o ̳i= @s8ddlmZmZddlmZddlmZmZzddlm Z Wne y-ddlm Z Ynwddl m Z mZmZzddlmZmZddlmZmZmZmZWne ygdd lmZmZmZmZmZmZYnwgd Zd Zzdd l mZWn e yd ZYnwiZiZiZGdddZdee<dee<eed<eed<GdddZ dee <dee<eed<eed<GdddeZ!eZ"eZ#dee!<e"ed<e#ed<GdddeZ$eZ%eZ&dee$<e%ed<e&ed<deedee'fddZ(d e'defd!d"Z)d e'defd#d$Z* % %d,d&ej+d'ed(d)ed(dd%fd*d+Z,d%S)-)CallableOptional)nn) LoRALinear QATLoRALinear)TensorCoreTiledLayout)TensorCoreTiledLayoutType)int4_weight_only#int8_dynamic_activation_int4_weight quantize_)Int4WeightOnlyQATQuantizer Int8DynActInt4WeightQATQuantizer)disable_4w_fake_quantdisable_8da4w_fake_quantenable_4w_fake_quantenable_8da4w_fake_quant)rrrrr r )get_quantizer_modeInt4WeightOnlyQuantizerr $Int4WeightOnlyQATQuantizerModuleSwapInt8DynActInt4WeightQuantizerr *Int8DynActInt4WeightQATQuantizerModuleSwapT)qatFc@s(eZdZdZd defddZddZdS) rz Quantizer for applying int8 per token dynamic activation + int4 per group weight quantization to linear layers in the model.  groupsizecCs ||_dSN)r)selfrrS/home/ubuntu/.local/lib/python3.10/site-packages/torchtune/training/quantization.py__init__Ss z&Int8DynActInt4WeightQuantizer.__init__cCst|j}t|||Sr)r rr )rmodel quantize_fnrrrquantizeVs  z&Int8DynActInt4WeightQuantizer.quantizeN)r__name__ __module__ __qualname____doc__intrr!rrrrrMs r8da4wz 8da4w-qatc@s,eZdZdZd dedefddZdd Zd S) rz Quantizer for applying int4 per group weight only quantization to linear layers in the model using the efficient tinygemm kernel. r inner_k_tilescCs||_||_dSr)rr+)rrr+rrrrms z Int4WeightOnlyQuantizer.__init__cCs$t|j}t|j|}t|||Sr)rr+r rr )rr layout_typer rrrr!qs   z Int4WeightOnlyQuantizer.quantizeN)r)r*r"rrrrrgs r4wz4w-qatc@ eZdZdS)rNr#r$r%rrrrrrz4w-qat-module-swapc@r.)rNr/rrrrrr0rz8da4w-qat-module-swap quantizerreturncCstt|dS)aGiven a quantizer object, returns a string that specifies the type of quantization. For example, in the case of int4 weight only quantization, we'll return "4w". If the quantizer is not recognized as a known quantizer, we'll return None. Currently supported: - :class:`~torchtune.training.quantization.Int8DynActInt4WeightQuantizer`: "8da4w" - :class:`~torchtune.training.quantization.Int4WeightOnlyQuantizer`: "4w" - :class:`~torchao.quantization.qat.Int8DynActInt4WeightQATQuantizer`: "8da4w-qat" - :class:`~torchao.quantization.qat.Int4WeightOnlyQATQuantizer`: "4w-qat" Args: quantizer (Optional[Callable]): A callable object that implements the `quantize` method. Returns: Optional[str]: The quantization mode. N)_quantizer_to_modegettype)r1rrrrsrquantizer_modecC t|dS)zGiven a quantizer mode, return the corresponding function for disabling fake quantize in a model prepared by the quantizer. If the quantizer is not recognized as a known QAT quantizer, return None. N)%_quantizer_mode_to_disable_fake_quantr4r6rrr_get_disable_fake_quant r:cCr7)zGiven a quantizer mode, return the corresponding function for enabling fake quantize in a model prepared by the quantizer. If the quantizer is not recognized as a known QAT quantizer, return None. N)$_quantizer_mode_to_enable_fake_quantr4r9rrr_get_enable_fake_quantr;r=Nmoduleactivation_qat_configFakeQuantizeConfigweight_qat_configcCsH|D]\}}t|trt|||}t|||qt|||qdS)a` Swap all `LoRALinear` in the model with `QATLoRALinear`. This is used for combining QAT + LoRA during finetuning. The resulting linear layers will apply the following transformation instead: x -> fake_quantize(W_frozen) @ fake_quantize(x) + BAx Fake quantization here refers to simulating the quantization numerics without actual dtype casting, with the goal of providing improved accuracies when the model is ultimately quantized after finetuning. Args: module (nn.Module): The model to swap linear layers on activation_qat_config (Optional[FakeQuantizeConfig]): The config for specifying how to fake quantize input activations in the base linear layer weight_qat_config (Optional[FakeQuantizeConfig]): The config for specifying how to fake quantize base linear weights N)named_children isinstancerrfrom_lora_linearsetattrswap_lora_linear_with_qat)r>r?rAnamechild new_linearrrrrFs rF)NN)-typingrrtorchrtorchtune.modules.peft.lorarrtorchao.dtypesr ImportErrorrtorchao.quantizationr r r torchao.quantization.qatr r torchao.quantization.qat.linearrrrr"torchao.quantization.prototype.qat__all___torchao_0_7_supportedrr3r8r<rrr!disable_4w_fake_quant_module_swap enable_4w_fake_quant_module_swapr$disable_8da4w_fake_quant_module_swap#enable_8da4w_fake_quant_module_swapstrrr:r=ModulerFrrrrs   $