1302 lines
64 KiB
Python
1302 lines
64 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch Graphormer model. """
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import copy
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import inspect
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import os
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import tempfile
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import unittest
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from transformers import GraphormerConfig, is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import tensor
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from transformers import GraphormerForGraphClassification, GraphormerModel
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class GraphormerModelTester:
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def __init__(
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self,
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parent,
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num_classes=1,
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num_atoms=32 * 9,
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num_edges=32 * 3,
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num_in_degree=32,
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num_out_degree=32,
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num_spatial=32,
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num_edge_dis=16,
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multi_hop_max_dist=5, # sometimes is 20
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spatial_pos_max=32,
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edge_type="multi_hop",
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init_fn=None,
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max_nodes=32,
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share_input_output_embed=False,
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num_hidden_layers=2,
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embedding_dim=32,
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ffn_embedding_dim=32,
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num_attention_heads=4,
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dropout=0.1,
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attention_dropout=0.1,
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activation_dropout=0.1,
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layerdrop=0.0,
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encoder_normalize_before=False,
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pre_layernorm=False,
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apply_graphormer_init=False,
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activation_fn="gelu",
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embed_scale=None,
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freeze_embeddings=False,
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num_trans_layers_to_freeze=0,
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traceable=False,
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q_noise=0.0,
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qn_block_size=8,
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kdim=None,
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vdim=None,
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bias=True,
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self_attention=True,
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batch_size=10,
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graph_size=20,
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is_training=True,
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):
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self.parent = parent
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self.num_classes = num_classes
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self.num_labels = num_classes
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self.num_atoms = num_atoms
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self.num_in_degree = num_in_degree
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self.num_out_degree = num_out_degree
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self.num_edges = num_edges
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self.num_spatial = num_spatial
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self.num_edge_dis = num_edge_dis
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self.edge_type = edge_type
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self.multi_hop_max_dist = multi_hop_max_dist
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self.spatial_pos_max = spatial_pos_max
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self.max_nodes = max_nodes
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self.num_hidden_layers = num_hidden_layers
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self.embedding_dim = embedding_dim
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self.hidden_size = embedding_dim
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self.ffn_embedding_dim = ffn_embedding_dim
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self.num_attention_heads = num_attention_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.layerdrop = layerdrop
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self.encoder_normalize_before = encoder_normalize_before
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self.pre_layernorm = pre_layernorm
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self.apply_graphormer_init = apply_graphormer_init
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self.activation_fn = activation_fn
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self.embed_scale = embed_scale
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self.freeze_embeddings = freeze_embeddings
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self.num_trans_layers_to_freeze = num_trans_layers_to_freeze
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self.share_input_output_embed = share_input_output_embed
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self.traceable = traceable
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self.q_noise = q_noise
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self.qn_block_size = qn_block_size
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self.init_fn = init_fn
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self.kdim = kdim
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self.vdim = vdim
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self.self_attention = self_attention
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self.bias = bias
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self.batch_size = batch_size
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self.graph_size = graph_size
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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attn_bias = ids_tensor(
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[self.batch_size, self.graph_size + 1, self.graph_size + 1], self.num_atoms
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) # Def not sure here
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attn_edge_type = ids_tensor([self.batch_size, self.graph_size, self.graph_size, 1], self.num_edges)
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spatial_pos = ids_tensor([self.batch_size, self.graph_size, self.graph_size], self.num_spatial)
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in_degree = ids_tensor([self.batch_size, self.graph_size], self.num_in_degree)
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out_degree = ids_tensor([self.batch_size, self.graph_size], self.num_out_degree)
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input_nodes = ids_tensor([self.batch_size, self.graph_size, 1], self.num_atoms)
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input_edges = ids_tensor(
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[self.batch_size, self.graph_size, self.graph_size, self.multi_hop_max_dist, 1], self.num_edges
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)
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labels = ids_tensor([self.batch_size], self.num_classes)
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config = self.get_config()
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return config, attn_bias, attn_edge_type, spatial_pos, in_degree, out_degree, input_nodes, input_edges, labels
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def get_config(self):
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return GraphormerConfig(
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num_atoms=self.num_atoms,
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num_in_degree=self.num_in_degree,
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num_out_degree=self.num_out_degree,
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num_edges=self.num_edges,
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num_spatial=self.num_spatial,
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num_edge_dis=self.num_edge_dis,
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edge_type=self.edge_type,
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multi_hop_max_dist=self.multi_hop_max_dist,
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spatial_pos_max=self.spatial_pos_max,
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max_nodes=self.max_nodes,
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num_hidden_layers=self.num_hidden_layers,
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embedding_dim=self.embedding_dim,
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hidden_size=self.embedding_dim,
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ffn_embedding_dim=self.ffn_embedding_dim,
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num_attention_heads=self.num_attention_heads,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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activation_dropout=self.activation_dropout,
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layerdrop=self.layerdrop,
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encoder_normalize_before=self.encoder_normalize_before,
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pre_layernorm=self.pre_layernorm,
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apply_graphormer_init=self.apply_graphormer_init,
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activation_fn=self.activation_fn,
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embed_scale=self.embed_scale,
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freeze_embeddings=self.freeze_embeddings,
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num_trans_layers_to_freeze=self.num_trans_layers_to_freeze,
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share_input_output_embed=self.share_input_output_embed,
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traceable=self.traceable,
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q_noise=self.q_noise,
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qn_block_size=self.qn_block_size,
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init_fn=self.init_fn,
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kdim=self.kdim,
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vdim=self.vdim,
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self_attention=self.self_attention,
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bias=self.bias,
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)
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def create_and_check_model(
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self, config, attn_bias, attn_edge_type, spatial_pos, in_degree, out_degree, input_nodes, input_edges, labels
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):
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model = GraphormerModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_nodes=input_nodes,
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attn_bias=attn_bias,
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in_degree=in_degree,
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out_degree=out_degree,
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spatial_pos=spatial_pos,
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input_edges=input_edges,
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attn_edge_type=attn_edge_type,
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labels=labels,
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)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.graph_size + 1, self.hidden_size)
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)
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def create_and_check_for_graph_classification(
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self, config, attn_bias, attn_edge_type, spatial_pos, in_degree, out_degree, input_nodes, input_edges, labels
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):
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model = GraphormerForGraphClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_nodes=input_nodes,
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attn_bias=attn_bias,
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in_degree=in_degree,
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out_degree=out_degree,
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spatial_pos=spatial_pos,
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input_edges=input_edges,
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attn_edge_type=attn_edge_type,
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labels=labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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attn_bias,
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attn_edge_type,
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spatial_pos,
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in_degree,
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out_degree,
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input_nodes,
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input_edges,
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labels,
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) = config_and_inputs
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inputs_dict = {
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"attn_bias": attn_bias,
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"attn_edge_type": attn_edge_type,
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"spatial_pos": spatial_pos,
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"in_degree": in_degree,
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"out_degree": out_degree,
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"input_nodes": input_nodes,
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"input_edges": input_edges,
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"labels": labels,
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}
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return config, inputs_dict
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@require_torch
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class GraphormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (GraphormerForGraphClassification, GraphormerModel) if is_torch_available() else ()
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all_generative_model_classes = ()
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pipeline_model_mapping = {"feature-extraction": GraphormerModel} if is_torch_available() else {}
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test_pruning = False
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test_head_masking = False
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test_resize_embeddings = False
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main_input_name_nodes = "input_nodes"
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main_input_name_edges = "input_edges"
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has_attentions = False # does not output attention
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def setUp(self):
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self.model_tester = GraphormerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=GraphormerConfig, has_text_modality=False)
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# overwrite from common as `Graphormer` requires more input arguments
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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try:
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required_keys = (
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"input_nodes",
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"input_edges",
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"attn_bias",
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"in_degree",
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"out_degree",
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"spatial_pos",
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"attn_edge_type",
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)
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required_inputs = tuple(inputs[k] for k in required_keys)
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model(*required_inputs)
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traced_model = torch.jit.trace(model, required_inputs)
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_model, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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loaded_model_state_dict = loaded_model.state_dict()
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non_persistent_buffers = {}
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for key in loaded_model_state_dict.keys():
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if key not in model_state_dict.keys():
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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if layer_name in loaded_model_state_dict:
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p2 = loaded_model_state_dict[layer_name]
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
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# (Even with this call, there are still memory leak by ~0.04MB)
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self.clear_torch_jit_class_registry()
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="Graphormer does not use one single inputs_embedding but three")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Graphormer does not implement feed forward chunking")
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def test_feed_forward_chunking(self):
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pass
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@unittest.skip(reason="Graphormer does not share input and output embeddings")
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def test_model_common_attributes(self):
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pass
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def test_initialization(self):
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
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setattr(configs_no_init, key, 1e-10)
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return configs_no_init
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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batch_size = self.model_tester.batch_size
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[batch_size, self.model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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# Always returns hidden_states
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_retain_grad_hidden_states_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = False
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# no need to test all models as different heads yield the same functionality
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model_class = self.all_model_classes[0]
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model = model_class(config)
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model.to(torch_device)
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outputs = model(**inputs_dict)
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output = outputs[0]
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hidden_states = outputs.hidden_states[0]
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hidden_states.retain_grad()
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output.flatten()[0].backward(retain_graph=True)
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self.assertIsNotNone(hidden_states.grad)
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# Inputs are 'input_nodes' and 'input_edges' not 'input_ids'
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def test_model_main_input_name(self):
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for model_class in self.all_model_classes:
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model_signature = inspect.signature(getattr(model_class, "forward"))
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# The main input is the name of the argument after `self`
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observed_main_input_name_nodes = list(model_signature.parameters.keys())[1]
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observed_main_input_name_edges = list(model_signature.parameters.keys())[2]
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self.assertEqual(model_class.main_input_name_nodes, observed_main_input_name_nodes)
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self.assertEqual(model_class.main_input_name_edges, observed_main_input_name_edges)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_nodes", "input_edges"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_graph_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_graph_classification(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "clefourrier/graphormer-base-pcqm4mv1"
|
|
model = GraphormerForGraphClassification.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class GraphormerModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_graph_classification(self):
|
|
model = GraphormerForGraphClassification.from_pretrained("clefourrier/graphormer-base-pcqm4mv2")
|
|
|
|
# Actual real graph data from the MUTAG dataset
|
|
# fmt: off
|
|
model_input = {
|
|
"attn_bias": tensor(
|
|
[
|
|
[
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
],
|
|
[
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, float("-inf"), float("-inf"), float("-inf"), float("-inf")],
|
|
],
|
|
]
|
|
),
|
|
"attn_edge_type": tensor(
|
|
[
|
|
[
|
|
[[0], [3], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [3], [0], [3], [0], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [3], [0], [3], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[3], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [3], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [3], [0], [0], [0]],
|
|
[[0], [0], [0], [3], [0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [3], [3], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [0], [3], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [3], [3]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0]],
|
|
],
|
|
[
|
|
[[0], [3], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [3], [0], [3], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [3], [0], [0], [0], [3], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [3], [0], [3], [3], [0], [0], [0], [0], [0], [0]],
|
|
[[3], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [3], [3], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [3], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
|
|
],
|
|
]
|
|
),
|
|
# fmt: on
|
|
"spatial_pos": tensor(
|
|
[
|
|
[
|
|
[1, 2, 3, 4, 3, 2, 4, 5, 6, 5, 6, 7, 8, 7, 9, 10, 10],
|
|
[2, 1, 2, 3, 4, 3, 5, 6, 5, 4, 5, 6, 7, 6, 8, 9, 9],
|
|
[3, 2, 1, 2, 3, 4, 4, 5, 4, 3, 4, 5, 6, 5, 7, 8, 8],
|
|
[4, 3, 2, 1, 2, 3, 3, 4, 3, 2, 3, 4, 5, 4, 6, 7, 7],
|
|
[3, 4, 3, 2, 1, 2, 2, 3, 4, 3, 4, 5, 6, 5, 7, 8, 8],
|
|
[2, 3, 4, 3, 2, 1, 3, 4, 5, 4, 5, 6, 7, 6, 8, 9, 9],
|
|
[4, 5, 4, 3, 2, 3, 1, 2, 3, 4, 5, 6, 5, 4, 6, 7, 7],
|
|
[5, 6, 5, 4, 3, 4, 2, 1, 2, 3, 4, 5, 4, 3, 5, 6, 6],
|
|
[6, 5, 4, 3, 4, 5, 3, 2, 1, 2, 3, 4, 3, 2, 4, 5, 5],
|
|
[5, 4, 3, 2, 3, 4, 4, 3, 2, 1, 2, 3, 4, 3, 5, 6, 6],
|
|
[6, 5, 4, 3, 4, 5, 5, 4, 3, 2, 1, 2, 3, 4, 4, 5, 5],
|
|
[7, 6, 5, 4, 5, 6, 6, 5, 4, 3, 2, 1, 2, 3, 3, 4, 4],
|
|
[8, 7, 6, 5, 6, 7, 5, 4, 3, 4, 3, 2, 1, 2, 2, 3, 3],
|
|
[7, 6, 5, 4, 5, 6, 4, 3, 2, 3, 4, 3, 2, 1, 3, 4, 4],
|
|
[9, 8, 7, 6, 7, 8, 6, 5, 4, 5, 4, 3, 2, 3, 1, 2, 2],
|
|
[10, 9, 8, 7, 8, 9, 7, 6, 5, 6, 5, 4, 3, 4, 2, 1, 3],
|
|
[10, 9, 8, 7, 8, 9, 7, 6, 5, 6, 5, 4, 3, 4, 2, 3, 1],
|
|
],
|
|
[
|
|
[1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 4, 5, 5, 0, 0, 0, 0],
|
|
[2, 1, 2, 3, 4, 5, 4, 3, 4, 3, 5, 6, 6, 0, 0, 0, 0],
|
|
[3, 2, 1, 2, 3, 4, 3, 2, 3, 4, 4, 5, 5, 0, 0, 0, 0],
|
|
[4, 3, 2, 1, 2, 3, 4, 3, 4, 5, 5, 6, 6, 0, 0, 0, 0],
|
|
[5, 4, 3, 2, 1, 2, 3, 4, 5, 6, 6, 7, 7, 0, 0, 0, 0],
|
|
[6, 5, 4, 3, 2, 1, 2, 3, 4, 5, 5, 6, 6, 0, 0, 0, 0],
|
|
[5, 4, 3, 4, 3, 2, 1, 2, 3, 4, 4, 5, 5, 0, 0, 0, 0],
|
|
[4, 3, 2, 3, 4, 3, 2, 1, 2, 3, 3, 4, 4, 0, 0, 0, 0],
|
|
[3, 4, 3, 4, 5, 4, 3, 2, 1, 2, 2, 3, 3, 0, 0, 0, 0],
|
|
[2, 3, 4, 5, 6, 5, 4, 3, 2, 1, 3, 4, 4, 0, 0, 0, 0],
|
|
[4, 5, 4, 5, 6, 5, 4, 3, 2, 3, 1, 2, 2, 0, 0, 0, 0],
|
|
[5, 6, 5, 6, 7, 6, 5, 4, 3, 4, 2, 1, 3, 0, 0, 0, 0],
|
|
[5, 6, 5, 6, 7, 6, 5, 4, 3, 4, 2, 3, 1, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
]
|
|
),
|
|
"in_degree": tensor(
|
|
[
|
|
[3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3, 4, 3, 4, 2, 2],
|
|
[3, 3, 4, 3, 3, 3, 3, 4, 4, 3, 4, 2, 2, 0, 0, 0, 0],
|
|
]
|
|
),
|
|
"out_degree": tensor(
|
|
[
|
|
[3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3, 4, 3, 4, 2, 2],
|
|
[3, 3, 4, 3, 3, 3, 3, 4, 4, 3, 4, 2, 2, 0, 0, 0, 0],
|
|
]
|
|
),
|
|
"input_nodes": tensor(
|
|
[
|
|
[[3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3]],
|
|
[[3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [0], [0], [0], [0]],
|
|
]
|
|
),
|
|
"input_edges": tensor(
|
|
[
|
|
[
|
|
[
|
|
[[0], [0], [0], [0], [0]],
|
|
[[4], [0], [0], [0], [0]],
|
|
[[4], [4], [0], [0], [0]],
|
|
[[4], [4], [4], [0], [0]],
|
|
[[4], [4], [0], [0], [0]],
|
|
[[4], [0], [0], [0], [0]],
|
|
[[4], [4], [4], [0], [0]],
|
|
[[4], [4], [4], [4], [0]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [0]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [4]],
|
|
],
|
|
[
|
|
[[4], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
|
[[4], [0], [0], [0], [0]],
|
|
[[4], [4], [0], [0], [0]],
|
|
[[4], [4], [4], [0], [0]],
|
|
[[4], [4], [0], [0], [0]],
|
|
[[4], [4], [4], [4], [0]],
|
|
[[4], [4], [4], [4], [4]],
|
|
[[4], [4], [4], [4], [0]],
|
|
[[4], [4], [4], [0], [0]],
|
|
[[4], [4], [4], [4], [0]],
|
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],
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[
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],
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[
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[[4], [4], [4], [4], [0]],
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[[4], [4], [4], [4], [4]],
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],
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[
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[[4], [4], [4], [4], [0]],
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[[4], [4], [4], [0], [0]],
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[[4], [4], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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],
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[
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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],
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[
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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],
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[
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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],
|
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[
|
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[[0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
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[[0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
|
[[0], [0], [0], [0], [0]],
|
|
],
|
|
],
|
|
]
|
|
),
|
|
"labels": tensor([1, 0]),
|
|
}
|
|
|
|
output = model(**model_input)["logits"]
|
|
|
|
expected_shape = torch.Size((2, 1))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_logs = torch.tensor(
|
|
[[7.6060], [7.4126]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output, expected_logs, atol=1e-4))
|