o i?(@sdZddlZddlmZmZmZmZmZddlZddl m Z m Z ddl m Z ddlmZmZejZGdddeZd ed ed ed eeeffd dZdejdeedffddZ   d7d eded ededejdeedeeded efddZdejdeeded ed!ed"ed ejfd#d$Zd%ejd efd&d'Zd%ejd efd(d)Zd%ejd efd*d+Zd%ejd efd,d-Zd.ee ee e fee fd efd/d0Zd1eee ee e fee fd ee e ffd2d3Z d4ee e fd dfd5d6Z!dS)8z; Defines an nn module designed to be used during inference N)List NamedTupleOptionalTupleUnion) is_row_majorpad_tensor_for_matmul)FP8Granularity)is_MI300is_sm_at_least_89c@s6eZdZUdZdZeed<dZeed<dZeed<dS)Float8MMConfiga Configuration for the scaled_mm in the forward and backward pass. Attributes: emulate (bool): Whether to emulate the matmuls in fp32. use_fast_accum (bool): Whether to use the fast-accumulation option for scaled_mm. pad_inner_dim (bool): Whether to pad the inner dimension of a and b with 0s. This is needed for matmuls not aligned to 16. Femulateuse_fast_accum pad_inner_dimN) __name__ __module__ __qualname____doc__r bool__annotations__rrrrL/home/ubuntu/.local/lib/python3.10/site-packages/torchao/float8/inference.pyr s  r a_datab_datascaled_mm_configreturncCs|jr)|d|dksJd|dd|dt|dd}t|dd}t|s3|}t|rA|}||fS)aPreprocess the inner fp8 data tensors for admmm Args: a_data: Input tensor A. b_data: Input tensor B. scaled_mm_config: Configuration for _scaled_mm. Returns: Preprocessed tensors A and B in the format for _scaled_mm. rz"Inner dims must match for mm, got z and )dims)rsizerrstride contiguoust)rrrrrrpreprocess_data)s     r" input_scale input_shape.cCsD|dkr |ddS|d}|dkr |d|jd}|S)z:Ensures input tensor is correctly formatted for _scaled_mmr)numelreshape unsqueezedimshape)r#r$rrrpreprocess_scaleCs    r,Fa_scaleb_scale output_dtype output_scalebiasrc CsL|tjkr|durtj|||||||d}||Stj||||||||dS)z This is the unwrapped version of addmm_float8, which does not take in Float8TrainingTensors as inputs. This is used to standardize the logic between subclassed and non subclassed versions of the linear module. N)scale_ascale_b scale_result out_dtyper)r2r3r1r4r5r)torchfloat32 _scaled_mm) rr-rr.r/r0r1routputrrr addmm_float8_unwrapped_inferenceUs* r:scale data_shaper*startendstepc stjj}jkr|j||||StfddttD}|t|kr+S||}|dkr=|j||||S|durE||nd} |durS||d|nd} |dkr]t d|j|| | dS)z Slice the scale tensor appropriately based on the data tensor slicing. This function calculates how the scale should be sliced when the data tensor is sliced along a given dimension, taking into account the block structure. c3s"|] }|j|VqdS)N)r+.0ir<r;rr s z-_slice_scale_for_dimension..rNz;Slicing with step > 1 is not implemented for scale tensors.) r6opsatenr+sliceTensortuplerangelenNotImplementedError) r;r<r*r=r>r?rF block_sizesblock_size_for_dim scale_start scale_endrrCr_slice_scale_for_dimension}s&   rQxcCs8t|ds Jdt|jd|d|jdfkS)~Checks if a quantized tensor is rowwise scaled Args: x: quantized tensor (should have `block_size` attribute) block_size.Expecting input to have `block_size` attribute)rrr%)hasattrrIrTr*r+rRrrr_is_rowwise_scaleds&rXcs.tds JdtfddtjDS)rSrTrUc3s2|]}j|dkpj|j|kVqdS)r%N)rTr+r@rWrrrDs" z(_is_tensorwise_scaled..)rVallrJndimrWrrWr_is_tensorwise_scaleds r[cCsFt|ds Jd|j}t|dko"t|dddko"|ddkS)zChecks if a quantized tensor is scaled with a block size of 1x128 Args: x: quantized tensor (should have `block_size` attribute) rTrUr&Nr%r)rVrTrKmathprodrRbrrr_is_1_128_scaleds.racCs<t|ds Jd|j}t|dko|ddko|ddkS)zChecks if a quantized tensor is scaled with a block size of 128x128 Args: x: quantized tensor (should have `block_size` attribute) rTrUr&rr\r)rVrTrKr_rrr_is_128_128_scaleds$rbgcCs@ddlm}t|dko|d|ddgko|d|ddgkS)Nr)PerBlockr&rr\) torchao.quantization.granularityrdrK)rcrdrrr!_granularity_is_a_1_128_w_128_128s 4rf granularitycCsddlm}m}d}|dur||f}|St|||fr#||f}|St|ttfrwt|dkrwt|d|o=t|d|}t|d|oKt|d|}t|}|s^|s^|s^td|dt|dt |dsqtd|dt|}|Std|d) NrPerRow PerTensorr&rzUnsupported granularity types: .zEDifferent granularities for activation and weight are not supported: z#Invalid granularity specification: ) rerirj isinstancerIlistrKrf ValueErrortype)rgrirjprocessed_granularity is_per_tensor is_per_rowis_a_1_128_w_128_128rrr_normalize_granularitys2    rt granularitiescCsddlm}m}t|d|ot|d|}t|d|o#t|d|}t|}|s,|rFtjs@tjr9t sDt sBJddSdSdS|rQt sOJddSt d|d) a9 Validate that the hardware supports the requested granularities. Args: granularities: Tuple of (activation_granularity, weight_granularity) Raises: AssertionError: If hardware doesn't support the requested granularity ValueError: If invalid granularity type is provided rrhruUFloat8 dynamic quantization requires CUDA compute capability ≥8.9 or MI300+ or XPU.u[Float8 1x128 activation and 128x128 weight scaling requires CUDA compute capability ≥8.9.zInvalid granularities rkN) rerirjrlrfr6xpu is_availablecudar r rn)rurirjrqrrrsrrr_check_hardware_supports.  ry)NNF)"rr]typingrrrrrr6torchao.float8.float8_utilsrrtorchao.float8.typesr torchao.utilsr r rHr r"intr,dtyperr:rQrXr[rarbrmrfrtryrrrrs    ( 3         (