mirror of
https://github.com/huggingface/transformers.git
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90178b0cef
* Add option to load a pretrained model with mismatched shapes * Fail at loading when mismatched shapes in Flax * Fix tests * Update src/transformers/modeling_flax_utils.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Address review comments Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
1744 lines
75 KiB
Python
Executable File
1744 lines
75 KiB
Python
Executable File
# 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 gc
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import inspect
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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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import warnings
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from typing import Dict, List, Tuple
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from huggingface_hub import HfApi
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from requests.exceptions import HTTPError
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from transformers import AutoModel, AutoModelForSequenceClassification, is_torch_available, logging
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from transformers.file_utils import WEIGHTS_NAME, is_torch_fx_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import (
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ENDPOINT_STAGING,
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PASS,
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USER,
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CaptureLogger,
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TestCasePlus,
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is_staging_test,
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require_torch,
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require_torch_multi_gpu,
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slow,
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torch_device,
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)
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if is_torch_available():
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import numpy as np
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import torch
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from torch import nn
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from transformers import (
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BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
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MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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MODEL_MAPPING,
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AdaptiveEmbedding,
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BertConfig,
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BertModel,
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PretrainedConfig,
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PreTrainedModel,
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T5Config,
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T5ForConditionalGeneration,
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)
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if is_torch_fx_available():
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from transformers.utils.fx import symbolic_trace
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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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TINY_T5 = "patrickvonplaten/t5-tiny-random"
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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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fx_ready_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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test_model_parallel = False
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is_encoder_decoder = False
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test_sequence_classification_problem_types = False
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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inputs_dict = {
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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 > 1
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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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if return_labels:
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if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
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elif model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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inputs_dict["start_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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inputs_dict["end_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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elif model_class in [
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*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
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*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING),
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*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
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]:
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inputs_dict["labels"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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elif model_class in [
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*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
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*get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
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*get_values(MODEL_FOR_MASKED_LM_MAPPING),
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*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
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]:
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
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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_save_load_keys_to_ignore_on_save(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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_keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None)
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if _keys_to_ignore_on_save is None:
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continue
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# check the keys are in the original state_dict
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for k in _keys_to_ignore_on_save:
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self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys()))
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# check that certain keys didn't get saved with the model
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
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state_dict_saved = torch.load(output_model_file)
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for k in _keys_to_ignore_on_save:
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self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys()))
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# Test we can load the state dict in the model, necessary for the checkpointing API in Trainer.
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load_result = model.load_state_dict(state_dict_saved, strict=False)
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self.assertTrue(
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len(load_result.missing_keys) == 0
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or set(load_result.missing_keys) == set(model._keys_to_ignore_on_save)
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)
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self.assertTrue(len(load_result.unexpected_keys) == 0)
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def _mock_init_weights(self, module):
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if hasattr(module, "weight") and module.weight is not None:
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module.weight.data.fill_(3)
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if hasattr(module, "bias") and module.bias is not None:
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module.bias.data.fill_(3)
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def test_save_load_fast_init_from_base(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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base_class = MODEL_MAPPING[config.__class__]
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if isinstance(base_class, tuple):
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base_class = base_class[0]
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for model_class in self.all_model_classes:
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if model_class == base_class:
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continue
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# make a copy of model class to not break future tests
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# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
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class CopyClass(model_class):
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pass
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model_class_copy = CopyClass
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# make sure that all keys are expected for test
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model_class_copy._keys_to_ignore_on_load_missing = []
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# make init deterministic, but make sure that
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# non-initialized weights throw errors nevertheless
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model_class_copy._init_weights = self._mock_init_weights
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model = base_class(config)
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state_dict = model.state_dict()
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# this will often delete a single weight of a multi-weight module
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# to test an edge case
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random_key_to_del = random.choice(list(state_dict.keys()))
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del state_dict[random_key_to_del]
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# check that certain keys didn't get saved with the model
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
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model_fast_init = model_class_copy.from_pretrained(tmpdirname)
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model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False)
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for key in model_fast_init.state_dict().keys():
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max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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def test_save_load_fast_init_to_base(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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base_class = MODEL_MAPPING[config.__class__]
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if isinstance(base_class, tuple):
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base_class = base_class[0]
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for model_class in self.all_model_classes:
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if model_class == base_class:
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continue
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# make a copy of model class to not break future tests
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# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
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class CopyClass(base_class):
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pass
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base_class_copy = CopyClass
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# make sure that all keys are expected for test
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base_class_copy._keys_to_ignore_on_load_missing = []
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# make init deterministic, but make sure that
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# non-initialized weights throw errors nevertheless
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base_class_copy._init_weights = self._mock_init_weights
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model = model_class(config)
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state_dict = model.state_dict()
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# this will often delete a single weight of a multi-weight module
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# to test an edge case
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random_key_to_del = random.choice(list(state_dict.keys()))
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del state_dict[random_key_to_del]
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# check that certain keys didn't get saved with the model
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.config.save_pretrained(tmpdirname)
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torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
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model_fast_init = base_class_copy.from_pretrained(tmpdirname)
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model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False)
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for key in model_fast_init.state_dict().keys():
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max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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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=f"Parameter {name} of model {model_class} seems not properly initialized",
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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_forward_signature(self):
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config, _ = 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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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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if model.config.is_encoder_decoder:
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expected_arg_names = [
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"input_ids",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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]
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expected_arg_names.extend(
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["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
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if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
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else ["encoder_outputs"]
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)
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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else:
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expected_arg_names = ["input_ids"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_training(self):
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if not self.model_tester.is_training:
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return
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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for model_class in self.all_model_classes:
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if model_class in get_values(MODEL_MAPPING):
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.model_tester.is_training or not hasattr(config, "gradient_checkpointing"):
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return
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config.gradient_checkpointing = True
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config.use_cache = False
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config.return_dict = True
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for model_class in self.all_model_classes:
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if model_class in get_values(MODEL_MAPPING):
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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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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config.return_dict = True
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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, "decoder_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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config.return_dict = 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.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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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.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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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 = 5
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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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# Question Answering model returns start_logits and end_logits
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if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
|
|
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
|
|
if "past_key_values" in outputs:
|
|
correct_outlen += 1 # past_key_values have been returned
|
|
|
|
self.assertEqual(out_len, correct_outlen)
|
|
|
|
# decoder attentions
|
|
decoder_attentions = outputs.decoder_attentions
|
|
self.assertIsInstance(decoder_attentions, (list, tuple))
|
|
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(decoder_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
|
|
)
|
|
|
|
# cross attentions
|
|
cross_attentions = outputs.cross_attentions
|
|
self.assertIsInstance(cross_attentions, (list, tuple))
|
|
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(cross_attentions[0].shape[-3:]),
|
|
[
|
|
self.model_tester.num_attention_heads,
|
|
decoder_seq_length,
|
|
encoder_key_length,
|
|
],
|
|
)
|
|
|
|
# Check attention is always last and order is fine
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
if hasattr(self.model_tester, "num_hidden_states_types"):
|
|
added_hidden_states = self.model_tester.num_hidden_states_types
|
|
elif self.is_encoder_decoder:
|
|
added_hidden_states = 2
|
|
else:
|
|
added_hidden_states = 1
|
|
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
|
|
|
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
|
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
|
if chunk_length is not None:
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-4:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
|
|
)
|
|
else:
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
|
|
@slow
|
|
def test_torchscript(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
self._create_and_check_torchscript(config, inputs_dict)
|
|
|
|
@slow
|
|
def test_torchscript_output_attentions(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_attentions = True
|
|
self._create_and_check_torchscript(config, inputs_dict)
|
|
|
|
@slow
|
|
def test_torchscript_output_hidden_state(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
self._create_and_check_torchscript(config, inputs_dict)
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.torchscript = True
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
try:
|
|
if model.config.is_encoder_decoder:
|
|
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
|
|
input_ids = inputs["input_ids"]
|
|
attention_mask = inputs["attention_mask"]
|
|
decoder_input_ids = inputs["decoder_input_ids"]
|
|
decoder_attention_mask = inputs["decoder_attention_mask"]
|
|
traced_model = torch.jit.trace(
|
|
model, (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
|
|
)
|
|
else:
|
|
input_ids = inputs["input_ids"]
|
|
traced_model = torch.jit.trace(model, input_ids)
|
|
except RuntimeError:
|
|
self.fail("Couldn't trace module.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
|
|
|
try:
|
|
torch.jit.save(traced_model, pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't save module.")
|
|
|
|
try:
|
|
loaded_model = torch.jit.load(pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't load module.")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
loaded_model.to(torch_device)
|
|
loaded_model.eval()
|
|
|
|
model_state_dict = model.state_dict()
|
|
loaded_model_state_dict = loaded_model.state_dict()
|
|
|
|
non_persistent_buffers = {}
|
|
for key in loaded_model_state_dict.keys():
|
|
if key not in model_state_dict.keys():
|
|
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
|
|
|
loaded_model_state_dict = {
|
|
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
|
}
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
models_equal = True
|
|
for layer_name, p1 in model_state_dict.items():
|
|
if layer_name in loaded_model_state_dict:
|
|
p2 = loaded_model_state_dict[layer_name]
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_torch_fx(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
self._create_and_check_torch_fx_tracing(config, inputs_dict)
|
|
|
|
def test_torch_fx_output_loss(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True)
|
|
|
|
def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
|
|
if not is_torch_fx_available():
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.return_dict = False
|
|
|
|
for model_class in self.fx_ready_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)
|
|
|
|
try:
|
|
if model.config.is_encoder_decoder:
|
|
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
|
|
input_ids = inputs["input_ids"]
|
|
decoder_attention_mask = inputs["decoder_attention_mask"]
|
|
labels = inputs.get("labels", None)
|
|
input_names = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask"]
|
|
if labels is not None:
|
|
input_names.append("labels")
|
|
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
|
|
|
model_output = model(**filtered_inputs)
|
|
|
|
batch_size = input_ids.shape[0]
|
|
encoder_sequence_length = input_ids.shape[1]
|
|
decoder_sequence_length = decoder_attention_mask.shape[1]
|
|
|
|
traced_model = symbolic_trace(
|
|
model,
|
|
input_names,
|
|
batch_size=batch_size,
|
|
sequence_length=[encoder_sequence_length, decoder_sequence_length],
|
|
)
|
|
|
|
traced_output = traced_model(**filtered_inputs)
|
|
|
|
else:
|
|
input_names = ["input_ids", "attention_mask", "token_type_ids"]
|
|
input_ids = inputs["input_ids"]
|
|
|
|
labels = inputs.get("labels", None)
|
|
start_positions = inputs.get("start_positions", None)
|
|
end_positions = inputs.get("end_positions", None)
|
|
if labels is not None:
|
|
input_names.append("labels")
|
|
if start_positions is not None:
|
|
input_names.append("start_positions")
|
|
if end_positions is not None:
|
|
input_names.append("end_positions")
|
|
|
|
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
|
input_names = filtered_inputs.keys()
|
|
|
|
model_output = model(**filtered_inputs)
|
|
|
|
rank = len(input_ids.shape)
|
|
if rank == 2:
|
|
batch_size, sequence_length = input_ids.shape
|
|
num_choices = -1
|
|
elif rank == 3:
|
|
batch_size, num_choices, sequence_length = input_ids.shape
|
|
else:
|
|
raise NotImplementedError(
|
|
f"symbolic_trace automatic parameters inference not implemented for input of rank {rank}."
|
|
)
|
|
|
|
traced_model = symbolic_trace(
|
|
model,
|
|
input_names,
|
|
batch_size=batch_size,
|
|
sequence_length=sequence_length,
|
|
num_choices=num_choices,
|
|
)
|
|
traced_output = traced_model(**filtered_inputs)
|
|
|
|
except RuntimeError:
|
|
self.fail("Couldn't trace module.")
|
|
|
|
def flatten_output(output):
|
|
flatten = []
|
|
for x in output:
|
|
if isinstance(x, (tuple, list)):
|
|
flatten += flatten_output(x)
|
|
elif not isinstance(x, torch.Tensor):
|
|
continue
|
|
else:
|
|
flatten.append(x)
|
|
return flatten
|
|
|
|
model_output = flatten_output(model_output)
|
|
traced_output = flatten_output(traced_output)
|
|
num_outputs = len(model_output)
|
|
|
|
for i in range(num_outputs):
|
|
self.assertTrue(
|
|
torch.allclose(model_output[i], traced_output[i]),
|
|
f"traced {i}th output doesn't match model {i}th output for {model_class}",
|
|
)
|
|
|
|
def test_headmasking(self):
|
|
if not self.test_head_masking:
|
|
return
|
|
|
|
global_rng.seed(42)
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
global_rng.seed()
|
|
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = True
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# Prepare head_mask
|
|
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
|
|
head_mask = torch.ones(
|
|
self.model_tester.num_hidden_layers,
|
|
self.model_tester.num_attention_heads,
|
|
device=torch_device,
|
|
)
|
|
head_mask[0, 0] = 0
|
|
head_mask[-1, :-1] = 0
|
|
head_mask.requires_grad_(requires_grad=True)
|
|
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
|
|
inputs["head_mask"] = head_mask
|
|
if model.config.is_encoder_decoder:
|
|
signature = inspect.signature(model.forward)
|
|
arg_names = [*signature.parameters.keys()]
|
|
if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model
|
|
inputs["decoder_head_mask"] = head_mask
|
|
if "cross_attn_head_mask" in arg_names:
|
|
inputs["cross_attn_head_mask"] = head_mask
|
|
outputs = model(**inputs, return_dict=True)
|
|
|
|
# Test that we can get a gradient back for importance score computation
|
|
output = sum(t.sum() for t in outputs[0])
|
|
output = output.sum()
|
|
output.backward()
|
|
multihead_outputs = head_mask.grad
|
|
|
|
self.assertIsNotNone(multihead_outputs)
|
|
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
|
|
|
|
def check_attentions_validity(attentions):
|
|
# Remove Nan
|
|
for t in attentions:
|
|
self.assertLess(
|
|
torch.sum(torch.isnan(t)), t.numel() / 4
|
|
) # Check we don't have more than 25% nans (arbitrary)
|
|
attentions = [
|
|
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
|
|
] # remove them (the test is less complete)
|
|
|
|
self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
|
|
self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
|
|
if len(attentions) > 2: # encoder-decoder models have only 2 layers in each module
|
|
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
|
|
self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
|
|
self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
|
|
|
|
if model.config.is_encoder_decoder:
|
|
check_attentions_validity(outputs.encoder_attentions)
|
|
check_attentions_validity(outputs.decoder_attentions)
|
|
check_attentions_validity(outputs.cross_attentions)
|
|
else:
|
|
check_attentions_validity(outputs.attentions)
|
|
|
|
def test_head_pruning(self):
|
|
if not self.test_pruning:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
(
|
|
config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
if "head_mask" in inputs_dict:
|
|
del inputs_dict["head_mask"]
|
|
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = False
|
|
model = model_class(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
heads_to_prune = {
|
|
0: list(range(1, self.model_tester.num_attention_heads)),
|
|
-1: [0],
|
|
}
|
|
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], 1)
|
|
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
|
|
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
|
|
|
|
def test_head_pruning_save_load_from_pretrained(self):
|
|
if not self.test_pruning:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
(
|
|
config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
if "head_mask" in inputs_dict:
|
|
del inputs_dict["head_mask"]
|
|
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = False
|
|
model = model_class(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
heads_to_prune = {
|
|
0: list(range(1, self.model_tester.num_attention_heads)),
|
|
-1: [0],
|
|
}
|
|
model.prune_heads(heads_to_prune)
|
|
|
|
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], 1)
|
|
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
|
|
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
|
|
|
|
def test_head_pruning_save_load_from_config_init(self):
|
|
if not self.test_pruning:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
(
|
|
config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
if "head_mask" in inputs_dict:
|
|
del inputs_dict["head_mask"]
|
|
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = False
|
|
|
|
heads_to_prune = {
|
|
0: list(range(1, self.model_tester.num_attention_heads)),
|
|
-1: [0],
|
|
}
|
|
config.pruned_heads = heads_to_prune
|
|
|
|
model = model_class(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs[-1]
|
|
|
|
self.assertEqual(attentions[0].shape[-3], 1)
|
|
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
|
|
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
|
|
|
|
def test_head_pruning_integration(self):
|
|
if not self.test_pruning:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
(
|
|
config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
if "head_mask" in inputs_dict:
|
|
del inputs_dict["head_mask"]
|
|
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = False
|
|
|
|
heads_to_prune = {0: [0], 1: [1, 2]}
|
|
config.pruned_heads = heads_to_prune
|
|
|
|
model = model_class(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
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)
|
|
|
|
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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
|
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
|
)
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
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],
|
|
)
|
|
|
|
if config.is_encoder_decoder:
|
|
hidden_states = outputs.decoder_hidden_states
|
|
|
|
self.assertIsInstance(hidden_states, (list, tuple))
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
seq_len = getattr(self.model_tester, "seq_length", None)
|
|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[decoder_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_retain_grad_hidden_states_attentions(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
config.output_attentions = True
|
|
|
|
# no need to test all models as different heads yield the same functionality
|
|
model_class = self.all_model_classes[0]
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
outputs = model(**inputs)
|
|
|
|
output = outputs[0]
|
|
|
|
if config.is_encoder_decoder:
|
|
# Seq2Seq models
|
|
encoder_hidden_states = outputs.encoder_hidden_states[0]
|
|
encoder_attentions = outputs.encoder_attentions[0]
|
|
encoder_hidden_states.retain_grad()
|
|
encoder_attentions.retain_grad()
|
|
|
|
decoder_hidden_states = outputs.decoder_hidden_states[0]
|
|
decoder_attentions = outputs.decoder_attentions[0]
|
|
decoder_hidden_states.retain_grad()
|
|
decoder_attentions.retain_grad()
|
|
|
|
cross_attentions = outputs.cross_attentions[0]
|
|
cross_attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(encoder_hidden_states.grad)
|
|
self.assertIsNotNone(encoder_attentions.grad)
|
|
self.assertIsNotNone(decoder_hidden_states.grad)
|
|
self.assertIsNotNone(decoder_attentions.grad)
|
|
self.assertIsNotNone(cross_attentions.grad)
|
|
else:
|
|
# Encoder-/Decoder-only models
|
|
hidden_states = outputs.hidden_states[0]
|
|
attentions = outputs.attentions[0]
|
|
|
|
hidden_states.retain_grad()
|
|
attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(hidden_states.grad)
|
|
self.assertIsNotNone(attentions.grad)
|
|
|
|
def test_feed_forward_chunking(self):
|
|
(
|
|
original_config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
for model_class in self.all_model_classes:
|
|
torch.manual_seed(0)
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
|
|
|
|
torch.manual_seed(0)
|
|
config.chunk_size_feed_forward = 1
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
|
|
self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
|
|
|
|
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)
|
|
|
|
# make sure that decoder_input_ids are resized as well
|
|
if "decoder_input_ids" in inputs_dict:
|
|
inputs_dict["decoder_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_resize_embeddings_untied(self):
|
|
(
|
|
original_config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
original_config.tie_word_embeddings = False
|
|
|
|
# if model cannot untied embeddings -> leave test
|
|
if original_config.tie_word_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config).to(torch_device)
|
|
|
|
# if no output embeddings -> leave test
|
|
if model.get_output_embeddings() is None:
|
|
continue
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_vocab_size = config.vocab_size
|
|
model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 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.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
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 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)
|
|
if "decoder_input_ids" in inputs_dict:
|
|
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
|
# 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))
|
|
|
|
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(), (nn.Embedding, AdaptiveEmbedding))
|
|
model.set_input_embeddings(nn.Embedding(10, 10))
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(x is None or isinstance(x, 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=f"Missing keys for {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_model_outputs_equivalence(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def set_nan_tensor_to_zero(t):
|
|
t[t != t] = 0
|
|
return t
|
|
|
|
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
|
|
with torch.no_grad():
|
|
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
|
|
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
|
|
|
|
def recursive_check(tuple_object, dict_object):
|
|
if isinstance(tuple_object, (List, Tuple)):
|
|
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif isinstance(tuple_object, Dict):
|
|
for tuple_iterable_value, dict_iterable_value in zip(
|
|
tuple_object.values(), dict_object.values()
|
|
):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif tuple_object is None:
|
|
return
|
|
else:
|
|
self.assertTrue(
|
|
torch.allclose(
|
|
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
|
|
),
|
|
msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
|
|
)
|
|
|
|
recursive_check(tuple_output, dict_output)
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
check_equivalence(model, tuple_inputs, dict_inputs)
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(model, tuple_inputs, dict_inputs)
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(
|
|
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
|
|
)
|
|
|
|
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)[0]
|
|
|
|
@require_torch_multi_gpu
|
|
def test_multi_gpu_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", "decoder_head_mask", "cross_attn_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 = nn.DataParallel(model)
|
|
with torch.no_grad():
|
|
_ = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
@require_torch_multi_gpu
|
|
def test_model_parallelization(self):
|
|
if not self.test_model_parallel:
|
|
return
|
|
|
|
# a candidate for testing_utils
|
|
def get_current_gpu_memory_use():
|
|
"""returns a list of cuda memory allocations per GPU in MBs"""
|
|
|
|
per_device_memory = []
|
|
for id in range(torch.cuda.device_count()):
|
|
with torch.cuda.device(id):
|
|
per_device_memory.append(torch.cuda.memory_allocated() >> 20)
|
|
|
|
return per_device_memory
|
|
|
|
# Needs a large model to see the difference.
|
|
config = self.model_tester.get_large_model_config()
|
|
|
|
for model_class in self.all_parallelizable_model_classes:
|
|
torch.cuda.empty_cache()
|
|
|
|
# 1. single gpu memory load + unload + memory measurements
|
|
# Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests)
|
|
memory_at_start = get_current_gpu_memory_use()
|
|
|
|
# Put model on device 0 and take a memory snapshot
|
|
model = model_class(config)
|
|
model.to("cuda:0")
|
|
memory_after_model_load = get_current_gpu_memory_use()
|
|
|
|
# The memory use on device 0 should be higher than it was initially.
|
|
self.assertGreater(memory_after_model_load[0], memory_at_start[0])
|
|
|
|
del model
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
# 2. MP test
|
|
# it's essential to re-calibrate the usage before the next stage
|
|
memory_at_start = get_current_gpu_memory_use()
|
|
|
|
# Spread model layers over multiple devices
|
|
model = model_class(config)
|
|
model.parallelize()
|
|
memory_after_parallelization = get_current_gpu_memory_use()
|
|
|
|
# Assert that the memory use on all devices is higher than it was when loaded only on CPU
|
|
for n in range(torch.cuda.device_count()):
|
|
self.assertGreater(memory_after_parallelization[n], memory_at_start[n])
|
|
|
|
# Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it
|
|
self.assertLess(memory_after_parallelization[0], memory_after_model_load[0])
|
|
|
|
# Assert that the memory use of device 1 is higher than it was when the entire model was loaded
|
|
# on device 0 and device 1 wasn't used at all
|
|
self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1])
|
|
|
|
del model
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
@require_torch_multi_gpu
|
|
def test_model_parallel_equal_results(self):
|
|
if not self.test_model_parallel:
|
|
return
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_parallelizable_model_classes:
|
|
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
def cast_to_device(dictionary, device):
|
|
output = {}
|
|
for k, v in dictionary.items():
|
|
if isinstance(v, torch.Tensor):
|
|
output[k] = v.to(device)
|
|
else:
|
|
output[k] = v
|
|
|
|
return output
|
|
|
|
model = model_class(config)
|
|
output = model(**cast_to_device(inputs_dict, "cpu"))
|
|
|
|
model.parallelize()
|
|
|
|
parallel_output = model(**cast_to_device(inputs_dict, "cuda:0"))
|
|
|
|
for value, parallel_value in zip(output, parallel_output):
|
|
if isinstance(value, torch.Tensor):
|
|
self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7))
|
|
elif isinstance(value, (Tuple, List)):
|
|
for value_, parallel_value_ in zip(value, parallel_value):
|
|
self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7))
|
|
|
|
@require_torch_multi_gpu
|
|
def test_model_parallel_beam_search(self):
|
|
if not self.test_model_parallel:
|
|
return
|
|
|
|
all_generative_and_parallelizable_model_classes = tuple(
|
|
set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes)
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in all_generative_and_parallelizable_model_classes:
|
|
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
model = model_class(config)
|
|
|
|
def cast_to_device(dictionary, device):
|
|
output = {}
|
|
for k, v in dictionary.items():
|
|
if isinstance(v, torch.Tensor):
|
|
output[k] = v.to(device)
|
|
else:
|
|
output[k] = v
|
|
|
|
return output
|
|
|
|
model.parallelize()
|
|
model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
|
|
|
|
def test_problem_types(self):
|
|
if not self.test_sequence_classification_problem_types:
|
|
return
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
problem_types = [
|
|
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
|
|
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
|
|
{"title": "regression", "num_labels": 1, "dtype": torch.float},
|
|
]
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
|
|
continue
|
|
|
|
for problem_type in problem_types:
|
|
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
|
|
|
|
config.problem_type = problem_type["title"]
|
|
config.num_labels = problem_type["num_labels"]
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
if problem_type["num_labels"] > 1:
|
|
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
|
|
|
|
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
|
|
|
|
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
|
|
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
|
|
# they have the same size." which is a symptom something in wrong for the regression problem.
|
|
# See https://github.com/huggingface/transformers/issues/11780
|
|
with warnings.catch_warnings(record=True) as warning_list:
|
|
loss = model(**inputs).loss
|
|
self.assertListEqual(warning_list, [])
|
|
|
|
loss.backward()
|
|
|
|
def test_load_with_mismatched_shapes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
|
|
continue
|
|
|
|
with self.subTest(msg=f"Testing {model_class}"):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = model_class(config)
|
|
model.save_pretrained(tmp_dir)
|
|
|
|
# Fails when we don't set ignore_mismatched_sizes=True
|
|
with self.assertRaises(RuntimeError) as e:
|
|
print(type(e))
|
|
new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
|
|
|
|
logger = logging.get_logger("transformers.modeling_utils")
|
|
with CaptureLogger(logger) as cl:
|
|
new_model = AutoModelForSequenceClassification.from_pretrained(
|
|
tmp_dir, num_labels=42, ignore_mismatched_sizes=True
|
|
)
|
|
self.assertIn("the shapes did not match", cl.out)
|
|
|
|
new_model.to(torch_device)
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
logits = new_model(**inputs).logits
|
|
self.assertEqual(logits.shape[1], 42)
|
|
|
|
|
|
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 random_attention_mask(shape, rng=None, name=None):
|
|
attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None)
|
|
# make sure that at least one token is attended to for each batch
|
|
attn_mask[:, -1] = 1
|
|
return attn_mask
|
|
|
|
|
|
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(TestCasePlus):
|
|
@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)
|
|
|
|
# Not sure this is the intended behavior. TODO fix Lysandre & Thom
|
|
config.name_or_path = model_name
|
|
|
|
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)
|
|
|
|
def test_model_from_pretrained_with_different_pretrained_model_name(self):
|
|
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
|
|
self.assertIsNotNone(model)
|
|
|
|
logger = logging.get_logger("transformers.configuration_utils")
|
|
with CaptureLogger(logger) as cl:
|
|
BertModel.from_pretrained(TINY_T5)
|
|
self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
|
|
|
|
@require_torch
|
|
def test_model_from_config_torch_dtype(self):
|
|
# test that the model can be instantiated with dtype of user's choice - as long as it's a
|
|
# float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
|
|
# model from the config object.
|
|
|
|
config = T5Config.from_pretrained(TINY_T5)
|
|
model = AutoModel.from_config(config)
|
|
# XXX: isn't supported
|
|
# model = T5ForConditionalGeneration.from_config(config)
|
|
self.assertEqual(model.dtype, torch.float32)
|
|
|
|
model = AutoModel.from_config(config, torch_dtype=torch.float16)
|
|
self.assertEqual(model.dtype, torch.float16)
|
|
|
|
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
|
|
with self.assertRaises(ValueError):
|
|
model = AutoModel.from_config(config, torch_dtype=torch.int64)
|
|
|
|
@require_torch
|
|
def test_model_from_pretrained_torch_dtype(self):
|
|
# test that the model can be instantiated with dtype of either
|
|
# 1. config.torch_dtype setting in the saved model (priority)
|
|
# 2. via autodiscovery by looking at model weights
|
|
# so if a model.half() was saved, we want it to be instantiated as such.
|
|
model_path = self.get_auto_remove_tmp_dir()
|
|
|
|
# baseline - we know TINY_T5 is fp32 model
|
|
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
|
|
self.assertEqual(model.dtype, torch.float32)
|
|
|
|
# test the default fp32 save_pretrained => from_pretrained cycle
|
|
model.save_pretrained(model_path)
|
|
model = T5ForConditionalGeneration.from_pretrained(model_path)
|
|
self.assertEqual(model.dtype, torch.float32)
|
|
# test with auto-detection
|
|
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
|
|
self.assertEqual(model.dtype, torch.float32)
|
|
|
|
# test forced loading in fp16 (even though the weights are in fp32)
|
|
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
|
|
self.assertEqual(model.dtype, torch.float16)
|
|
|
|
# test fp16 save_pretrained, loaded with auto-detection
|
|
model = model.half()
|
|
model.save_pretrained(model_path)
|
|
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
|
|
self.assertEqual(model.config.torch_dtype, "float16") # tests `config.torch_dtype` saving
|
|
self.assertEqual(model.dtype, torch.float16)
|
|
|
|
# test fp16 save_pretrained, loaded with the explicit fp16
|
|
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
|
|
self.assertEqual(model.dtype, torch.float16)
|
|
|
|
|
|
@require_torch
|
|
@is_staging_test
|
|
class ModelPushToHubTester(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls._api = HfApi(endpoint=ENDPOINT_STAGING)
|
|
cls._token = cls._api.login(username=USER, password=PASS)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
try:
|
|
cls._api.delete_repo(token=cls._token, name="test-model")
|
|
except HTTPError:
|
|
pass
|
|
|
|
try:
|
|
cls._api.delete_repo(token=cls._token, name="test-model-org", organization="valid_org")
|
|
except HTTPError:
|
|
pass
|
|
|
|
def test_push_to_hub(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = BertModel(config)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(os.path.join(tmp_dir, "test-model"), push_to_hub=True, use_auth_token=self._token)
|
|
|
|
new_model = BertModel.from_pretrained(f"{USER}/test-model")
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.equal(p1, p2))
|
|
|
|
def test_push_to_hub_in_organization(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = BertModel(config)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(
|
|
os.path.join(tmp_dir, "test-model-org"),
|
|
push_to_hub=True,
|
|
use_auth_token=self._token,
|
|
organization="valid_org",
|
|
)
|
|
|
|
new_model = BertModel.from_pretrained("valid_org/test-model-org")
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.equal(p1, p2))
|