466 lines
20 KiB
Python
466 lines
20 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 BioGPT model."""
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import math
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import unittest
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from transformers import BioGptConfig, is_sacremoses_available, is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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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 transformers import (
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BioGptForCausalLM,
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BioGptForSequenceClassification,
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BioGptForTokenClassification,
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BioGptModel,
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BioGptTokenizer,
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)
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class BioGptModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return BioGptConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = BioGptModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = BioGptForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_biogpt_model_attention_mask_past(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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model = BioGptModel(config=config)
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model.to(torch_device)
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model.eval()
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# create attention mask
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attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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half_seq_length = self.seq_length // 2
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attn_mask[:, half_seq_length:] = 0
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# first forward pass
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output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# change a random masked slice from input_ids
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random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
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input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
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# append to next input_ids and attn_mask
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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attn_mask = torch.cat(
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[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
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dim=1,
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)
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# get two different outputs
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output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_biogpt_model_past_large_inputs(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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model = BioGptModel(config=config).to(torch_device).eval()
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attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_forward_and_backwards(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
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):
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model = BioGptForCausalLM(config)
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model.to(torch_device)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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result = model(input_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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result.loss.backward()
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def create_and_check_biogpt_weight_initialization(self, config, *args):
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model = BioGptModel(config)
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model_std = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
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for key in model.state_dict().keys():
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if "c_proj" in key and "weight" in key:
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self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
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self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
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def create_and_check_biogpt_for_token_classification(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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config.num_labels = self.num_labels
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model = BioGptForTokenClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, 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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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class BioGptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (BioGptForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": BioGptModel,
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"text-classification": BioGptForSequenceClassification,
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"text-generation": BioGptForCausalLM,
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"token-classification": BioGptForTokenClassification,
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"zero-shot": BioGptForSequenceClassification,
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}
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if is_torch_available() and is_sacremoses_available()
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else {}
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)
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test_pruning = False
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def setUp(self):
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self.model_tester = BioGptModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BioGptConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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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_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_biogpt_model_att_mask_past(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_biogpt_model_attention_mask_past(*config_and_inputs)
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def test_biogpt_gradient_checkpointing(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_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
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def test_biogpt_model_past_with_large_inputs(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_biogpt_model_past_large_inputs(*config_and_inputs)
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def test_biogpt_weight_initialization(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_biogpt_weight_initialization(*config_and_inputs)
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def test_biogpt_token_classification_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_biogpt_for_token_classification(*config_and_inputs)
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@slow
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def test_batch_generation(self):
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model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
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model.to(torch_device)
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tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
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tokenizer.padding_side = "left"
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# Define PAD Token = EOS Token = 50256
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tokenizer.pad_token = tokenizer.eos_token
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model.config.pad_token_id = model.config.eos_token_id
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# use different length sentences to test batching
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sentences = [
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"Hello, my dog is a little",
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"Today, I",
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]
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inputs = tokenizer(sentences, return_tensors="pt", padding=True)
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input_ids = inputs["input_ids"].to(torch_device)
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outputs = model.generate(
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input_ids=input_ids,
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attention_mask=inputs["attention_mask"].to(torch_device),
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)
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inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
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output_non_padded = model.generate(input_ids=inputs_non_padded)
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num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
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inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
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output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
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batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
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padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
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expected_output_sentence = [
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"Hello, my dog is a little bit bigger than a little bit.",
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"Today, I have a good idea of how to use the information",
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]
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self.assertListEqual(expected_output_sentence, batch_out_sentence)
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self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
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@slow
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def test_model_from_pretrained(self):
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model_name = "microsoft/biogpt"
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model = BioGptModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# Copied from tests.models.opt.test_modeling_opt.OPTModelTest.test_opt_sequence_classification_model with OPT->BioGpt,opt->biogpt,prepare_config_and_inputs->prepare_config_and_inputs_for_common
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def test_biogpt_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = BioGptForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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# Copied from tests.models.opt.test_modeling_opt.OPTModelTest.test_opt_sequence_classification_model_for_multi_label with OPT->BioGpt,opt->biogpt,prepare_config_and_inputs->prepare_config_and_inputs_for_common
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def test_biogpt_sequence_classification_model_for_multi_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "multi_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor(
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[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
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).to(torch.float)
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model = BioGptForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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@unittest.skip("The `input_embeds` when fed don't produce the same results.")
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def test_beam_sample_generate(self):
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pass
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@require_torch
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class BioGptModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_lm_head_model(self):
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model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
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input_ids = torch.tensor([[2, 4805, 9, 656, 21]])
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output = model(input_ids)[0]
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vocab_size = 42384
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|
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expected_shape = torch.Size((1, 5, vocab_size))
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self.assertEqual(output.shape, expected_shape)
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|
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expected_slice = torch.tensor(
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[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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|
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@slow
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def test_biogpt_generation(self):
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tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
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model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
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model.to(torch_device)
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|
|
|
torch.manual_seed(0)
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tokenized = tokenizer("COVID-19 is", return_tensors="pt").to(torch_device)
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output_ids = model.generate(
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**tokenized,
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min_length=100,
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max_length=1024,
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|
num_beams=5,
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early_stopping=True,
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|
)
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output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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|
|
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EXPECTED_OUTPUT_STR = (
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|
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
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" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
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|
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
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" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
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|
" more than 800,000 deaths."
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|
)
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|
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
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