972 lines
41 KiB
Python
972 lines
41 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace Inc.
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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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import copy
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import os.path
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import random
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import tempfile
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import unittest
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from typing import List
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from transformers import is_torch_available
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from transformers.testing_utils import require_multigpu, require_torch, slow, torch_device
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if is_torch_available():
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import torch
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import numpy as np
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from transformers import (
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AdaptiveEmbedding,
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PretrainedConfig,
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PreTrainedModel,
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BertModel,
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BertConfig,
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BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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top_k_top_p_filtering,
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)
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key:
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setattr(configs_no_init, key, 1e-10)
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return configs_no_init
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@require_torch
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class ModelTesterMixin:
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model_tester = None
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all_model_classes = ()
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all_generative_model_classes = ()
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test_torchscript = True
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test_pruning = True
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test_resize_embeddings = True
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test_head_masking = True
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test_missing_keys = True
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is_encoder_decoder = False
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def _prepare_for_class(self, inputs_dict, model_class):
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if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
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return {
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k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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if isinstance(v, torch.Tensor) and v.ndim != 0
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else v
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for k, v in inputs_dict.items()
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}
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return inputs_dict
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def test_save_load(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 self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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out_2 = outputs[0].cpu().numpy()
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out_2[np.isnan(out_2)] = 0
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname)
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model.to(torch_device)
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with torch.no_grad():
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after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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# Make sure we don't have nans
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out_1 = after_outputs[0].cpu().numpy()
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out_1[np.isnan(out_1)] = 0
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
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)
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def test_determinism(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 self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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chunk_length = getattr(self.model_tester, "chunk_length", None)
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if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
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encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs[-1]
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs[-1]
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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if self.is_encoder_decoder:
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correct_outlen = 4
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decoder_attention_idx = 1
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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decoder_attention_idx += 1
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# Question Answering model returns start_logits and end_logits
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if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
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correct_outlen += 1 # start_logits and end_logits instead of only 1 output
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decoder_attention_idx += 1
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self.assertEqual(out_len, correct_outlen)
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decoder_attentions = outputs[decoder_attention_idx]
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
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)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
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self_attentions = outputs[-1]
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(self_attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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def test_torchscript(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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self._create_and_check_torchscript(config, inputs_dict)
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def test_torchscript_output_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_attentions = True
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self._create_and_check_torchscript(config, inputs_dict)
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def test_torchscript_output_hidden_state(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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self._create_and_check_torchscript(config, inputs_dict)
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)["input_ids"] # Let's keep only input_ids
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try:
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traced_gpt2 = torch.jit.trace(model, inputs)
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_gpt2, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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loaded_model_state_dict = loaded_model.state_dict()
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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p2 = loaded_model_state_dict[layer_name]
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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def test_headmasking(self):
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if not self.test_head_masking:
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return
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global_rng.seed(42)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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global_rng.seed()
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = True
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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# Prepare head_mask
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# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
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head_mask = torch.ones(
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self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device,
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)
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head_mask[0, 0] = 0
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head_mask[-1, :-1] = 0
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head_mask.requires_grad_(requires_grad=True)
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inputs = self._prepare_for_class(inputs_dict, model_class).copy()
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inputs["head_mask"] = head_mask
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outputs = model(**inputs)
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# Test that we can get a gradient back for importance score computation
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output = sum(t.sum() for t in outputs[0])
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output = output.sum()
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output.backward()
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multihead_outputs = head_mask.grad
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attentions = outputs[-1]
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# Remove Nan
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for t in attentions:
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self.assertLess(
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torch.sum(torch.isnan(t)), t.numel() / 4
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) # Check we don't have more than 25% nans (arbitrary)
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attentions = [
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t.masked_fill(torch.isnan(t), 0.0) for t in attentions
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] # remove them (the test is less complete)
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self.assertIsNotNone(multihead_outputs)
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self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
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self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
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self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
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self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
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self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
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self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
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def test_head_pruning(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = False
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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heads_to_prune = {
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0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0],
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}
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model.prune_heads(heads_to_prune)
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_head_pruning_save_load_from_pretrained(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = False
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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heads_to_prune = {
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0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0],
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}
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model.prune_heads(heads_to_prune)
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with tempfile.TemporaryDirectory() as temp_dir_name:
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model.save_pretrained(temp_dir_name)
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model = model_class.from_pretrained(temp_dir_name)
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model.to(torch_device)
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_head_pruning_save_load_from_config_init(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = False
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heads_to_prune = {
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0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0],
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}
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config.pruned_heads = heads_to_prune
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_head_pruning_integration(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = False
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heads_to_prune = {0: [0], 1: [1, 2]}
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config.pruned_heads = heads_to_prune
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
|
|
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
|
|
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
|
|
|
|
with tempfile.TemporaryDirectory() as temp_dir_name:
|
|
model.save_pretrained(temp_dir_name)
|
|
model = model_class.from_pretrained(temp_dir_name)
|
|
model.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs[-1]
|
|
|
|
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
|
|
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
|
|
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
|
|
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
|
|
|
|
heads_to_prune = {0: [0], 2: [1, 2]}
|
|
model.prune_heads(heads_to_prune)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs[-1]
|
|
|
|
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
|
|
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
|
|
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
|
|
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
|
|
|
|
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
|
|
|
|
def test_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
hidden_states = outputs[-1]
|
|
|
|
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
|
|
if hasattr(self.model_tester, "encoder_seq_length"):
|
|
seq_length = self.model_tester.encoder_seq_length
|
|
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
|
|
seq_length = seq_length * self.model_tester.chunk_length
|
|
else:
|
|
seq_length = self.model_tester.seq_length
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
def test_resize_tokens_embeddings(self):
|
|
(original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
if self.model_tester.is_training is False:
|
|
model.eval()
|
|
|
|
model_vocab_size = config.vocab_size
|
|
# Retrieve the embeddings and clone theme
|
|
model_embed = model.resize_token_embeddings(model_vocab_size)
|
|
cloned_embeddings = model_embed.weight.clone()
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
|
|
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
# Input ids should be clamped to the maximum size of the vocabulary
|
|
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
|
|
models_equal = True
|
|
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_model_common_attributes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding, AdaptiveEmbedding))
|
|
model.set_input_embeddings(torch.nn.Embedding(10, 10))
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
|
|
|
|
def test_correct_missing_keys(self):
|
|
if not self.test_missing_keys:
|
|
return
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
base_model_prefix = model.base_model_prefix
|
|
|
|
if hasattr(model, base_model_prefix):
|
|
with tempfile.TemporaryDirectory() as temp_dir_name:
|
|
model.base_model.save_pretrained(temp_dir_name)
|
|
model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)
|
|
|
|
with self.subTest(msg="Missing keys for {}".format(model.__class__.__name__)):
|
|
self.assertGreater(len(loading_info["missing_keys"]), 0)
|
|
|
|
def test_tie_model_weights(self):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def check_same_values(layer_1, layer_2):
|
|
equal = True
|
|
for p1, p2 in zip(layer_1.weight, layer_2.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
equal = False
|
|
return equal
|
|
|
|
for model_class in self.all_model_classes:
|
|
config.torchscript = True
|
|
model_not_tied = model_class(config)
|
|
if model_not_tied.get_output_embeddings() is None:
|
|
continue
|
|
|
|
config_tied = copy.deepcopy(config)
|
|
config_tied.torchscript = False
|
|
model_tied = model_class(config_tied)
|
|
params_tied = list(model_tied.parameters())
|
|
# Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(check_same_values(embeddings, decoding))
|
|
|
|
# # Check that after modification, they remain the same.
|
|
# embeddings.weight.data.div_(2)
|
|
# # Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
|
|
# self.assertTrue(check_same_values(embeddings, decoding))
|
|
|
|
# # Check that after modification, they remain the same.
|
|
# decoding.weight.data.div_(4)
|
|
# # Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
|
|
# self.assertTrue(check_same_values(embeddings, decoding))
|
|
|
|
# Check that after resize they remain tied.
|
|
model_tied.resize_token_embeddings(config.vocab_size + 10)
|
|
params_tied_2 = list(model_tied.parameters())
|
|
self.assertEqual(len(params_tied_2), len(params_tied))
|
|
|
|
# decoding.weight.data.mul_(20)
|
|
# # Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
|
|
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
|
|
|
|
def test_inputs_embeds(self):
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
|
|
if not self.is_encoder_decoder:
|
|
input_ids = inputs["input_ids"]
|
|
del inputs["input_ids"]
|
|
else:
|
|
encoder_input_ids = inputs["input_ids"]
|
|
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
|
|
del inputs["input_ids"]
|
|
inputs.pop("decoder_input_ids", None)
|
|
|
|
wte = model.get_input_embeddings()
|
|
if not self.is_encoder_decoder:
|
|
inputs["inputs_embeds"] = wte(input_ids)
|
|
else:
|
|
inputs["inputs_embeds"] = wte(encoder_input_ids)
|
|
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
|
|
|
|
with torch.no_grad():
|
|
model(**inputs)
|
|
|
|
def test_lm_head_model_random_no_beam_search_generate(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
input_ids = inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]
|
|
|
|
# make sure that input_ids is at most of size 15
|
|
input_ids = input_ids[..., :15]
|
|
|
|
# iterate over all generative models
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config).to(torch_device)
|
|
model.eval()
|
|
|
|
if config.bos_token_id is None:
|
|
# if bos token id is not defined, model needs input_ids
|
|
with self.assertRaises(AssertionError):
|
|
model.generate(do_sample=True, max_length=5)
|
|
# num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True))
|
|
else:
|
|
# num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(do_sample=True, max_length=5))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# generating multiple sequences when no beam search generation
|
|
# is not allowed as it would always generate the same sequences
|
|
model.generate(input_ids, do_sample=False, num_return_sequences=2)
|
|
|
|
# num_return_sequences > 1, sample
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_return_sequences=2))
|
|
|
|
# check bad words tokens language generation
|
|
# create list of 1-seq bad token and list of 2-seq of bad tokens
|
|
bad_words_ids = [
|
|
self._generate_random_bad_tokens(1, model.config),
|
|
self._generate_random_bad_tokens(2, model.config),
|
|
]
|
|
output_tokens = model.generate(
|
|
input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
|
|
)
|
|
# only count generated tokens
|
|
generated_ids = output_tokens[:, input_ids.shape[-1] :]
|
|
self.assertFalse(self._check_match_tokens(generated_ids.tolist(), bad_words_ids))
|
|
|
|
def test_lm_head_model_random_beam_search_generate(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
input_ids = (inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]).to(
|
|
torch_device
|
|
)
|
|
|
|
# make sure that input_ids is at most of size 15
|
|
input_ids = input_ids[..., :15]
|
|
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config).to(torch_device)
|
|
model.eval()
|
|
|
|
if config.bos_token_id is None:
|
|
# if bos token id is not defined mobel needs input_ids, num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2))
|
|
else:
|
|
# num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(do_sample=True, max_length=5, num_beams=2))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# generating more sequences than having beams leads is not possible
|
|
model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
|
|
|
|
# num_return_sequences > 1, sample
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2, num_return_sequences=2,))
|
|
# num_return_sequences > 1, greedy
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2))
|
|
|
|
# check bad words tokens language generation
|
|
# create list of 1-seq bad token and list of 2-seq of bad tokens
|
|
bad_words_ids = [
|
|
self._generate_random_bad_tokens(1, model.config),
|
|
self._generate_random_bad_tokens(2, model.config),
|
|
]
|
|
output_tokens = model.generate(
|
|
input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
|
|
)
|
|
# only count generated tokens
|
|
generated_ids = output_tokens[:, input_ids.shape[-1] :]
|
|
self.assertFalse(self._check_match_tokens(generated_ids.tolist(), bad_words_ids))
|
|
|
|
def _generate_random_bad_tokens(self, num_bad_tokens: int, config) -> List[int]:
|
|
# special tokens cannot be bad tokens
|
|
special_tokens = [x for x in [config.bos_token_id, config.eos_token_id, config.pad_token_id] if x is not None]
|
|
# create random bad tokens that are not special tokens
|
|
bad_tokens = []
|
|
while len(bad_tokens) < num_bad_tokens:
|
|
token = ids_tensor((1, 1), self.model_tester.vocab_size).squeeze(0).cpu().numpy()[0]
|
|
if token not in special_tokens:
|
|
bad_tokens.append(token)
|
|
return bad_tokens
|
|
|
|
def _check_generated_ids(self, output_ids):
|
|
for token_id in output_ids[0].tolist():
|
|
self.assertGreaterEqual(token_id, 0)
|
|
self.assertLess(token_id, self.model_tester.vocab_size)
|
|
|
|
def _check_match_tokens(self, generated_ids, bad_words_ids):
|
|
# for all bad word tokens
|
|
for bad_word_ids in bad_words_ids:
|
|
# for all slices in batch
|
|
for generated_ids_slice in generated_ids:
|
|
# for all word idx
|
|
for i in range(len(bad_word_ids), len(generated_ids_slice)):
|
|
# if tokens match
|
|
if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids:
|
|
return True
|
|
return False
|
|
|
|
@require_multigpu
|
|
def test_multigpu_data_parallel_forward(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# some params shouldn't be scattered by nn.DataParallel
|
|
# so just remove them if they are present.
|
|
blacklist_non_batched_params = ["head_mask"]
|
|
for k in blacklist_non_batched_params:
|
|
inputs_dict.pop(k, None)
|
|
|
|
# move input tensors to cuda:O
|
|
for k, v in inputs_dict.items():
|
|
if torch.is_tensor(v):
|
|
inputs_dict[k] = v.to(0)
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=config)
|
|
model.to(0)
|
|
model.eval()
|
|
|
|
# Wrap model in nn.DataParallel
|
|
model = torch.nn.DataParallel(model)
|
|
with torch.no_grad():
|
|
_ = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
|
|
global_rng = random.Random()
|
|
|
|
|
|
def ids_tensor(shape, vocab_size, rng=None, name=None):
|
|
# Creates a random int32 tensor of the shape within the vocab size
|
|
if rng is None:
|
|
rng = global_rng
|
|
|
|
total_dims = 1
|
|
for dim in shape:
|
|
total_dims *= dim
|
|
|
|
values = []
|
|
for _ in range(total_dims):
|
|
values.append(rng.randint(0, vocab_size - 1))
|
|
|
|
return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()
|
|
|
|
|
|
def floats_tensor(shape, scale=1.0, rng=None, name=None):
|
|
"""Creates a random float32 tensor"""
|
|
if rng is None:
|
|
rng = global_rng
|
|
|
|
total_dims = 1
|
|
for dim in shape:
|
|
total_dims *= dim
|
|
|
|
values = []
|
|
for _ in range(total_dims):
|
|
values.append(rng.random() * scale)
|
|
|
|
return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
|
|
|
|
|
|
@require_torch
|
|
class ModelUtilsTest(unittest.TestCase):
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
config = BertConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, PretrainedConfig)
|
|
|
|
model = BertModel.from_pretrained(model_name)
|
|
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, PreTrainedModel)
|
|
for value in loading_info.values():
|
|
self.assertEqual(len(value), 0)
|
|
|
|
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
|
|
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
|
|
self.assertEqual(model.config.output_hidden_states, True)
|
|
self.assertEqual(model.config, config)
|
|
|
|
|
|
@require_torch
|
|
class UtilsFunctionsTest(unittest.TestCase):
|
|
|
|
# tests whether the top_k_top_p function behaves as expected
|
|
def test_top_k_top_p_filtering(self):
|
|
logits = torch.tensor(
|
|
[
|
|
[
|
|
8.2220991, # 3rd highest value; idx. 0
|
|
-0.5620044,
|
|
5.23229752,
|
|
4.0386393,
|
|
-6.8798378,
|
|
-0.54785802,
|
|
-3.2012153,
|
|
2.92777176,
|
|
1.88171953,
|
|
7.35341276, # 5th highest value; idx. 9
|
|
8.43207833, # 2nd highest value; idx. 10
|
|
-9.85711836,
|
|
-5.96209236,
|
|
-1.13039161,
|
|
-7.1115294,
|
|
-0.8369633,
|
|
-5.3186408,
|
|
7.06427407,
|
|
0.81369344,
|
|
-0.82023817,
|
|
-5.9179796,
|
|
0.58813443,
|
|
-6.99778438,
|
|
4.71551189,
|
|
-0.18771637,
|
|
7.44020759, # 4th highest value; idx. 25
|
|
9.38450987, # 1st highest value; idx. 26
|
|
2.12662941,
|
|
-9.32562038,
|
|
2.35652522,
|
|
], # cummulative prob of 5 highest values <= 0.6
|
|
[
|
|
0.58425518,
|
|
4.53139238,
|
|
-5.57510464,
|
|
-6.28030699,
|
|
-7.19529503,
|
|
-4.02122551,
|
|
1.39337037,
|
|
-6.06707057,
|
|
1.59480517,
|
|
-9.643119,
|
|
0.03907799,
|
|
0.67231762,
|
|
-8.88206726,
|
|
6.27115922, # 4th highest value; idx. 13
|
|
2.28520723,
|
|
4.82767506,
|
|
4.30421368,
|
|
8.8275313, # 2nd highest value; idx. 17
|
|
5.44029958, # 5th highest value; idx. 18
|
|
-4.4735794,
|
|
7.38579536, # 3rd highest value; idx. 20
|
|
-2.91051663,
|
|
2.61946077,
|
|
-2.5674762,
|
|
-9.48959302,
|
|
-4.02922645,
|
|
-1.35416918,
|
|
9.67702323, # 1st highest value; idx. 27
|
|
-5.89478553,
|
|
1.85370467,
|
|
], # cummulative prob of 5 highest values <= 0.6
|
|
],
|
|
dtype=torch.float,
|
|
device=torch_device,
|
|
)
|
|
|
|
non_inf_expected_idx = torch.tensor(
|
|
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
) # expected non filtered idx as noted above
|
|
|
|
non_inf_expected_output = torch.tensor(
|
|
[
|
|
8.2221,
|
|
7.3534,
|
|
8.4321,
|
|
7.4402,
|
|
9.3845,
|
|
6.2712,
|
|
8.8275,
|
|
5.4403,
|
|
7.3858,
|
|
9.6770,
|
|
], # expected non filtered values as noted above
|
|
dtype=torch.float,
|
|
device=torch_device,
|
|
)
|
|
|
|
output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
|
|
non_inf_output = output[output != -float("inf")].to(device=torch_device)
|
|
non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)
|
|
|
|
self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
|
|
self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))
|