672 lines
26 KiB
Python
672 lines
26 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 PLBART model. """
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import copy
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import tempfile
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import unittest
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from transformers import PLBartConfig, is_torch_available
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from transformers.testing_utils import (
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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require_torch_fp16,
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slow,
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torch_device,
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)
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from transformers.utils import cached_property
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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
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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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AutoTokenizer,
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PLBartForCausalLM,
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PLBartForConditionalGeneration,
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PLBartForSequenceClassification,
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PLBartModel,
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)
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from transformers.models.plbart.modeling_plbart import PLBartDecoder, PLBartEncoder
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def prepare_plbart_inputs_dict(
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config,
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input_ids,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_mask=None,
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cross_attn_head_mask=None,
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):
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if attention_mask is None:
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attention_mask = input_ids.ne(config.pad_token_id)
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if decoder_attention_mask is None:
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decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
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if head_mask is None:
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head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
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if decoder_head_mask is None:
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decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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if cross_attn_head_mask is None:
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cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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return {
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"input_ids": input_ids,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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"decoder_attention_mask": attention_mask,
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"head_mask": head_mask,
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"decoder_head_mask": decoder_head_mask,
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"cross_attn_head_mask": cross_attn_head_mask,
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}
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class PLBartModelTester:
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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_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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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=100,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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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_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.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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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_ids = input_ids.clamp(3)
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input_ids[:, -1] = self.eos_token_id # Eos Token
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decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = self.get_config()
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inputs_dict = prepare_plbart_inputs_dict(config, input_ids, decoder_input_ids)
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return config, inputs_dict
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def get_config(self):
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return PLBartConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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)
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
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model = PLBartModel(config=config).get_decoder().to(torch_device).eval()
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input_ids = inputs_dict["input_ids"]
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attention_mask = inputs_dict["attention_mask"]
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head_mask = inputs_dict["head_mask"]
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_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_with_past_key_values = model(
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next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values
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)
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output_from_past = output_with_past_key_values["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[:, -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 check_encoder_decoder_model_standalone(self, config, inputs_dict):
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model = PLBartModel(config=config).to(torch_device).eval()
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outputs = model(**inputs_dict)
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encoder_last_hidden_state = outputs.encoder_last_hidden_state
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last_hidden_state = outputs.last_hidden_state
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with tempfile.TemporaryDirectory() as tmpdirname:
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encoder = model.get_encoder()
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encoder.save_pretrained(tmpdirname)
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encoder = PLBartEncoder.from_pretrained(tmpdirname).to(torch_device)
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encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
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0
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]
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self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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decoder = model.get_decoder()
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decoder.save_pretrained(tmpdirname)
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decoder = PLBartDecoder.from_pretrained(tmpdirname).to(torch_device)
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last_hidden_state_2 = decoder(
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input_ids=inputs_dict["decoder_input_ids"],
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attention_mask=inputs_dict["decoder_attention_mask"],
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encoder_hidden_states=encoder_last_hidden_state,
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encoder_attention_mask=inputs_dict["attention_mask"],
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)[0]
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self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
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@require_torch
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class PLBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(PLBartModel, PLBartForConditionalGeneration, PLBartForSequenceClassification) if is_torch_available() else ()
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)
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all_generative_model_classes = (PLBartForConditionalGeneration,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"conversational": PLBartForConditionalGeneration,
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"feature-extraction": PLBartModel,
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"summarization": PLBartForConditionalGeneration,
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"text-classification": PLBartForSequenceClassification,
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"text-generation": PLBartForCausalLM,
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"text2text-generation": PLBartForConditionalGeneration,
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"translation": PLBartForConditionalGeneration,
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"zero-shot": PLBartForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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is_encoder_decoder = True
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fx_compatible = False # Fix me Michael
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test_pruning = False
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test_missing_keys = False
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "TranslationPipelineTests":
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# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
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# `PLBartConfig` was never used in pipeline tests: cannot create a simple tokenizer.
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return True
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return False
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def setUp(self):
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self.model_tester = PLBartModelTester(self)
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self.config_tester = ConfigTester(self, config_class=PLBartConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_save_load_strict(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
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self.assertEqual(info["missing_keys"], [])
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def test_decoder_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_decoder_model_past_large_inputs(*config_and_inputs)
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def test_encoder_decoder_model_standalone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
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self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
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# PLBartForSequenceClassification does not support inputs_embeds
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def test_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in (PLBartModel, PLBartForConditionalGeneration):
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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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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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if not self.is_encoder_decoder:
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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else:
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encoder_input_ids = inputs["input_ids"]
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decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
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del inputs["input_ids"]
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inputs.pop("decoder_input_ids", None)
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wte = model.get_input_embeddings()
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if not self.is_encoder_decoder:
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inputs["inputs_embeds"] = wte(input_ids)
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else:
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inputs["inputs_embeds"] = wte(encoder_input_ids)
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inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
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with torch.no_grad():
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model(**inputs)[0]
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@require_torch_fp16
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def test_generate_fp16(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs()
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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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model = PLBartForConditionalGeneration(config).eval().to(torch_device)
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model.half()
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model.generate(input_ids, attention_mask=attention_mask)
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model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
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@unittest.skip("Failing since #26752")
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def test_sample_generate(self):
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pass
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def assert_tensors_close(a, b, atol=1e-12, prefix=""):
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"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
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if a is None and b is None:
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return True
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try:
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if torch.allclose(a, b, atol=atol):
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return True
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raise
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except Exception:
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pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
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if a.numel() > 100:
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msg = f"tensor values are {pct_different:.1%} percent different."
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else:
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msg = f"{a} != {b}"
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if prefix:
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msg = prefix + ": " + msg
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raise AssertionError(msg)
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def _long_tensor(tok_lst):
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return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class AbstractSeq2SeqIntegrationTest(unittest.TestCase):
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maxDiff = 1000 # longer string compare tracebacks
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checkpoint_name = None
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@classmethod
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def setUpClass(cls):
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cls.tokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name, use_fast=False)
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return cls
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@cached_property
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def model(self):
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"""Only load the model if needed."""
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model = PLBartForConditionalGeneration.from_pretrained(self.checkpoint_name).to(torch_device)
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if "cuda" in torch_device:
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model = model.half()
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return model
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class PLBartJavaCsIntegrationTest(AbstractSeq2SeqIntegrationTest):
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checkpoint_name = "uclanlp/plbart-java-cs"
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src_text = [
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"public int maximum(int a, int b, int c){return Math.max(a, Math.max(b, c));}",
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"public int product(int a, int b, int c){return a*b*c;}",
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]
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tgt_text = [
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"public int maximum(int a, int b, int c){return Math.Max(",
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"public int Product(int a, int b, int c){return a * b *",
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]
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@slow
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def test_java_cs_generate_one(self):
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batch = self.tokenizer(
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["public int maximum(int a, int b, int c){return Math.max(a, Math.max(b, c));}"], return_tensors="pt"
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)
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batch = batch.to(torch_device)
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translated_tokens = self.model.generate(**batch)
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decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
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self.assertEqual(self.tgt_text[0], decoded[0])
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# self.assertEqual(self.tgt_text[1], decoded[1])
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@slow
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def test_java_cs_generate_batch(self):
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batch = self.tokenizer(self.src_text, return_tensors="pt", padding=True, truncation=True)
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batch = batch.to(torch_device)
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translated_tokens = self.model.generate(**batch)
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decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
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assert self.tgt_text == decoded
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def test_plbart_java_cs_config(self):
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plbart_models = ["uclanlp/plbart-java-cs"]
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expected = {"scale_embedding": True}
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for name in plbart_models:
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config = PLBartConfig.from_pretrained(name)
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for k, v in expected.items():
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try:
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self.assertEqual(v, getattr(config, k))
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except AssertionError as e:
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e.args += (name, k)
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raise
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def test_plbart_fast_forward(self):
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config = PLBartConfig(
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vocab_size=99,
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d_model=24,
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encoder_layers=2,
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decoder_layers=2,
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encoder_attention_heads=2,
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decoder_attention_heads=2,
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encoder_ffn_dim=32,
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decoder_ffn_dim=32,
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max_position_embeddings=48,
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add_final_layer_norm=True,
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)
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lm_model = PLBartForConditionalGeneration(config).to(torch_device)
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context = torch.tensor(
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[[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long
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)
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summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long)
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result = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary)
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expected_shape = (*summary.shape, config.vocab_size)
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self.assertEqual(result.logits.shape, expected_shape)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class PLBartBaseIntegrationTest(AbstractSeq2SeqIntegrationTest):
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checkpoint_name = "uclanlp/plbart-base"
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src_text = ["Is 0 the first Fibonacci number ?", "Find the sum of all prime numbers ."]
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tgt_text = ["0 the first Fibonacci number?", "the sum of all prime numbers.......... the the"]
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def test_base_generate(self):
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inputs = self.tokenizer([self.src_text[0]], return_tensors="pt").to(torch_device)
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src_lan = self.tokenizer._convert_lang_code_special_format("en_XX")
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translated_tokens = self.model.generate(
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input_ids=inputs["input_ids"].to(torch_device),
|
|
decoder_start_token_id=self.tokenizer.lang_code_to_id[src_lan],
|
|
)
|
|
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
|
|
self.assertEqual(self.tgt_text[0], decoded[0])
|
|
|
|
@slow
|
|
def test_fill_mask(self):
|
|
inputs = self.tokenizer(["Is 0 the <mask> Fibonacci <mask> ?"], return_tensors="pt").to(torch_device)
|
|
src_lan = self.tokenizer._convert_lang_code_special_format("en_XX")
|
|
outputs = self.model.generate(
|
|
inputs["input_ids"], decoder_start_token_id=self.tokenizer.lang_code_to_id[src_lan], num_beams=1
|
|
)
|
|
prediction: str = self.tokenizer.batch_decode(
|
|
outputs, clean_up_tokenization_spaces=True, skip_special_tokens=True
|
|
)[0]
|
|
self.assertEqual(prediction, "0 0 the 0 the 0 the 0 the 0 the 0 the 0 the 0 the")
|
|
|
|
|
|
class PLBartStandaloneDecoderModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
vocab_size=99,
|
|
batch_size=13,
|
|
d_model=16,
|
|
decoder_seq_length=7,
|
|
is_training=True,
|
|
is_decoder=True,
|
|
use_attention_mask=True,
|
|
use_cache=False,
|
|
use_labels=True,
|
|
decoder_start_token_id=2,
|
|
decoder_ffn_dim=32,
|
|
decoder_layers=2,
|
|
encoder_attention_heads=4,
|
|
decoder_attention_heads=4,
|
|
max_position_embeddings=30,
|
|
is_encoder_decoder=False,
|
|
pad_token_id=0,
|
|
bos_token_id=1,
|
|
eos_token_id=2,
|
|
scope=None,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.decoder_seq_length = decoder_seq_length
|
|
# For common tests
|
|
self.seq_length = self.decoder_seq_length
|
|
self.is_training = is_training
|
|
self.use_attention_mask = use_attention_mask
|
|
self.use_labels = use_labels
|
|
|
|
self.vocab_size = vocab_size
|
|
self.d_model = d_model
|
|
self.hidden_size = d_model
|
|
self.num_hidden_layers = decoder_layers
|
|
self.decoder_layers = decoder_layers
|
|
self.decoder_ffn_dim = decoder_ffn_dim
|
|
self.encoder_attention_heads = encoder_attention_heads
|
|
self.decoder_attention_heads = decoder_attention_heads
|
|
self.num_attention_heads = decoder_attention_heads
|
|
self.eos_token_id = eos_token_id
|
|
self.bos_token_id = bos_token_id
|
|
self.pad_token_id = pad_token_id
|
|
self.decoder_start_token_id = decoder_start_token_id
|
|
self.use_cache = use_cache
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.is_encoder_decoder = is_encoder_decoder
|
|
|
|
self.scope = None
|
|
self.decoder_key_length = decoder_seq_length
|
|
self.base_model_out_len = 2
|
|
self.decoder_attention_idx = 1
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
|
|
|
|
attention_mask = None
|
|
if self.use_attention_mask:
|
|
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
|
|
|
|
lm_labels = None
|
|
if self.use_labels:
|
|
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
|
|
|
|
config = PLBartConfig(
|
|
vocab_size=self.vocab_size,
|
|
d_model=self.d_model,
|
|
decoder_layers=self.decoder_layers,
|
|
decoder_ffn_dim=self.decoder_ffn_dim,
|
|
encoder_attention_heads=self.encoder_attention_heads,
|
|
decoder_attention_heads=self.decoder_attention_heads,
|
|
eos_token_id=self.eos_token_id,
|
|
bos_token_id=self.bos_token_id,
|
|
use_cache=self.use_cache,
|
|
pad_token_id=self.pad_token_id,
|
|
decoder_start_token_id=self.decoder_start_token_id,
|
|
max_position_embeddings=self.max_position_embeddings,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
)
|
|
|
|
return (config, input_ids, attention_mask, lm_labels)
|
|
|
|
def create_and_check_decoder_model_past(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
lm_labels,
|
|
):
|
|
config.use_cache = True
|
|
model = PLBartDecoder(config=config).to(torch_device).eval()
|
|
# first forward pass
|
|
outputs = model(input_ids, use_cache=True)
|
|
outputs_use_cache_conf = model(input_ids)
|
|
outputs_no_past = model(input_ids, use_cache=False)
|
|
|
|
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
|
|
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
|
|
|
|
past_key_values = outputs["past_key_values"]
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# append to next input_ids and
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
|
|
output_from_no_past = model(next_input_ids)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
|
|
|
# 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_attention_mask_past(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
lm_labels,
|
|
):
|
|
model = PLBartDecoder(config=config).to(torch_device).eval()
|
|
|
|
# create attention mask
|
|
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
|
|
|
half_seq_length = input_ids.shape[-1] // 2
|
|
attn_mask[:, half_seq_length:] = 0
|
|
|
|
# first forward pass
|
|
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# change a random masked slice from input_ids
|
|
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
|
|
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
|
|
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
|
|
|
|
# append to next input_ids and attn_mask
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
attn_mask = torch.cat(
|
|
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
|
|
dim=1,
|
|
)
|
|
|
|
# get two different outputs
|
|
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[
|
|
"last_hidden_state"
|
|
]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
|
|
|
# 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 prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(config, input_ids, attention_mask, lm_labels) = config_and_inputs
|
|
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class PLBartStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
|
all_model_classes = (PLBartDecoder, PLBartForCausalLM) if is_torch_available() else ()
|
|
all_generative_model_classes = (PLBartForCausalLM,) if is_torch_available() else ()
|
|
test_pruning = False
|
|
is_encoder_decoder = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = PLBartStandaloneDecoderModelTester(self, is_training=False)
|
|
self.config_tester = ConfigTester(self, config_class=PLBartConfig)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_decoder_model_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
|
|
|
|
def test_decoder_model_attn_mask_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
# decoder cannot keep gradients
|
|
return
|