Vho9ddlZddlmZddlmZddlmZmZddlmZm Z gdZ GddejjZ Gd d ejjZ Gd d e Zy) N)Tensor)ListOptional)_hide_packed_params_repr_quantize_weight)EmbeddingPackedParams Embedding EmbeddingBagceZdZdZej ffd ZejjdejddfdZ ejjdZ dZ fd Z fd Zd ZxZS) r rct|||_|jtjtj fvrtj |tj}tj|tj}tj||g||d|j}|j|ytd|)Ndtyperscales zero_pointsaxisrzSUnsupported dtype on quantized embedding! Supports quint8 and quint4x2. Got dtype: ) super__init__rtorchquint8quint4x2onesfloatzeros#_empty_per_channel_affine_quantized set_weightNotImplementedError)selfnum_embeddings embedding_dimrrrwq __class__s [/home/dcms/DCMS/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.pyrzEmbeddingPackedParams.__init__s  ::%,,7 7ZZekkBF++nEKKHK::/'jj B OOB %efkelm weightreturnNc|jtjtjfvr/tjj j ||_ytd)NzPUnsupported dtype for quantized embedding prepack! Supports quint8 and quint4x2.) rrrrops quantizedembedding_bag_prepack_packed_weightr)rr&s r$rz EmbeddingPackedParams.set_weight$sH ::%,,7 7"'))"5"5"K"KF"SD %b r%c|jtjtjfvr3tjj j |jStd)NzOUnsupported dtype for quantized embedding unpack! Supports quint8 and quint4x2.) rrrrr)r*embedding_bag_unpackr,rrs r$_weightzEmbeddingPackedParams._weight-sL ::%,,7 799&&;;Ds= #KC(, FW$%15 F--.r%c ||dz|_|j|dz||dz}|j|dz|j|t ||||d|||y)Nrr,F)rpoprr_load_from_state_dict) r state_dictr9local_metadatastrict missing_keysunexpected_keys error_msgsr&r#s r$r=z+EmbeddingPackedParams._load_from_state_dictCsw  01 v'(F%556v 001  %        r%c>|jjSr2)r0__repr__r/s r$rEzEmbeddingPackedParams.__repr__^s||~&&((r%)__name__ __module__ __qualname___versionrrrjitexportrrr0r5r7r=rE __classcell__r#s@r$r r swH>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12) >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8]) >>> output = m(indices) >>> print(output.size()) torch.Size([9, 12]) rN@Fr r! padding_idxmax_norm norm_typescale_grad_by_freqsparser0r'c t |||_||_| |_|ut j |t j} t j|t j} t j||g| | dt j} n#t|j||gk(sJd|} t||| |_|jj| y)Nrrrz?Shape of weight does not match num_embeddings and embedding_dim)rrr r!rrrrrrrlistshaper _packed_paramsr)rr r!rPrQrRrSrTr0rrrqweightr#s r$rzEmbedding.__init__ys ,* ?ZZekkBF++nEKKHK??/'ll G &+ QQ QG3 M5  &&w/r%indicesc4|jtjk(r>tjjj |j j|Stjjj|j j|Sr2) rrrr)r*embedding_4bitrXr,embedding_byte)rrZs r$r5zEmbedding.forwardsp :: '99&&55##22G 99&&55##22G r%cy)NQuantizedEmbeddingr3r/s r$ _get_namezEmbedding._get_names#r%c"t|tSr2)rr r/s r$rEzEmbedding.__repr__s'.CDDr%c d|jd|jd|jjd|j j }|S)Nznum_embeddings=z, embedding_dim=z, dtype=z , qscheme=)r r!rXrr&qscheme)rextra_repr_strs r$ extra_reprzEmbedding.extra_reprs_d1122B4CUCUBVW((../z$++-:O:O:Q9R T  r%wc:|jj|yr2)rXr)rrfs r$rzEmbedding.set_weights &&q)r%c6|jjSr2)rXr0r/s r$r&zEmbedding.weights""**,,r%cLt|drt|tjjj j k(sSJd|jzdzdztjjj j jz|j}nt|tj k(s2Jd|jzdztj jzt|dsJddd l m }|j1|jj|jj}n|j}|j}|jtjk(}|sJd |tj k(s|tj"k(s Jd |||jt%|jj'|}t |j(|j*}|j-||S) zCreate a quantized embedding module from a float module Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by user weight_fake_quantnnq.z .from_float zwith fake quant only works for .from_float only works for qconfigz6Embedding input float module must have qconfig definedr!float_qparams_weight_only_qconfigzPEmbedding quantization is only supported with float_qparams_weight_only_qconfig.zSThe only supported dtype for nnq.Embedding is torch.quint8 and torch.quint4x2, got )hasattrtyperaonnqatr rFrjtorch.ao.quantizationrormr&rrc per_channel_affine_float_qparamsrrrrr r!r) clsmoduse_precomputed_fake_quantweight_observerroris_float_qparams_qconfigrY qembeddings r$ from_floatzEmbedding.from_floats 3+ ,9  9 99 ,, !44((++//++44 5 9"33O9 , ,,/0,,''( , Y HG H P{{&3;;+=+=+I"%++"4"4"6"C"J"J"L%%  # #u'M'M M ! % ^ ] ^ $ U\\ !Uenn%< i `af`g h i <  #"3::#3#3#5Gs1133D3DE g&r%c ||j|j|j|j|j|j |j |j|j }|Sr2) r r!rPrQrRrSrTget_quantized_weight weight_dtype)rw ref_embeddingr|s r$from_referencezEmbedding.from_referencesh  ( (  ' '  % %  " "  # #  , ,   . . 0  & &  r%F)rFrGrH__doc__rIrrintrrboolrrr5r`rErerr& classmethodr}rrLrMs@r$r r bs(H &*$(#($(ll%0%0%0c] %0 5/ %0  %0!%0%0&!%0 %0Nv&$E*ELL*T*-44l  r%r ceZdZdZdZdddddddej fdeded ee d e d e d e d e dee de ddffd Z dde dee dee dee de f dZdZeddZedZxZS)r a A quantized EmbeddingBag module with quantized packed weights as inputs. We adopt the same interface as `torch.nn.EmbeddingBag`, please see https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html for documentation. Similar to :class:`~torch.nn.EmbeddingBag`, attributes will be randomly initialized at module creation time and will be overwritten later Attributes: weight (Tensor): the non-learnable quantized weights of the module of shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`. Examples:: >>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum') >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3]) >>> offsets = torch.tensor([0, 19, 20, 28, 28, 32]) >>> output = m(indices, offsets) >>> print(output.size()) torch.Size([5, 12]) rNrOFsumr r!rQrRrSmoderTr0include_last_offsetr'c dt ||||| ||_d|_| |_| |_y)N)r0rF)rrrpruned_weightsrr) rr r!rQrRrSrrTr0rrr#s r$rzEmbeddingBag.__init__s9 uU ##6  r%rZoffsetsper_sample_weightscompressed_indices_mappingc |jtjk(rYtjjj |j j||dd|j|||j Stjjj|j j||dd|j|||j S)NFr) rrrr)r*embedding_bag_4bitrXr,rrembedding_bag_byte)rrZrrrs r$r5zEmbeddingBag.forward1s :: '99&&99##22##"*((  99&&99##22##"*((  r%cy)NQuantizedEmbeddingBagr3r/s r$r`zEmbeddingBag._get_nameQs&r%c t|dr |j}nt|tjk(s2Jd|j zdztjj zt|dsJdddlm}|j1|jj|jj}n|j}|j}|jtjk(}|sJd|tjk(s|tjk(s Jd |||jt!|jj#|}t |j$|j&|j(|j*|j,|j.|j0|j2| }|j5||S) zCreate a quantized embedding_bag module from a float module Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by user rjrkrlrmz9EmbeddingBag input float module must have qconfig definedrrnzSEmbeddingBag quantization is only supported with float_qparams_weight_only_qconfig.zVThe only supported dtype for nnq.EmbeddingBag is torch.quint8 and torch.quint4x2, got )rQrRrSrrTrr)rprjrqrsr rFtorch.ao.quantization.qconfigrormr&rrcrrvrrrrr r!rQrRrSrrTrr) rwrxryrzrorr{rYqembedding_bags r$r}zEmbeddingBag.from_floatTs 3+ ,!33O9/ ,,/0//**+ / Y KJ K X{{&3;;+=+=+I"%++"4"4"6"C"J"J"L%%  # #u'M'M M ! % a ` a $ U\\ !Uenn%< l cdicj k l <  #"3::#3#3#5G&      \\mm"55:: # 7 7   !!'*r%c ||j|j|j|j|j|j |j |j|j|j }|Sr2) r r!rQrRrSrrTrrr)rwref_embedding_bagrs r$rzEmbeddingBag.from_referencesq  , ,  + +  & &  ' '  0 0  " "  $ $  2 2 4  1 1  * *  r%)NNNr)rFrGrHrrIrrrrrrstrrrr5r`rr}rrLrMs@r$r r s*H %)#($($)ll5/    ! &!" .%)/37; &!%V,  %-V$4   @'77r  r%r )rtorch.nnrsrtorch._jit_internalrrutilsrr__all__Moduler r r r3r%r$rsZ .= AQ)EHHOOQ)h``FW9Wr%