+ "ݳjR^RIHtHtHtHt^RIt^RIHt^RI H t H t ^RI H t Rt]PP!4;'d]P"P$RJt]P(]P*]P,]P.0t]P2!4RRRll4t]P2!4RR] P6R3R R ll4t]P2!4RR] P6RR3R R ll4tR RltRRltRRlt Rt!RRlt"RRlt#RRlt$R#))IterableOptionalTupleUnionN)AsyncCollectiveTensor all_reduce)ScalingGranularityg-q=FcdV^8dQhR\PR\PR\/#)amax float8_dtyperound_scales_to_power_of_2)torchTensordtypebool)formats"\/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchao/float8/float8_utils.py __annotate__r s. ,,++!%c^VP\P4pV\9db\P!V4P \P !V\R7, pVP\P4pM\RV 24hV'd \V4pV#)zConverts the amax value of a tensor to the fp8 scale. Args: amax: The amax value of the tensor. float8_dtype: The float8 dtype. round_scales_to_power_of_2: if true, round scaling factor down to the nearest power of 2. )minUnsupported float8_dtype: ) torfloat64 FP8_TYPESfinfomaxclampEPSfloat32 ValueError_round_scale_down_to_power_of_2)r r rress&&& r amax_to_scaler%sy 775== !Dy kk,'++ekk$C.HHffU]]#5l^DEE!-c2 Jrc V^8dQhR\PR\R\R\\ ,R\P/#)r x reduce_amaxscaling_granularity axiswise_dimreturn)rrrr rint)rs"rrr9sF ||,  3-   \\ rc\V\PJd,\P!\P!V44pMSV\P JgQR4hVfQR4h\P !\P!V4VRR7pV'd\P!4'dwVeVP4MRpVf(\\\P!444MTp\VRV4p\V\4'dVP!4pV#) unsupportedNT)dimkeepdimMAX)r TENSORWISErrabsAXISWISEr distis_initialized get_grouplistrangeget_world_sizer isinstancerwait)r'r( device_meshr)r*r pggroups&&&&& rtensor_to_amaxr@8s0;;;yy1&"&8&A&AAP=PA'66'zz%))A,L$G t**,,(3(?[ " " $T68jU4..012b$u- d1 2 299;D KrcV^8dQhR\PR\PR\R\R\ \ ,R\R\P/#)r hp_tensorr r(r)r*rr+)rrrrr rr,)rs"rrrVs^||++ ,  3- !% \\rc:\VVVVV4p\WqVR7#)a Compute scaling factor for the given high precision tensor. Args: hp_tensor: high precision tensor float8_dtype: the float8 dtype to use reduce_amax: whether to reduce the max(abs(hp_tensor)) value across distributed ranks scaling_granularity: Defines the scaling granularity axiswise_dim: if axiswise granularity is used, defines the dim to scale across round_scales_to_power_of_2: if true, round scaling factor down to the nearest power of 2. )r)r@r%)rBr r(r=r)r*rr s&&&&&&& rtensor_to_scalerDUs2*   D  7Q rcXV^8dQhR\PR\P/#)r r'r )rrr)rs"rrrvs&FF FEKKFrcV\9dF\P!V4PpVP V)VR7pVP V4#\ RV 24h)aConverts a tensor to a saturated fp8 tensor. Note: In PyTorch 2.11 and older, eager casts to FP8 did not saturate finite overflow values. So we saturate in this function. In compile, it was satured and so this extra clamp is a no-op in accuracy and regresses performance. In PyTorch 2.12 and newer, eager mode now saturates and this clamp is no longer needed. TODO: this explicit clamp can be removed after torchao stops supporting PyTorch 2.11. )rrr)rrrrrrr")r'r max_values&& rto_fp8_saturatedrHvsV y KK -11 GG G 2ttL!!5l^DEErcxV^8dQhR\PR\PR\P/#)r r'yr+rr)rs"rrrs- % %U\\ %ell %u|| %rc\PPV4p\PPW, 4p^\P!W#, 4,#)zComputes the error between two tensors in dB. For more details see: https://en.wikipedia.org/wiki/Signal-to-noise_ratio Args: x: The original tensor. y: The tensor to compare to the original tensor. )rlinalg vector_normlog10)r'rJPsPns&& r compute_errorrRsD  ! !! $B  ! !!% (B  BG$ $$rc~V^8dQhR\PR\PR\\R3,/#)r tensorr r+.)rrrrr,)rs"rrrs4 LL(-  38_rctV\9d"\P!V4PpM\ RV 24hVP P VPR7p\P!V4V8HP4P4pV^8HP4P4pWT3#)zCalculate FP8 tensor stats Args: tensor: The tensor to calculate stats for. float8_dtype: The float8 dtype. Returns: A tuple containing the number of zeros and the number of max values. r)r) rrrrr"_datar _orig_dtyper3sumitem)rTr FP8_MAXtensor_orig_typenum_maxnum_zeros&& rfp8_tensor_statisticsr^sy ++l+//5l^DEE||V-?-?@yy)*g5::<AACG A%**,113H  rc~\V4^8XgQR4hV^,V^,8;'d V^,^8H#)r z%is_row_major only supports 2D tensors)len)strides&r is_row_majorrbs= v;! DDD  !9vay 3 3VAY!^3rc<V^8dQhR\R\R\/#)r sizealignment_valuer+)r,)rs"rrrs&CCSC3C3Crc>^V^, V,,V,#)aY Returns the minimum alignment value that is greater than or equal to the given size. Args: size: The size of the data to be aligned. alignment_value: The alignment value to be used. Returns: int: The minimum alignment value that is greater than or equal to the given size. Usage: ``` >>> _get_min_alignment(10, 8) 16 ``` )rdres&&r_get_min_alignmentrhs" $(. /? BBrcV^8dQhR\PR\\\\,3,R\P/#)r rTdimsr+)rrrr,r)rs"rrrs?%G%G LL%G %c8C=&8 9%G \\%GrcNVP4^8XgQhVPwr#\V\4'dV3p^V9d \ V^4MTp^V9d \ V^4MTpWB, pWS, p\ P PPV^V^V34#)a Pads a 2D tensor with zeros to ensure that its dimensions are multiples of 16, which is required `torch._scaled_mm` Args: tensor: The tensor to pad. dims: Dimensions to pad. Returns: torch.Tensor: The padded tensor. Usage: ``` >>> pad_tensor_for_matmul(torch.randn((10, 10)), dims=0).shape torch.Size([16, 10]) >>> pad_tensor_for_matmul(torch.randn((10, 10)), dims=1).shape torch.Size([10, 16]) >>> pad_tensor_for_matmul(torch.randn((10, 10)), dims=(0, 1)).shape torch.Size([16, 16]) ``` ) r/shaper;r,rhrnn functionalpad)rTrjdim1dim2 dim1_aligned dim2_alignedpad_dim1pad_dim2s&& rpad_tensor_for_matmulrvs. ::<1  JD$w459%dB/$L349%dB/$L"H"H 88   " "6AxH+E FFrc8V^8dQhR\P/#)r scalerK)rs"rrrs665<<6rcVP\P8XgQR4h\P!\P!\P !V444#)zscale must be float32 tensor)rrr!exp2floorlog2)rxs&rr#r#s? ;;%-- 'G)GG ' ::ekk%**U"34 55r)F)%typingrrrrrtorch.distributed distributedr5)torch.distributed._functional_collectivesrrtorchao.float8.configr r cuda is_availableversionhipIS_ROCM float8_e4m3fn float8_e5m2float8_e4m3fnuzfloat8_e5m2fnuzrno_gradr%r2r@rDrHrRr^rbrhrvr#rgrrrs43 W4  ** ! ! # E E (9(9(E       0.@.K.K"& 8.@.K.K"&',@F0 %,4 C(%GP6r