ij.ddlmZddlmZddlmZedGddZedGdd eZed Gd d eZed GddeZ dZ dZ dZ y)) keras_export)QUANTIZATION_MODES)serialization_libz#keras.quantizers.QuantizationConfigc^eZdZdZd dZedZdZedZ e dZ e dZ y) QuantizationConfiga Base class for quantization configs. Subclasses must implement the `mode` property and the `get_config` and `from_config` class methods. Args: weight_quantizer: Quantizer for weights. activation_quantizer: Quantizer for activations. Nc ||_||_yNweight_quantizeractivation_quantizer)selfr r s }/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/quantizers/quantization_config.py__init__zQuantizationConfig.__init__s 0$8!ctd)NzXSubclasses must implement this property. Do not instantiate QuantizationConfig directly.)NotImplementedErrorr s rmodezQuantizationConfig.modes! +  rctj|jtj|jdS)Nr )rserialize_keras_objectr r rs r get_configzQuantizationConfig.get_configs< 1 H H%%!%6$L$L))%   rctj|jd}tj|jd}|||S)Nr r r rdeserialize_keras_objectget)clsconfigr r s r from_configzQuantizationConfig.from_config'sU,EE JJ) *  1II JJ- . -!5  rc:||j |jS|Sr )r rdefaults rweight_quantizer_or_defaultz.QuantizationConfig.weight_quantizer_or_default4s$  &"9"9"E** *rc"| |jS|Sr )r r s ractivation_quantizer_or_defaultz2QuantizationConfig.activation_quantizer_or_default:s  .. .r)NN) __name__ __module__ __qualname____doc__rpropertyrr classmethodr staticmethodr"r$rrrrsa9        rrz'keras.quantizers.Int8QuantizationConfigc4eZdZdZdfd ZedZxZS)Int8QuantizationConfigzInt8 quantization config. Args: weight_quantizer: Quantizer for weights. activation_quantizer: Quantizer for activations. If "default", uses AbsMaxQuantizer with axis=-1. cddlm}|dk(r|}t| |||j<|jj dk7r"t d|jj yy)Nr)AbsMaxQuantizerr!int8zdInt8QuantizationConfig requires a weight_quantizer with output_dtype='int8'. Received: output_dtype=)keras.src.quantizers.quantizersr0superrr output_dtype ValueError)r r r r0 __class__s rrzInt8QuantizationConfig.__init__Ks|C 9 ,#2#4  )+?@  ,$$11V; $$($9$9$F$F#GI< -rcy)Nr1r,rs rrzInt8QuantizationConfig.modeYr)Nr!)r%r&r'r(rr)r __classcell__r6s@rr.r.As! rr.z'keras.quantizers.Int4QuantizationConfigcTeZdZdZ dfd ZedZfdZedZ xZ S)Int4QuantizationConfigaCInt4 quantization config. Args: weight_quantizer: Quantizer for weights. activation_quantizer: Quantizer for activations. If "default", uses AbsMaxQuantizer with axis=-1. block_size: Size of groups along the input dimension for sub-channel quantization. If a positive integer, uses sub-channel quantization with `ceil(input_dim / block_size)` groups. If `None` or `-1`, uses per-channel quantization (one scale per output channel). Default: `128` (sub-channel with 128-element groups). c|dk(rd}t|||||dk7r|dkrtd|||_|duxr|dkD}|jduxs|j du}|r|rtd||jw|jj dk7r"td|jj |jjdk7r"td |jjyy) Nr!rzIblock_size must be None, -1, or a positive integer. Received: block_size=zInt4 sub-channel quantization (block_size > 0) does not support custom quantizers. Either set block_size to None or -1 for per-channel quantization, or remove the custom quantizer arguments. Received: block_size=)izcInt4QuantizationConfig requires a weight_quantizer with value_range=(-8, 7). Received: value_range=r1zdInt4QuantizationConfig requires a weight_quantizer with output_dtype='int8'. Received: output_dtype=)r3rr5 block_sizer r value_ranger4)r r r r@is_sub_channelhas_custom_quantizerr6s rrzInt4QuantizationConfig.__init__msW 9 ,#'  )+?@  !jB&6:?((2|5 %$4/BJN  ! ! - 5((4  2=>HLJ   ,$$00G; ##'#8#8#D#D"EG $$11V; $$($9$9$F$F#GI< -rcy)Nint4r,rs rrzInt4QuantizationConfig.moder8rcBt|}|j|d<|S)Nr@)r3rr@)r rr6s rrz!Int4QuantizationConfig.get_configs##%#| rctj|jd}tj|jd}|jdd}||||S)Nr r r@)r r r@r)rrr r r@s rrz"Int4QuantizationConfig.from_configsh,EE JJ) *  1II JJ- .  ZZ d3 -!5!  r)Nr! r%r&r'r(rr)rrr*rr9r:s@rr<r<^sF & .`   rr<z)keras.quantizers.Float8QuantizationConfigcHeZdZdZfdZedZdZedZ xZ S)Float8QuantizationConfigzFP8 quantization config. FP8 mixed-precision training does not support user defined quantizers. This config is only used to indicate that FP8 mixed-precision training should be used. c&t|ddyr )r3r)r r6s rrz!Float8QuantizationConfig.__init__s t$rcy)Nfloat8r,rs rrzFloat8QuantizationConfig.modesrciSr r,rs rrz#Float8QuantizationConfig.get_configs rc|Sr r,)rrs rrz$Float8QuantizationConfig.from_configs u rrIr:s@rrKrKs:%rrKct|tr|}d}t||k|dk(r t}n|dk(r t }nv|dk(r t }nf|dk(r t d|dk(r t d|t d |t d t|tst d |d t|d |+|j|k7rt d|d|jd|j}t||dk(r)ddl m }t||st dt||dk(r)ddl m }t||st dt||S)aSValidate and resolve quantization config. This function validates the quantization config and resolves the mode. If mode is not provided, it is inferred from the config. If config is not provided, a default config is inferred from the mode. Args: mode: Quantization mode. config: Quantization config. Nr1rErNgptqzGFor GPTQ, you must pass a `GPTQConfig` object in the `config` argument.awqzFFor AWQ, you must pass an `AWQConfig` object in the `config` argument.z*Invalid quantization mode. Received: mode=z9You must provide either `mode` or `config` to `quantize`.zPArgument `config` must be an instance of `QuantizationConfig`. Received: config=z (of type )zContradictory arguments: mode='z' but config.mode=''r) GPTQConfigzOMode 'gptq' requires a valid `config` argument of type `GPTQConfig`. Received: ) AWQConfigzMMode 'awq' requires a valid `config` argument of type `AWQConfig`. Received: ) isinstancestr_validate_moder.r<rKr5rtyper keras.src.quantizers.gptq_configrVkeras.src.quantizers.awq_configrW)rrrVrWs rvalidate_and_resolve_configr^s&#4~ 6>+-F V^+-F X -/F V^% U]%   @GK &"45$$*8:d6l^1F  FKK4/-dV4"KK= +  ;;D4 v~?&*-++/<.:   u}=&),**.v,9  MrcD||tvrtdtd|yy)zValidates quantization mode.Nz+Invalid quantization mode. Expected one of z. Received: mode=)rr5)rs rrZrZ&s= D(:: 122CD6 K  ;rcdddlm}ddlm}|rt |t r |j St |j|r|jj }|dk(rdS|St |j|r;|j|j}t ||r|j }|dk(rdS|Syy)aDetermine the block size for int4 quantization. The block size can be specified either through the `config` argument or through the `dtype_policy` if it is of type `Int4DTypePolicy`. The config argument is usually available when quantizing the layer via the `quantize` method. If the layer was deserialized from a saved model, the block size should be specified in the `dtype_policy`. Args: layer: The layer being quantized. config: An optional configuration object that may contain the `block_size` attribute. Returns: int or None. The determined block size for int4 quantization. Returns `None` or `-1` for per-channel quantization. r)Int4DTypePolicy)DTypePolicyMapr>N) %keras.src.dtype_policies.dtype_policyra)keras.src.dtype_policies.dtype_policy_maprbrXr<r@ dtype_policypath)layerrrarbr@policys rget_block_size_for_layerri/s$FH *V%;<   E&& 8''22 !R't7Z7 E&& 7##EJJ/ fo .**J%+4 ; ;rN) keras.src.api_exportrkeras.src.dtype_policiesrkeras.src.savingrrr.r<rKr^rZrir,rrrms-7.347757t78/9878V /V 9V r9:1;.Sl %r