr\ __classcell__rrrFrr8cs ,r8modulecCs|d}|d}t|tjrJ|dkr|dksJ|D]E\}}t|tjs.t||q|js;td|dtt t j j d|dt t j j d |d |j d }t|||||j||qdS) NrrrzFUIntxWeightOnlyQuantizedLinear requires contiguous weights for layer 'zF'. Please call .contiguous() on the weight tensor before quantization. _pack_weight_bit_linear_fp_act_ bit_weightrDrEr9) isinstancer@Linearnamed_children)_replace_linear_with_quantized_linear_mpsweight is_contiguousrOr8getattrr%opstorchaor9setattrrV)r_kwargsrrnamechildqlinearrrrris*     ric @sZeZdZddddddeedeejdeedeefddZd e j d e j fd d Z dS) UIntxWeightOnlyLinearQuantizerN)rK precisionbitwidth groupsizerKrurvrwcCs|r |dkr td||_|r|tjtjtjfvrtd||_|dur/d}t d|d|t ddvr:td ||_ |durLd }t d |d|d vrTtd ||_ dS)NmpszHOnly device=mps is currently supported in UIntxWeightOnlyLinearQuantizerz[Only precisions float32, float16 & bfloat16 are supported in UIntxWeightOnlyLinearQuantizerz&bitwidth not specified, defaulting to .rrzDOnly bitwidts 1 to 7 are supported in UIntxWeightOnlyLinearQuantizerz'groupsize not specified, defaulting to  @r{zQOnly groupsizes 32, 64, 128 & 256 are supported in UIntxWeightOnlyLinearQuantizer) NotImplementedErrorrKr%float32float16bfloat16rOruloggerwarningrangervrw)rCrKrurvrwrrrr=s<  z'UIntxWeightOnlyLinearQuantizer.__init__modelreturncCs>|jr ||j}|jr||j}t||j|jdd|S)N)rr)rp)rKrNrurirwrv)rCrrrrquantizes  z'UIntxWeightOnlyLinearQuantizer.quantize) r rrrstrr%r"intr=r@Modulerrrrrrts .rtc@sHeZdZUdZdZeed<dZeed<ej Z e ee fed<ddZ d S) UIntxWeightOnlyConfiga Configuration for applying uintx weight-only asymmetric per-group quantization to linear layers for MPS devices. Args: bitwidth (int): Number of bits for quantization, must be between 1 and 7 inclusive. Default is 4. group_size (int): Group size for quantization. Must be one of [32, 64, 128, 256]. Default is 128. uintx_choose_qparams_algorithm (Union[UIntxChooseQParamsAlgorithm, str]): Algorithm for choosing quantization parameters. Options: - "min_max" (default): Simple min-max scaling - "hqq": Half-Quadratic Quantization for better accuracy ryrvr{rrHcCsZ|jtddvrtd|j|jdvrtd|jt|jtr+t|j|_dSdS)Nrrz0bitwidth must be between 1 and 7 inclusive, got r|z2group_size must be one of [32, 64, 128, 256], got )rvrrOrrfrHrr )rCrrr __post_init__,s     z#UIntxWeightOnlyConfig.__post_init__N)r rrrrvr__annotations__rr rrHrrrrrrrrs    rconfigrcCsn|j}|j}|j}|jstdtttj j d|dttj j d|d|j d}| |j||||S)NzUIntxWeightOnlyQuantizedLinear requires contiguous weights. Please call .contiguous() on the weight tensor before quantization.rarbrcrdre) rvrrHrjrkrOr8rlr%rmrnr9rV)r_rrrrHrsrrr _uintx_weight_only_mps_transform<s   r)T),loggingsys dataclassesrenumrtypingrrr%torch.nnr@$torch.ao.quantization.fx._decomposedrtorchao.core.configr%torchao.quantization.quant_primitivesr%torchao.quantization.transform_moduler getLoggerr rsetLevelWARNING StreamHandlerstdouthandler Formatter formatter setFormatter addHandlerrr r]rboolr7rr8rirtrrrrrrsR              *Y>&