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transformers/tests/models/mega/test_modeling_mega.py

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# coding=utf-8
# Copyright 2023 The HuggingFace 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.
import unittest
from transformers import MegaConfig, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
is_flaky,
require_torch,
require_torch_fp16,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
)
class MegaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_positions=1024,
bidirectional=False, # needed for decoding, and can't modify common generation tests; test separately by overriding
ema_projection_size=16,
shared_representation_size=64,
use_chunking=False,
chunk_size=32,
attention_activation="softmax",
use_normalized_ffn=True,
nffn_hidden_size=24,
add_token_type_embeddings=True,
type_vocab_size=2,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.add_token_type_embeddings = add_token_type_embeddings
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_positions = max_positions
self.bidirectional = bidirectional
self.ema_projection_size = ema_projection_size
self.shared_representation_size = shared_representation_size
self.use_chunking = use_chunking
self.chunk_size = chunk_size
self.attention_activation = attention_activation
self.use_normalized_ffn = use_normalized_ffn
self.nffn_hidden_size = nffn_hidden_size
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.num_attention_heads = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.add_token_type_embeddings:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return MegaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
intermediate_size=self.intermediate_size,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
# added args
add_token_type_embeddings=self.add_token_type_embeddings,
max_positions=self.max_positions,
bidirectional=self.bidirectional,
ema_projection_size=self.ema_projection_size,
shared_representation_size=self.shared_representation_size,
use_chunking=self.use_chunking,
chunk_size=self.chunk_size,
attention_activation=self.attention_activation,
use_normalized_ffn=self.use_normalized_ffn,
nffn_hidden_size=self.nffn_hidden_size,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
config.bidirectional = False
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = MegaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = MegaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.bidirectional = False
config.add_cross_attention = True
model = MegaForCausalLM(config=config).to(torch_device).eval()
# make sure that ids don't start with pad token
mask = input_ids.ne(config.pad_token_id).long()
input_ids = input_ids * mask
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# make sure that ids don't start with pad token
mask = next_tokens.ne(config.pad_token_id).long()
next_tokens = next_tokens * mask
next_mask = ids_tensor((self.batch_size, 1), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_decoder_model_with_chunking(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.use_chunking = True
config.output_attentions = True
config.attention_activation = "laplace"
config.chunk_size = input_ids.size(1) * 2
model = MegaForCausalLM(config).to(torch_device).eval()
input_ids = input_ids.repeat(1, 8)
# multiply the sequence length by 8 since we repeat the same ids 8 times in input_ids
input_mask = random_attention_mask([self.batch_size, self.seq_length * 8])
result = model(input_ids, attention_mask=input_mask)
# test if the sequence length of attentions is same provided chunk_size
self.parent.assertEqual(result["attentions"][0].shape[-1], config.chunk_size)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegaForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MegaForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = MegaForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegaForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
# extra checks for Mega-specific model functionality
def create_and_check_bidirectionality(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.bidirectional = True
model = MegaModel(config)
model.to(torch_device)
model.eval()
# no mask
result = model(input_ids)
# with mask & token types
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
def check_chunking_shorter_sequence(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.use_chunking = True
config.chunk_size = input_ids.size(1) + 25
model = MegaModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
def check_chunking_longer_sequence(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.use_chunking = True
# we want the chunk size to be < sequence length, and the sequence length to be a multiple of chunk size
config.chunk_size = input_ids.size(1) * 2
model = MegaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids.repeat(1, 8),
)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length * 8, self.hidden_size))
def check_laplace_self_attention(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.attention_activation = "laplace"
model = MegaModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
def check_relu2_self_attention(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.attention_activation = "relu2"
model = MegaModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
def check_sequence_length_beyond_max_positions(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.max_positions = self.seq_length - 2
model = MegaModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class MegaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MegaForCausalLM,
MegaForMaskedLM,
MegaModel,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaForMultipleChoice,
MegaForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (MegaForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": MegaModel,
"fill-mask": MegaForMaskedLM,
"question-answering": MegaForQuestionAnswering,
"text-classification": MegaForSequenceClassification,
"text-generation": MegaForCausalLM,
"token-classification": MegaForTokenClassification,
"zero-shot": MegaForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_head_masking = False
test_pruning = False
def setUp(self):
self.model_tester = MegaModelTester(self)
self.config_tester = ConfigTester(self, config_class=MegaConfig, hidden_size=37)
# TODO: @ydshieh
@is_flaky(description="Sometimes gives `AssertionError` on expected outputs")
def test_pipeline_fill_mask(self):
super().test_pipeline_fill_mask()
# TODO: @ydshieh
@is_flaky(
description="Sometimes gives `RuntimeError: probability tensor contains either `inf`, `nan` or element < 0`"
)
def test_pipeline_text_generation(self):
super().test_pipeline_text_generation()
def test_config(self):
self.config_tester.run_common_tests()
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_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_with_chunking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_with_chunking(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_bidirectionality(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bidirectionality(*config_and_inputs)
def test_for_chunking_shorter_sequence(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_chunking_shorter_sequence(*config_and_inputs)
def test_for_chunking_longer_sequence(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_chunking_longer_sequence(*config_and_inputs)
def test_for_laplace_attention(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_laplace_self_attention(*config_and_inputs)
def test_for_relu2_attention(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_relu2_self_attention(*config_and_inputs)
def test_for_sequence_length_beyond_max_positions(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_sequence_length_beyond_max_positions(*config_and_inputs)
@require_torch_fp16
def test_generate_fp16(self):
config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs_for_decoder()
# attention_mask = torch.LongTensor(input_ids.ne(1)).to(torch_device)
model = MegaForCausalLM(config).eval().to(torch_device)
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_sequence_classification_model(self):
config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs()
config.num_labels = self.model_tester.num_labels
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = MegaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_sequence_classification_model_for_multi_label(self):
config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs()
config.num_labels = self.model_tester.num_labels
config.problem_type = "multi_label_classification"
sequence_labels = ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
).to(torch.float)
model = MegaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
@slow
def test_model_from_pretrained(self):
model_name = "mnaylor/mega-base-wikitext"
model = MegaModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
def test_cpu_offload(self):
super().test_cpu_offload()
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
def test_disk_offload(self):
super().test_disk_offload()
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
def test_model_parallelism(self):
super().test_model_parallelism()
@unittest.skip(
reason=(
"Calling `self.attention_function` in `MegaMovingAverageGatedAttention.forward` changes the submodules on "
"device 1 to device 0 (also changes `requires_grad`). No idea how this could happen for now."
)
)
def test_multi_gpu_data_parallel_forward(self):
super().test_multi_gpu_data_parallel_forward()
@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
def test_torchscript_simple(self):
super().test_torchscript_simple()
@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
def test_torchscript_output_hidden_state(self):
super().test_torchscript_output_hidden_state()
@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
def test_torchscript_output_attentions(self):
super().test_torchscript_output_attentions()
@require_torch
class MegaModelIntegrationTest(TestCasePlus):
@slow
def test_inference_masked_lm(self):
model = MegaForMaskedLM.from_pretrained("mnaylor/mega-base-wikitext")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 50265))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[67.8389, 10.1470, -32.7148], [-11.1655, 29.1152, 23.1304], [-3.8015, 66.0397, 29.6733]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_no_head(self):
model = MegaModel.from_pretrained("mnaylor/mega-base-wikitext")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 128))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice. taken from output[:, :3, :3]
expected_slice = torch.tensor(
[[[1.1767, -0.6349, 2.8494], [-0.5109, -0.7745, 1.9495], [-0.3287, -0.2111, 3.3367]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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