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