U d x@s ddlZddlmZmZmZmZddlZddlmZddlm Z ddl m Z ddl mZdd lmZdd lmZdd lmZdd lmZdd lmZGddde ZGddde ZGddde ZGddde ZGddde ZddZeeegefdddZ dS)N)OptionalAnyUnionCallable)Tensor) functional)Module)MultiheadAttention) ModuleList)xavier_uniform_)Dropout)Linear) LayerNormcseZdZdZddddddejdddd d ddfeeeeeeee e e ge ffe e e e eeedd fd d Zde e e e e e e e e e e e e e e d ddZeee dddZddZZS) Transformera<A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters. Args: d_model: the number of expected features in the encoder/decoder inputs (default=512). nhead: the number of heads in the multiheadattention models (default=8). num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6). num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of encoder/decoder intermediate layer, can be a string ("relu" or "gelu") or a unary callable. Default: relu custom_encoder: custom encoder (default=None). custom_decoder: custom decoder (default=None). layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False`` (seq, batch, feature). norm_first: if ``True``, encoder and decoder layers will perform LayerNorms before other attention and feedforward operations, otherwise after. Default: ``False`` (after). Examples:: >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12) >>> src = torch.rand((10, 32, 512)) >>> tgt = torch.rand((20, 32, 512)) >>> out = transformer_model(src, tgt) Note: A full example to apply nn.Transformer module for the word language model is available in https://github.com/pytorch/examples/tree/master/word_language_model i皙?Nh㈵>F) d_modelnheadnum_encoder_layersnum_decoder_layersdim_feedforwarddropout activationcustom_encodercustom_decoderlayer_norm_eps batch_first norm_firstreturnc s| |d}tt||dk r(||_n>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask) r z-the batch number of src and tgt must be equalrz:the feature number of src and tgt must be equal to d_model)maskr>r<r=r?r@)dimr!size RuntimeErrorrr*r-) r1r9r:r;r<r=r>r?r@Z is_batchedmemoryoutputr7r7r8forwardUs3    zTransformer.forward)szr#cCstjt||ftdddS)zGenerate a square mask for the sequence. The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0). z-infr )Zdiagonal)torchZtriufullfloat)rKr7r7r8generate_square_subsequent_masksz+Transformer.generate_square_subsequent_maskcCs&|D]}|dkrt|qdS)z-Initiate parameters in the transformer model.r N) parametersrEr )r1pr7r7r8r0s  zTransformer._reset_parameters)NNNNNN)__name__ __module__ __qualname____doc__FreluintrNrstrrrrrboolr)rJ staticmethodrOr0 __classcell__r7r7r5r8rsP"" BrcsFeZdZdZdgZd fdd Zd eeeeeeddd ZZ S) r,a3TransformerEncoder is a stack of N encoder layers Args: encoder_layer: an instance of the TransformerEncoderLayer() class (required). num_layers: the number of sub-encoder-layers in the encoder (required). norm: the layer normalization component (optional). enable_nested_tensor: if True, input will automatically convert to nested tensor (and convert back on output). This will improve the overall performance of TransformerEncoder when padding rate is high. Default: ``False`` (disabled). Examples:: >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6) >>> src = torch.rand(10, 32, 512) >>> out = transformer_encoder(src) normNFcs0tt|t|||_||_||_||_dSN)r(r,r) _get_cloneslayers num_layersr]enable_nested_tensor)r1r3rar]rbr5r7r8r)s  zTransformerEncoder.__init__)r9rCr>r#c Cs|}d}|jd}t|tjjr<|js<|js<|jjr<|jj r<|j r<|j j |j j kr<|dkr<|jr<|dk r<|js<|dkr<||jj|jj|jjj|jjj|j j|j j|j j|j j|jj|jj|jj|jjf }tj|ssz.TransformerEncoder.forward..cpuT)r;)r;r>g)#r` isinstancerLnnr+r"training self_attnr!_qkv_same_embed_dimactivation_relu_or_gelunorm1r'norm2rErb is_nestedin_proj_weight in_proj_biasout_projweightbiaslinear1linear2 overrideshas_torch_functionis_grad_enabledallis_cudarYr%Z_nested_tensor_from_maskZ logical_notZto_padded_tensorr]) r1r9rCr>rIZconvert_to_nestedZ first_layer tensor_argsmodr7r7r8rJs\       zTransformerEncoder.forward)NF)NN rRrSrTrU __constants__r)rrrJr\r7r7r5r8r,sr,c sTeZdZdZdgZd fdd Zd eeeeeeeeeeedddZZ S) r/a_TransformerDecoder is a stack of N decoder layers Args: decoder_layer: an instance of the TransformerDecoderLayer() class (required). num_layers: the number of sub-decoder-layers in the decoder (required). norm: the layer normalization component (optional). Examples:: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = transformer_decoder(tgt, memory) r]Ncs*tt|t|||_||_||_dSr^)r(r/r)r_r`rar])r1r4rar]r5r7r8r) s zTransformerDecoder.__init__r:rHr<r=r?r@r#c Cs<|}|jD]}|||||||d}q |jdk r8||}|S)aMPass the inputs (and mask) through the decoder layer in turn. Args: tgt: the sequence to the decoder (required). memory: the sequence from the last layer of the encoder (required). tgt_mask: the mask for the tgt sequence (optional). memory_mask: the mask for the memory sequence (optional). tgt_key_padding_mask: the mask for the tgt keys per batch (optional). memory_key_padding_mask: the mask for the memory keys per batch (optional). Shape: see the docs in Transformer class. rDN)r`r]) r1r:rHr<r=r?r@rIrr7r7r8rJs   zTransformerDecoder.forward)N)NNNNrr7r7r5r8r/s r/c seZdZdZddgZddejdddddfeeeee e e e ge ffee e dd fd d Zfd d Zde ee ee e dddZe ee ee e dddZe e dddZZS)r+a TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application. Args: d_model: the number of expected features in the input (required). nhead: the number of heads in the multiheadattention models (required). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of the intermediate layer, can be a string ("relu" or "gelu") or a unary callable. Default: relu layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False`` (seq, batch, feature). norm_first: if ``True``, layer norm is done prior to attention and feedforward operations, respectivaly. Otherwise it's done after. Default: ``False`` (after). Examples:: >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) >>> src = torch.rand(10, 32, 512) >>> out = encoder_layer(src) Alternatively, when ``batch_first`` is ``True``: >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True) >>> src = torch.rand(32, 10, 512) >>> out = encoder_layer(src) Fast path: forward() will use a special optimized implementation if all of the following conditions are met: - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad`` - training is disabled (using ``.eval()``) - batch_first is ``True`` and the input is batched (i.e., ``src.dim() == 3``) - norm_first is ``False`` (this restriction may be loosened in the future) - activation is one of: ``"relu"``, ``"gelu"``, ``torch.functional.relu``, or ``torch.functional.gelu`` - at most one of ``src_mask`` and ``src_key_padding_mask`` is passed - if src is a `NestedTensor `_, neither ``src_mask`` nor ``src_key_padding_mask`` is passed - the two ``LayerNorm`` instances have a consistent ``eps`` value (this will naturally be the case unless the caller has manually modified one without modifying the other) If the optimized implementation is in use, a `NestedTensor `_ can be passed for ``src`` to represent padding more efficiently than using a padding mask. In this case, a `NestedTensor `_ will be returned, and an additional speedup proportional to the fraction of the input that is padding can be expected. r!r"rrrFN rrrrrr r!r"r#c s| | d} tt|t||f||d| |_t||f| |_t||_t||f| |_ ||_ t |fd|i| |_ t |fd|i| |_ t||_t||_t|trt|}|tjkrd|_n|tjkrd|_nd|_||_dS)Nr$rr!r'r rr)r(r+r)r rlrrwrrrxr"rrorpdropout1dropout2rirY_get_activation_fnrVrWrngelur r1rrrrrr r!r"r%r&r2r5r7r8r)es*       z TransformerEncoderLayer.__init__cs&tt||t|ds"tj|_dSNr)r(r+ __setstate__hasattrrVrWrr1stater5r7r8rs z$TransformerEncoderLayer.__setstate__)r9r;r>r#cCs|dkrx|jsx|jsx|jjrx|jjrx|jrx|jj|j jkrx|dkrx|j rf|dk sx||jj |jj |jj j|jj j|jj|jj|j j|j j|jj|jj|jj|jjf }tj|sxtdd|Drxtrtdd|Drxt||jj|jj|jj |jj |jj j|jj j|jdkd|jj|jj|jj|j j|j j|jj|jj|jj|jj|dk rr|n|S|}|jr||||||}||| |}n,||||||}| |||}|S)aQPass the input through the encoder layer. Args: src: the sequence to the encoder layer (required). src_mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). Shape: see the docs in Transformer class. rANcSs g|]}|jpdt|jkqS)rh)r}rYr%rdr7r7r8rgsz3TransformerEncoderLayer.forward..cSsg|] }|j qSr7rcrdr7r7r8rgsrF)rEr"rkrlr!rmrnror'rprqrrrsrtrurvrwrxrLryrzr|r{Z_transformer_encoder_layer_fwdZ embed_dimZ num_heads _sa_block _ff_block)r1r9r;r>r~rfr7r7r8rJszTransformerEncoderLayer.forwardrf attn_maskkey_padding_maskr#cCs$|j|||||ddd}||SNF)rrZ need_weightsrrlrr1rfrrr7r7r8rs z!TransformerEncoderLayer._sa_blockrfr#c Cs&|||||}||Sr^)rxrrrwrr1rfr7r7r8rsz!TransformerEncoderLayer._ff_block)NN)rRrSrTrUrrVrWrXrNrrYrrrZr)rrrJrrr\r7r7r5r8r+-s:5 !   I r+c seZdZdZddgZddejdddddfeeeee e e e ge ffee e dd fd d Zfd d Zde e ee ee ee ee e dddZe ee ee e dddZe e ee ee e dddZe e dddZZS)r.aTransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application. Args: d_model: the number of expected features in the input (required). nhead: the number of heads in the multiheadattention models (required). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of the intermediate layer, can be a string ("relu" or "gelu") or a unary callable. Default: relu layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False`` (seq, batch, feature). norm_first: if ``True``, layer norm is done prior to self attention, multihead attention and feedforward operations, respectivaly. Otherwise it's done after. Default: ``False`` (after). Examples:: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = decoder_layer(tgt, memory) Alternatively, when ``batch_first`` is ``True``: >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=True) >>> memory = torch.rand(32, 10, 512) >>> tgt = torch.rand(32, 20, 512) >>> out = decoder_layer(tgt, memory) r!r"rrrFNrc s| | d} tt|t||f||d| |_t||f||d| |_t||f| |_t||_ t||f| |_ ||_ t |fd|i| |_ t |fd|i| |_t |fd|i| |_t||_t||_t||_t|trt||_n||_dS)Nr$rr')r(r.r)r rlmultihead_attnrrwrrrxr"rrorpnorm3rrdropout3rirYrrrr5r7r8r)s*       z TransformerDecoderLayer.__init__cs&d|krtj|d<tt||dSr)rVrWr(r.rrr5r7r8r$s z$TransformerDecoderLayer.__setstate__rc Cs|}|jrR||||||}|||||||}||||}nF||||||}|||||||}||||}|S)aKPass the inputs (and mask) through the decoder layer. Args: tgt: the sequence to the decoder layer (required). memory: the sequence from the last layer of the encoder (required). tgt_mask: the mask for the tgt sequence (optional). memory_mask: the mask for the memory sequence (optional). tgt_key_padding_mask: the mask for the tgt keys per batch (optional). memory_key_padding_mask: the mask for the memory keys per batch (optional). 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