import{S as Bo,i as Ho,s as Uo,e as t,k as d,w as v,t as i,M as Vo,c as a,d as o,m as p,a as n,x as b,h as l,b as s,G as r,g as h,y,L as jo,q as w,o as G,B as x,v as Ko}from"../../chunks/vendor-hf-doc-builder.js";import{D as qe}from"../../chunks/Docstring-hf-doc-builder.js";import{I as Fe}from"../../chunks/IconCopyLink-hf-doc-builder.js";function Qo(ao){let C,Se,$,z,ue,R,nr,ge,sr,Ie,T,q,_e,Y,ir,ve,lr,De,F,mr,B,dr,pr,Me,re,hr,Oe,oe,be,fr,Re,te,cr,Ye,S,ur,ye,gr,_r,Be,ae,vr,He,g,br,H,yr,wr,U,Gr,xr,Ue,E,I,we,V,Cr,Ge,$r,Ve,u,j,Tr,k,Er,ne,kr,Nr,K,Ar,Lr,Pr,N,zr,se,qr,Fr,ie,Sr,Ir,je,A,D,xe,Q,Dr,Ce,Mr,Ke,c,W,Or,$e,Rr,Yr,Te,Br,Hr,le,Z,Qe,L,M,Ee,J,Ur,ke,Vr,We,f,X,jr,Ne,Kr,Qr,Ae,Wr,Zr,P,Le,Jr,Xr,Pe,eo,ro,ze,oo,to,me,ee,Ze;return R=new Fe({}),Y=new Fe({}),V=new Fe({}),j=new qe({props:{name:"class transformers.GraphormerConfig",anchor:"transformers.GraphormerConfig",parameters:[{name:"num_classes",val:": int = 1"},{name:"num_atoms",val:": int = 4608"},{name:"num_edges",val:": int = 1536"},{name:"num_in_degree",val:": int = 512"},{name:"num_out_degree",val:": int = 512"},{name:"num_spatial",val:": int = 512"},{name:"num_edge_dis",val:": int = 128"},{name:"multi_hop_max_dist",val:": int = 5"},{name:"spatial_pos_max",val:": int = 1024"},{name:"edge_type",val:": str = 'multi_hop'"},{name:"max_nodes",val:": int = 512"},{name:"share_input_output_embed",val:": bool = False"},{name:"num_hidden_layers",val:": int = 12"},{name:"embedding_dim",val:": int = 768"},{name:"ffn_embedding_dim",val:": int = 768"},{name:"num_attention_heads",val:": int = 32"},{name:"dropout",val:": float = 0.1"},{name:"attention_dropout",val:": float = 0.1"},{name:"layerdrop",val:": float = 0.0"},{name:"encoder_normalize_before",val:": bool = False"},{name:"pre_layernorm",val:": bool = False"},{name:"apply_graphormer_init",val:": bool = False"},{name:"activation_fn",val:": str = 'gelu'"},{name:"embed_scale",val:": float = None"},{name:"freeze_embeddings",val:": bool = False"},{name:"num_trans_layers_to_freeze",val:": int = 0"},{name:"traceable",val:": bool = False"},{name:"q_noise",val:": float = 0.0"},{name:"qn_block_size",val:": int = 8"},{name:"kdim",val:": int = None"},{name:"vdim",val:": int = None"},{name:"bias",val:": bool = True"},{name:"self_attention",val:": bool = True"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GraphormerConfig.num_classes",description:`<strong>num_classes</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of target classes or labels, set to n for binary classification of n tasks.`,name:"num_classes"},{anchor:"transformers.GraphormerConfig.num_atoms",description:`<strong>num_atoms</strong> (<code>int</code>, <em>optional</em>, defaults to 512*9) &#x2014;
Number of node types in the graphs.`,name:"num_atoms"},{anchor:"transformers.GraphormerConfig.num_edges",description:`<strong>num_edges</strong> (<code>int</code>, <em>optional</em>, defaults to 512*3) &#x2014;
Number of edges types in the graph.`,name:"num_edges"},{anchor:"transformers.GraphormerConfig.num_in_degree",description:`<strong>num_in_degree</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Number of in degrees types in the input graphs.`,name:"num_in_degree"},{anchor:"transformers.GraphormerConfig.num_out_degree",description:`<strong>num_out_degree</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Number of out degrees types in the input graphs.`,name:"num_out_degree"},{anchor:"transformers.GraphormerConfig.num_edge_dis",description:`<strong>num_edge_dis</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Number of edge dis in the input graphs.`,name:"num_edge_dis"},{anchor:"transformers.GraphormerConfig.multi_hop_max_dist",description:`<strong>multi_hop_max_dist</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014;
Maximum distance of multi hop edges between two nodes.`,name:"multi_hop_max_dist"},{anchor:"transformers.GraphormerConfig.spatial_pos_max",description:`<strong>spatial_pos_max</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Maximum distance between nodes in the graph attention bias matrices, used during preprocessing and
collation.`,name:"spatial_pos_max"},{anchor:"transformers.GraphormerConfig.edge_type",description:`<strong>edge_type</strong> (<code>str</code>, <em>optional</em>, defaults to multihop) &#x2014;
Type of edge relation chosen.`,name:"edge_type"},{anchor:"transformers.GraphormerConfig.max_nodes",description:`<strong>max_nodes</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Maximum number of nodes which can be parsed for the input graphs.`,name:"max_nodes"},{anchor:"transformers.GraphormerConfig.share_input_output_embed",description:`<strong>share_input_output_embed</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Shares the embedding layer between encoder and decoder - careful, True is not implemented.`,name:"share_input_output_embed"},{anchor:"transformers.GraphormerConfig.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of layers.`,name:"num_layers"},{anchor:"transformers.GraphormerConfig.embedding_dim",description:`<strong>embedding_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimension of the embedding layer in encoder.`,name:"embedding_dim"},{anchor:"transformers.GraphormerConfig.ffn_embedding_dim",description:`<strong>ffn_embedding_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in encoder.`,name:"ffn_embedding_dim"},{anchor:"transformers.GraphormerConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of attention heads in the encoder.`,name:"num_attention_heads"},{anchor:"transformers.GraphormerConfig.self_attention",description:`<strong>self_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Model is self attentive (False not implemented).`,name:"self_attention"},{anchor:"transformers.GraphormerConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation_function"},{anchor:"transformers.GraphormerConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.GraphormerConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for the attention weights.`,name:"attention_dropout"},{anchor:"transformers.GraphormerConfig.layerdrop",description:`<strong>layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The LayerDrop probability for the encoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
for more details.`,name:"layerdrop"},{anchor:"transformers.GraphormerConfig.bias",description:`<strong>bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Uses bias in the attention module - unsupported at the moment.`,name:"bias"},{anchor:"transformers.GraphormerConfig.embed_scale(float,",description:`<strong>embed_scale(<code>float</code>,</strong> <em>optional</em>, defaults to None) &#x2014;
Scaling factor for the node embeddings.`,name:"embed_scale(float,"},{anchor:"transformers.GraphormerConfig.num_trans_layers_to_freeze",description:`<strong>num_trans_layers_to_freeze</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Number of transformer layers to freeze.`,name:"num_trans_layers_to_freeze"},{anchor:"transformers.GraphormerConfig.encoder_normalize_before",description:`<strong>encoder_normalize_before</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Normalize features before encoding the graph.`,name:"encoder_normalize_before"},{anchor:"transformers.GraphormerConfig.pre_layernorm",description:`<strong>pre_layernorm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Apply layernorm before self attention and the feed forward network. Without this, post layernorm will be
used.`,name:"pre_layernorm"},{anchor:"transformers.GraphormerConfig.apply_graphormer_init",description:`<strong>apply_graphormer_init</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Apply a custom graphormer initialisation to the model before training.`,name:"apply_graphormer_init"},{anchor:"transformers.GraphormerConfig.freeze_embeddings",description:`<strong>freeze_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Freeze the embedding layer, or train it along the model.`,name:"freeze_embeddings"},{anchor:"transformers.GraphormerConfig.encoder_normalize_before",description:`<strong>encoder_normalize_before</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Apply the layer norm before each encoder block.`,name:"encoder_normalize_before"},{anchor:"transformers.GraphormerConfig.q_noise",description:`<strong>q_noise</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Amount of quantization noise (see &#x201C;Training with Quantization Noise for Extreme Model Compression&#x201D;). (For
more detail, see fairseq&#x2019;s documentation on quant_noise).`,name:"q_noise"},{anchor:"transformers.GraphormerConfig.qn_block_size",description:`<strong>qn_block_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Size of the blocks for subsequent quantization with iPQ (see q_noise).`,name:"qn_block_size"},{anchor:"transformers.GraphormerConfig.kdim",description:`<strong>kdim</strong> (<code>int</code>, <em>optional</em>, defaults to None) &#x2014;
Dimension of the key in the attention, if different from the other values.`,name:"kdim"},{anchor:"transformers.GraphormerConfig.vdim",description:`<strong>vdim</strong> (<code>int</code>, <em>optional</em>, defaults to None) &#x2014;
Dimension of the value in the attention, if different from the other values.`,name:"vdim"},{anchor:"transformers.GraphormerConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"},{anchor:"transformers.GraphormerConfig.traceable",description:`<strong>traceable</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Changes return value of the encoder&#x2019;s inner_state to stacked tensors.</p>
<p>Example &#x2014;`,name:"traceable"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/graphormer/configuration_graphormer.py#L30"}}),Q=new Fe({}),W=new qe({props:{name:"class transformers.GraphormerModel",anchor:"transformers.GraphormerModel",parameters:[{name:"config",val:": GraphormerConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/graphormer/modeling_graphormer.py#L781"}}),Z=new qe({props:{name:"forward",anchor:"transformers.GraphormerModel.forward",parameters:[{name:"input_nodes",val:": LongTensor"},{name:"input_edges",val:": LongTensor"},{name:"attn_bias",val:": Tensor"},{name:"in_degree",val:": LongTensor"},{name:"out_degree",val:": LongTensor"},{name:"spatial_pos",val:": LongTensor"},{name:"attn_edge_type",val:": LongTensor"},{name:"perturb",val:" = None"},{name:"masked_tokens",val:" = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**unused",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/graphormer/modeling_graphormer.py#L810"}}),J=new Fe({}),X=new qe({props:{name:"class transformers.GraphormerForGraphClassification",anchor:"transformers.GraphormerForGraphClassification",parameters:[{name:"config",val:": GraphormerConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/graphormer/modeling_graphormer.py#L852"}}),ee=new qe({props:{name:"forward",anchor:"transformers.GraphormerForGraphClassification.forward",parameters:[{name:"input_nodes",val:": LongTensor"},{name:"input_edges",val:": LongTensor"},{name:"attn_bias",val:": Tensor"},{name:"in_degree",val:": LongTensor"},{name:"out_degree",val:": LongTensor"},{name:"spatial_pos",val:": LongTensor"},{name:"attn_edge_type",val:": LongTensor"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**unused",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/graphormer/modeling_graphormer.py#L875"}}),{c(){C=t("meta"),Se=d(),$=t("h1"),z=t("a"),ue=t("span"),v(R.$$.fragment),nr=d(),ge=t("span"),sr=i("Graphormer"),Ie=d(),T=t("h2"),q=t("a"),_e=t("span"),v(Y.$$.fragment),ir=d(),ve=t("span"),lr=i("Overview"),De=d(),F=t("p"),mr=i("The Graphormer model was proposed in "),B=t("a"),dr=i("Do Transformers Really Perform Bad for Graph Representation?"),pr=i(`  by
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configuration with the defaults will yield a similar configuration to that of the Graphormer
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GraphormerForGraphClassification instead. For any other downstream task, feel free to add a new class, or combine
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label per graph`),ro=d(),ze=t("li"),oo=i(`binary multi-task classification (by setting config.num_classes to the number of labels); there should be a list
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documentation from `),ie=a(fe,"A",{href:!0});var Eo=n(ie);Sr=l(Eo,"PretrainedConfig"),Eo.forEach(o),Ir=l(fe," for more information."),fe.forEach(o),pe.forEach(o),je=p(e),A=a(e,"H2",{class:!0});var tr=n(A);D=a(tr,"A",{id:!0,class:!0,href:!0});var ko=n(D);xe=a(ko,"SPAN",{});var No=n(xe);b(Q.$$.fragment,No),No.forEach(o),ko.forEach(o),Dr=p(tr),Ce=a(tr,"SPAN",{});var Ao=n(Ce);Mr=l(Ao,"GraphormerModel"),Ao.forEach(o),tr.forEach(o),Ke=p(e),c=a(e,"DIV",{class:!0});var O=n(c);b(W.$$.fragment,O),Or=p(O),$e=a(O,"P",{});var Lo=n($e);Rr=l(Lo,"The Graphormer model is a graph-encoder model."),Lo.forEach(o),Yr=p(O),Te=a(O,"P",{});var Po=n(Te);Br=l(Po,`It goes from a graph to its representation. If you want to use the model for a downstream classification task, use
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