3260 lines
144 KiB
Python
Executable File
3260 lines
144 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 json
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import os
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import os.path
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import pickle
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import random
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import sys
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import tempfile
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import unittest
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import unittest.mock as mock
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import warnings
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from pathlib import Path
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from typing import Dict, List, Tuple
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import numpy as np
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import transformers
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from huggingface_hub import HfFolder, delete_repo, set_access_token
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from huggingface_hub.file_download import http_get
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from requests.exceptions import HTTPError
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoModelForSequenceClassification,
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PretrainedConfig,
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is_torch_available,
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logging,
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)
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from transformers.models.auto import get_values
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from transformers.testing_utils import (
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TOKEN,
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USER,
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CaptureLogger,
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TestCasePlus,
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is_flaky,
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is_pt_flax_cross_test,
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is_pt_tf_cross_test,
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is_staging_test,
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require_accelerate,
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require_safetensors,
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require_torch,
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require_torch_gpu,
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require_torch_multi_gpu,
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require_usr_bin_time,
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slow,
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torch_device,
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)
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from transformers.utils import (
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SAFE_WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_NAME,
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WEIGHTS_INDEX_NAME,
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WEIGHTS_NAME,
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is_accelerate_available,
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is_flax_available,
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is_tf_available,
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is_torch_fx_available,
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)
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from transformers.utils.generic import ModelOutput
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sys.path.append(str(Path(__file__).parent.parent / "utils"))
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from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402
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if is_accelerate_available():
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from accelerate.utils import compute_module_sizes
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if is_torch_available():
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import torch
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from torch import nn
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from test_module.custom_modeling import CustomModel, NoSuperInitModel
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from transformers import (
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BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
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MODEL_FOR_AUDIO_XVECTOR_MAPPING,
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MODEL_FOR_BACKBONE_MAPPING,
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MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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MODEL_FOR_MASKED_IMAGE_MODELING_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_SEMANTIC_SEGMENTATION_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_FOR_VIDEO_CLASSIFICATION_MAPPING,
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MODEL_MAPPING,
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AdaptiveEmbedding,
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AutoModelForCausalLM,
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AutoTokenizer,
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BertConfig,
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BertModel,
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PreTrainedModel,
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T5Config,
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T5ForConditionalGeneration,
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)
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from transformers.modeling_utils import shard_checkpoint
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# Fake pretrained models for tests
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class BaseModel(PreTrainedModel):
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config_class = PretrainedConfig
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def __init__(self, config):
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super().__init__(config)
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self.linear = nn.Linear(4, 5)
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self.linear_2 = nn.Linear(5, 6)
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def forward(self, x):
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return self.linear_2(self.linear(x))
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class ModelWithHead(PreTrainedModel):
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base_model_prefix = "base"
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config_class = PretrainedConfig
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def _init_weights(self, module):
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pass
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def __init__(self, config):
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super().__init__(config)
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self.base = BaseModel(config)
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# linear is a common name between Base and Head on purpose.
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self.linear = nn.Linear(6, 3)
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self.linear2 = nn.Linear(3, 5)
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def forward(self, x):
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return self.linear2(self.linear(self.base(x)))
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if is_tf_available():
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import tensorflow as tf
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if is_flax_available():
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import jax.numpy as jnp
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from transformers.modeling_flax_pytorch_utils import (
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convert_pytorch_state_dict_to_flax,
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load_flax_weights_in_pytorch_model,
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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 or "layer_scale" 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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TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
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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_compatible = False
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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_resize_position_embeddings = False
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test_head_masking = True
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test_mismatched_shapes = 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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has_attentions = True
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model_split_percents = [0.5, 0.7, 0.9]
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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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elif model_class in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING):
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inputs_dict.pop("attention_mask")
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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 [
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*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING),
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*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING),
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]:
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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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*get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING),
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*get_values(MODEL_FOR_AUDIO_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_CAUSAL_IMAGE_MODELING_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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elif model_class in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING):
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num_patches = self.model_tester.image_size // self.model_tester.patch_size
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inputs_dict["bool_masked_pos"] = torch.zeros(
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(self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device
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)
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elif model_class in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING):
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batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
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inputs_dict["labels"] = torch.zeros(
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[self.model_tester.batch_size, height, width], device=torch_device
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).long()
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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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def check_save_load(out1, out2):
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# make sure we don't have nans
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out_2 = out2.cpu().numpy()
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out_2[np.isnan(out_2)] = 0
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out_1 = out1.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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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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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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second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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if isinstance(first, tuple) and isinstance(second, tuple):
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for tensor1, tensor2 in zip(first, second):
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check_save_load(tensor1, tensor2)
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else:
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check_save_load(first, second)
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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 test_gradient_checkpointing_backward_compatibility(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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if not model_class.supports_gradient_checkpointing:
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continue
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config.gradient_checkpointing = True
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model = model_class(config)
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self.assertTrue(model.is_gradient_checkpointing)
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def test_gradient_checkpointing_enable_disable(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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if not model_class.supports_gradient_checkpointing:
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continue
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# at init model should have gradient checkpointing disabled
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model = model_class(config)
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self.assertFalse(model.is_gradient_checkpointing)
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# check enable works
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model.gradient_checkpointing_enable()
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self.assertTrue(model.is_gradient_checkpointing)
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# check disable works
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model.gradient_checkpointing_disable()
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self.assertFalse(model.is_gradient_checkpointing)
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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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@is_flaky()
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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 = torch.max(
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torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key])
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).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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def check_determinism(first, second):
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
|
|
out_1 = out_1[~np.isnan(out_1)]
|
|
out_2 = out_2[~np.isnan(out_2)]
|
|
max_diff = np.amax(np.abs(out_1 - out_2))
|
|
self.assertLessEqual(max_diff, 1e-5)
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
|
|
second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
|
|
|
|
if isinstance(first, tuple) and isinstance(second, tuple):
|
|
for tensor1, tensor2 in zip(first, second):
|
|
check_determinism(tensor1, tensor2)
|
|
else:
|
|
check_determinism(first, second)
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
|
arg_names = [*signature.parameters.keys()]
|
|
|
|
if model.config.is_encoder_decoder:
|
|
expected_arg_names = [
|
|
"input_ids",
|
|
"attention_mask",
|
|
"decoder_input_ids",
|
|
"decoder_attention_mask",
|
|
]
|
|
expected_arg_names.extend(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["encoder_outputs"]
|
|
)
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
else:
|
|
expected_arg_names = ["input_ids"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
def test_training(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
if model_class in [
|
|
*get_values(MODEL_MAPPING),
|
|
*get_values(MODEL_FOR_BACKBONE_MAPPING),
|
|
]:
|
|
continue
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
def test_training_gradient_checkpointing(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.use_cache = False
|
|
config.return_dict = True
|
|
|
|
if (
|
|
model_class in [*get_values(MODEL_MAPPING), *get_values(MODEL_FOR_BACKBONE_MAPPING)]
|
|
or not model_class.supports_gradient_checkpointing
|
|
):
|
|
continue
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.gradient_checkpointing_enable()
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
seq_len = getattr(self.model_tester, "seq_length", None)
|
|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
|
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
|
|
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
|
|
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
|
chunk_length = getattr(self.model_tester, "chunk_length", None)
|
|
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
|
|
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = False
|
|
config.return_dict = 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))
|
|
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
# check that output_attentions also work using config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = 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))
|
|
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
if chunk_length is not None:
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-4:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
|
|
)
|
|
else:
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
out_len = len(outputs)
|
|
|
|
if self.is_encoder_decoder:
|
|
correct_outlen = 5
|
|
|
|
# loss is at first position
|
|
if "labels" in inputs_dict:
|
|
correct_outlen += 1 # loss is added to beginning
|
|
# Question Answering model returns start_logits and end_logits
|
|
if model_class in [
|
|
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING),
|
|
*get_values(MODEL_FOR_DOCUMENT_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_simple(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)
|
|
|
|
# This is copied from `torch/testing/_internal/jit_utils.py::clear_class_registry`
|
|
def clear_torch_jit_class_registry(self):
|
|
|
|
torch._C._jit_clear_class_registry()
|
|
torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
|
|
torch.jit._state._clear_class_state()
|
|
|
|
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)
|
|
|
|
main_input_name = model_class.main_input_name
|
|
|
|
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
|
|
main_input = inputs[main_input_name]
|
|
attention_mask = inputs["attention_mask"]
|
|
decoder_input_ids = inputs["decoder_input_ids"]
|
|
decoder_attention_mask = inputs["decoder_attention_mask"]
|
|
model(main_input, attention_mask, decoder_input_ids, decoder_attention_mask)
|
|
traced_model = torch.jit.trace(
|
|
model, (main_input, attention_mask, decoder_input_ids, decoder_attention_mask)
|
|
)
|
|
elif "bbox" in inputs and "image" in inputs: # LayoutLMv2 requires additional inputs
|
|
input_ids = inputs["input_ids"]
|
|
bbox = inputs["bbox"]
|
|
image = inputs["image"].tensor
|
|
model(input_ids, bbox, image)
|
|
traced_model = torch.jit.trace(
|
|
model, (input_ids, bbox, image), check_trace=False
|
|
) # when traced model is checked, an error is produced due to name mangling
|
|
else:
|
|
main_input = inputs[main_input_name]
|
|
model(main_input)
|
|
traced_model = torch.jit.trace(model, main_input)
|
|
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)
|
|
|
|
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
|
|
# (Even with this call, there are still memory leak by ~0.04MB)
|
|
self.clear_torch_jit_class_registry()
|
|
|
|
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() or not self.fx_compatible:
|
|
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.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, 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
|
|
labels = inputs.get("labels", None)
|
|
input_names = [
|
|
"attention_mask",
|
|
"decoder_attention_mask",
|
|
"decoder_input_ids",
|
|
"input_features",
|
|
"input_ids",
|
|
"input_values",
|
|
]
|
|
if labels is not None:
|
|
input_names.append("labels")
|
|
|
|
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
|
input_names = list(filtered_inputs.keys())
|
|
|
|
model_output = model(**filtered_inputs)
|
|
|
|
traced_model = symbolic_trace(model, input_names)
|
|
traced_output = traced_model(**filtered_inputs)
|
|
else:
|
|
input_names = [
|
|
"attention_mask",
|
|
"bbox",
|
|
"input_features",
|
|
"input_ids",
|
|
"input_values",
|
|
"pixel_values",
|
|
"token_type_ids",
|
|
"visual_feats",
|
|
"visual_pos",
|
|
]
|
|
|
|
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 = list(filtered_inputs.keys())
|
|
|
|
if isinstance(model, tuple(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values())) and (
|
|
not hasattr(model.config, "problem_type") or model.config.problem_type is None
|
|
):
|
|
model.config.problem_type = "single_label_classification"
|
|
|
|
traced_model = symbolic_trace(model, input_names)
|
|
traced_output = traced_model(**filtered_inputs)
|
|
model_output = model(**filtered_inputs)
|
|
|
|
except Exception as e:
|
|
self.fail(f"Couldn't trace module: {e}")
|
|
|
|
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}",
|
|
)
|
|
|
|
# Test that the model can be serialized and restored properly
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
|
|
try:
|
|
with open(pkl_file_name, "wb") as f:
|
|
pickle.dump(traced_model, f)
|
|
with open(pkl_file_name, "rb") as f:
|
|
loaded = pickle.load(f)
|
|
except Exception as e:
|
|
self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
|
|
|
|
loaded_output = loaded(**filtered_inputs)
|
|
loaded_output = flatten_output(loaded_output)
|
|
|
|
for i in range(num_outputs):
|
|
self.assertTrue(
|
|
torch.allclose(model_output[i], loaded_output[i]),
|
|
f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
|
|
)
|
|
|
|
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
|
|
# (Even with this call, there are still memory leak by ~0.04MB)
|
|
self.clear_torch_jit_class_registry()
|
|
|
|
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 = self.has_attentions
|
|
|
|
# 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_hidden_states.retain_grad()
|
|
|
|
decoder_hidden_states = outputs.decoder_hidden_states[0]
|
|
decoder_hidden_states.retain_grad()
|
|
|
|
if self.has_attentions:
|
|
encoder_attentions = outputs.encoder_attentions[0]
|
|
encoder_attentions.retain_grad()
|
|
|
|
decoder_attentions = outputs.decoder_attentions[0]
|
|
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(decoder_hidden_states.grad)
|
|
|
|
if self.has_attentions:
|
|
self.assertIsNotNone(encoder_attentions.grad)
|
|
self.assertIsNotNone(decoder_attentions.grad)
|
|
self.assertIsNotNone(cross_attentions.grad)
|
|
else:
|
|
# Encoder-/Decoder-only models
|
|
hidden_states = outputs.hidden_states[0]
|
|
hidden_states.retain_grad()
|
|
|
|
if self.has_attentions:
|
|
attentions = outputs.attentions[0]
|
|
attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(hidden_states.grad)
|
|
|
|
if self.has_attentions:
|
|
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_position_vector_embeddings(self):
|
|
if not self.test_resize_position_embeddings:
|
|
return
|
|
|
|
(
|
|
original_config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
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()
|
|
|
|
max_position_embeddings = config.max_position_embeddings
|
|
|
|
# Retrieve the embeddings and clone theme
|
|
if model.config.is_encoder_decoder:
|
|
encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
|
|
encoder_cloned_embeddings = encoder_model_embed.weight.clone()
|
|
decoder_cloned_embeddings = decoder_model_embed.weight.clone()
|
|
else:
|
|
model_embed = model.get_position_embeddings()
|
|
cloned_embeddings = model_embed.weight.clone()
|
|
|
|
# Check that resizing the position embeddings with a larger max_position_embeddings increases
|
|
# the model's postion embeddings size
|
|
model.resize_position_embeddings(max_position_embeddings + 10)
|
|
self.assertEqual(model.config.max_position_embeddings, max_position_embeddings + 10)
|
|
|
|
# Check that it actually resizes the embeddings matrix
|
|
if model.config.is_encoder_decoder:
|
|
encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
|
|
self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] + 10)
|
|
self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] + 10)
|
|
else:
|
|
model_embed = model.get_position_embeddings()
|
|
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 position embeddings with a smaller max_position_embeddings decreases
|
|
# the model's max_position_embeddings
|
|
model.resize_position_embeddings(max_position_embeddings - 5)
|
|
self.assertEqual(model.config.max_position_embeddings, max_position_embeddings - 5)
|
|
|
|
# Check that it actually resizes the embeddings matrix
|
|
if model.config.is_encoder_decoder:
|
|
encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
|
|
self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] - 5)
|
|
self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] - 5)
|
|
else:
|
|
model_embed = model.get_position_embeddings()
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 5)
|
|
|
|
# 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 adding and removing tokens has not modified the first part of the embedding matrix.
|
|
models_equal = True
|
|
|
|
if model.config.is_encoder_decoder:
|
|
for p1, p2 in zip(encoder_cloned_embeddings, encoder_model_embed.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
for p1, p2 in zip(decoder_cloned_embeddings, decoder_model_embed.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
else:
|
|
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_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_model_main_input_name(self):
|
|
for model_class in self.all_model_classes:
|
|
model_signature = inspect.signature(getattr(model_class, "forward"))
|
|
# The main input is the name of the argument after `self`
|
|
observed_main_input_name = list(model_signature.parameters.keys())[1]
|
|
self.assertEqual(model_class.main_input_name, observed_main_input_name)
|
|
|
|
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):
|
|
|
|
extra_params = {k: v for k, v in model.named_parameters() if not k.startswith(base_model_prefix)}
|
|
extra_params.update({k: v for k, v in model.named_buffers() if not k.startswith(base_model_prefix)})
|
|
# Some models define this as None
|
|
if model._keys_to_ignore_on_load_missing:
|
|
for key in model._keys_to_ignore_on_load_missing:
|
|
extra_params.pop(key, None)
|
|
|
|
if not extra_params:
|
|
# In that case, we *are* on a head model, but every
|
|
# single key is not actual parameters and this is
|
|
# tested in `test_tied_model_weights_key_ignore` test.
|
|
continue
|
|
|
|
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)
|
|
self.assertGreater(len(loading_info["missing_keys"]), 0, model.__class__.__name__)
|
|
|
|
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_tied_model_weights_key_ignore(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
for model_class in self.all_model_classes:
|
|
model_tied = model_class(config)
|
|
with tempfile.TemporaryDirectory() as d:
|
|
model_tied.save_pretrained(d)
|
|
|
|
# We are nuking ALL weights on file, so every parameter should
|
|
# yell on load. We're going to detect if we yell too much, or too little.
|
|
with open(os.path.join(d, "pytorch_model.bin"), "wb") as f:
|
|
torch.save({}, f)
|
|
model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True)
|
|
|
|
# ! Actually we could use `state_dict()` and check iteratively the tensors which are the same (for instance using `tensor.data_ptr()`). to detect the duplicates.
|
|
# ```python
|
|
# model = GPT2LMHeadModel.from_pretrained("gpt2")
|
|
# "lm_head.weight" in model.state_dict().keys() # True
|
|
# "lm_head.weight" in model.named_parameters() # False
|
|
# In [6]: model.lm_head.weight.data_ptr()
|
|
# Out[6]: 139901378371648
|
|
# In [9]: model.transformer.wte.weight.data_ptr()
|
|
# Out[9]: 139901378371648 # Same PTR, it's the same DATA ! we would need to check for stride too to be 100% accurate.
|
|
# ```
|
|
|
|
prefix = f"{model_reloaded.base_model_prefix}."
|
|
params = dict(model_reloaded.named_parameters())
|
|
params.update(dict(model_reloaded.named_buffers()))
|
|
# param_names = set(k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys())
|
|
param_names = set(k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys())
|
|
|
|
missing_keys = set(infos["missing_keys"])
|
|
|
|
extra_missing = missing_keys - param_names
|
|
# missed_missing = param_names - missing_keys
|
|
|
|
self.assertEqual(
|
|
extra_missing,
|
|
set(),
|
|
f"This model {model_class.__name__} might be missing some `keys_to_ignore`: {extra_missing}",
|
|
)
|
|
|
|
# self.assertEqual(
|
|
# missed_missing,
|
|
# set(),
|
|
# f"This model {model_class.__name__} ignores keys {missed_missing} but they look like real"
|
|
# " parameters",
|
|
# )
|
|
|
|
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=(
|
|
"Tuple and dict output are not equal. Difference:"
|
|
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
|
|
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
|
|
f" `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, 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})
|
|
|
|
if self.has_attentions:
|
|
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_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}
|
|
)
|
|
|
|
# Don't copy this method to model specific test file!
|
|
# TODO: remove this method once the issues are all fixed!
|
|
def _make_attention_mask_non_null(self, inputs_dict):
|
|
"""Make sure no sequence has all zeros as attention mask"""
|
|
|
|
for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]:
|
|
if k in inputs_dict:
|
|
attention_mask = inputs_dict[k]
|
|
|
|
# Make sure no all 0s attention masks - to avoid failure at this moment.
|
|
# Put `1` at the beginning of sequences to make it still work when combining causal attention masks.
|
|
# TODO: remove this line once a fix regarding large negative values for attention mask is done.
|
|
attention_mask = torch.cat(
|
|
[torch.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], dim=-1
|
|
)
|
|
|
|
# Here we make the first sequence with all 0s as attention mask.
|
|
# Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative
|
|
# values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks.
|
|
# TODO: enable this block once the large negative values thing is cleaned up.
|
|
# (see https://github.com/huggingface/transformers/issues/14859)
|
|
# attention_mask = torch.cat(
|
|
# [torch.zeros_like(attention_mask[:1], dtype=attention_mask.dtype), attention_mask[1:]],
|
|
# dim=0
|
|
# )
|
|
|
|
inputs_dict[k] = attention_mask
|
|
|
|
# Don't copy this method to model specific test file!
|
|
# TODO: remove this method once the issues are all fixed!
|
|
def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class):
|
|
"""For temporarily ignoring some failed test cases (issues to be fixed)"""
|
|
|
|
tf_keys = set([k for k, v in tf_outputs.items() if v is not None])
|
|
pt_keys = set([k for k, v in pt_outputs.items() if v is not None])
|
|
|
|
key_differences = tf_keys.symmetric_difference(pt_keys)
|
|
|
|
if model_class.__name__ in [
|
|
"FlaubertWithLMHeadModel",
|
|
"FunnelForPreTraining",
|
|
"ElectraForPreTraining",
|
|
"XLMWithLMHeadModel",
|
|
"TransfoXLLMHeadModel",
|
|
]:
|
|
for k in key_differences:
|
|
if k in ["loss", "losses"]:
|
|
tf_keys.discard(k)
|
|
pt_keys.discard(k)
|
|
elif model_class.__name__.startswith("GPT2"):
|
|
# `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple.
|
|
tf_keys.discard("past_key_values")
|
|
pt_keys.discard("past_key_values")
|
|
|
|
# create new outputs from the remaining fields
|
|
new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys})
|
|
new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys})
|
|
|
|
return new_tf_outputs, new_pt_outputs
|
|
|
|
# Copied from tests.test_modeling_tf_common.TFModelTesterMixin.check_pt_tf_outputs
|
|
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
|
|
"""Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way.
|
|
|
|
Args:
|
|
model_class: The class of the model that is currently testing. For example, `TFBertModel`,
|
|
TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative
|
|
error messages.
|
|
name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc.
|
|
attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element
|
|
being a named field in the output.
|
|
"""
|
|
|
|
self.assertEqual(type(name), str)
|
|
if attributes is not None:
|
|
self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")
|
|
|
|
# Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
|
|
if isinstance(tf_outputs, ModelOutput):
|
|
self.assertTrue(
|
|
isinstance(pt_outputs, ModelOutput),
|
|
f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is",
|
|
)
|
|
|
|
# Don't copy this block to model specific test file!
|
|
# TODO: remove this method and this line after issues are fixed
|
|
tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class)
|
|
|
|
tf_keys = [k for k, v in tf_outputs.items() if v is not None]
|
|
pt_keys = [k for k, v in pt_outputs.items() if v is not None]
|
|
|
|
self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch")
|
|
|
|
# convert to the case of `tuple`
|
|
# appending each key to the current (string) `name`
|
|
attributes = tuple([f"{name}.{k}" for k in tf_keys])
|
|
self.check_pt_tf_outputs(
|
|
tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
|
|
)
|
|
|
|
# Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
|
|
elif type(tf_outputs) in [tuple, list]:
|
|
self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch")
|
|
self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch")
|
|
|
|
if attributes is not None:
|
|
# case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
|
|
self.assertEqual(
|
|
len(attributes),
|
|
len(tf_outputs),
|
|
f"{name}: The tuple `attributes` should have the same length as `tf_outputs`",
|
|
)
|
|
else:
|
|
# case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name`
|
|
attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))])
|
|
|
|
for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes):
|
|
self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr)
|
|
|
|
elif isinstance(tf_outputs, tf.Tensor):
|
|
self.assertTrue(
|
|
isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is"
|
|
)
|
|
|
|
tf_outputs = tf_outputs.numpy()
|
|
pt_outputs = pt_outputs.detach().to("cpu").numpy()
|
|
|
|
self.assertEqual(
|
|
tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch"
|
|
)
|
|
|
|
# deal with NumPy's scalars to make replacing nan values by 0 work.
|
|
if np.isscalar(tf_outputs):
|
|
tf_outputs = np.array([tf_outputs])
|
|
pt_outputs = np.array([pt_outputs])
|
|
|
|
tf_nans = np.isnan(tf_outputs)
|
|
pt_nans = np.isnan(pt_outputs)
|
|
|
|
pt_outputs[tf_nans] = 0
|
|
tf_outputs[tf_nans] = 0
|
|
pt_outputs[pt_nans] = 0
|
|
tf_outputs[pt_nans] = 0
|
|
|
|
max_diff = np.amax(np.abs(tf_outputs - pt_outputs))
|
|
self.assertLessEqual(max_diff, tol, f"{name}: Difference between PyTorch and TF is {max_diff} (>= {tol}).")
|
|
else:
|
|
raise ValueError(
|
|
"`tf_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `tf.Tensor`. Got"
|
|
f" {type(tf_outputs)} instead."
|
|
)
|
|
|
|
def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict):
|
|
|
|
tf_inputs_dict = {}
|
|
for key, tensor in pt_inputs_dict.items():
|
|
# skip key that does not exist in tf
|
|
if type(tensor) == bool:
|
|
tf_inputs_dict[key] = tensor
|
|
elif key == "input_values":
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
|
|
elif key == "pixel_values":
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
|
|
elif key == "input_features":
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
|
|
# other general float inputs
|
|
elif tensor.is_floating_point():
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
|
|
else:
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
|
|
|
|
return tf_inputs_dict
|
|
|
|
def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict):
|
|
|
|
tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict)
|
|
|
|
# send pytorch inputs to the correct device
|
|
pt_inputs_dict = {
|
|
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items()
|
|
}
|
|
|
|
# send pytorch model to the correct device
|
|
pt_model.to(torch_device)
|
|
|
|
# Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences
|
|
pt_model.eval()
|
|
|
|
with torch.no_grad():
|
|
pt_outputs = pt_model(**pt_inputs_dict)
|
|
tf_outputs = tf_model(tf_inputs_dict)
|
|
|
|
# tf models returned loss is usually a tensor rather than a scalar.
|
|
# (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`)
|
|
# Change it here to a scalar to match PyTorch models' loss
|
|
tf_loss = getattr(tf_outputs, "loss", None)
|
|
if tf_loss is not None:
|
|
tf_outputs.loss = tf.math.reduce_mean(tf_loss)
|
|
|
|
self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(pt_model))
|
|
|
|
@is_pt_tf_cross_test
|
|
def test_pt_tf_model_equivalence(self):
|
|
import transformers
|
|
|
|
for model_class in self.all_model_classes:
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
tf_model_class_name = "TF" + model_class.__name__ # Add the "TF" at the beginning
|
|
if not hasattr(transformers, tf_model_class_name):
|
|
# transformers does not have this model in TF version yet
|
|
return
|
|
|
|
# Output all for aggressive testing
|
|
config.output_hidden_states = True
|
|
config.output_attentions = self.has_attentions
|
|
|
|
# Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
|
|
# of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
|
|
# TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
|
|
self._make_attention_mask_non_null(inputs_dict)
|
|
|
|
tf_model_class = getattr(transformers, tf_model_class_name)
|
|
|
|
pt_model = model_class(config)
|
|
tf_model = tf_model_class(config)
|
|
|
|
pt_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
pt_inputs_dict_with_labels = self._prepare_for_class(
|
|
inputs_dict,
|
|
model_class,
|
|
# Not all models accept "labels" in the forward pass (yet :) )
|
|
return_labels=True if "labels" in inspect.signature(model_class.forward).parameters.keys() else False,
|
|
)
|
|
|
|
# make sure only tf inputs are forward that actually exist in function args
|
|
tf_input_keys = set(inspect.signature(tf_model.call).parameters.keys())
|
|
|
|
# remove all head masks
|
|
tf_input_keys.discard("head_mask")
|
|
tf_input_keys.discard("cross_attn_head_mask")
|
|
tf_input_keys.discard("decoder_head_mask")
|
|
|
|
pt_inputs_dict = {k: v for k, v in pt_inputs_dict.items() if k in tf_input_keys}
|
|
pt_inputs_dict_with_labels = {k: v for k, v in pt_inputs_dict_with_labels.items() if k in tf_input_keys}
|
|
|
|
# For some models (e.g. base models), there is no label returned.
|
|
# Set the input dict to `None` to avoid check outputs twice for the same input dicts.
|
|
if set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()):
|
|
pt_inputs_dict_with_labels = None
|
|
|
|
# Check we can load pt model in tf and vice-versa with model => model functions
|
|
# Here requires `tf_inputs_dict` to build `tf_model`
|
|
tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict)
|
|
tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
|
|
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
|
|
|
|
# Original test: check without `labels`
|
|
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict)
|
|
# check with `labels`
|
|
if pt_inputs_dict_with_labels:
|
|
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels)
|
|
|
|
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
|
|
torch.save(pt_model.state_dict(), pt_checkpoint_path)
|
|
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
|
|
|
|
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
|
|
tf_model.save_weights(tf_checkpoint_path)
|
|
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
|
|
|
|
# Original test: check without `labels`
|
|
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict)
|
|
# check with `labels`
|
|
if pt_inputs_dict_with_labels:
|
|
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels)
|
|
|
|
def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
|
|
diff = np.abs((a - b)).max()
|
|
self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
|
|
|
|
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
|
|
"""
|
|
Args:
|
|
model_class: The class of the model that is currently testing. For example, ..., etc.
|
|
Currently unused, but it could make debugging easier and faster.
|
|
|
|
names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs.
|
|
Currently unused, but in the future, we could use this information to make the error message clearer
|
|
by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax.
|
|
"""
|
|
|
|
self.assertEqual(type(name), str)
|
|
if attributes is not None:
|
|
self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")
|
|
|
|
# Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
|
|
if isinstance(fx_outputs, ModelOutput):
|
|
self.assertTrue(
|
|
isinstance(pt_outputs, ModelOutput),
|
|
f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is",
|
|
)
|
|
|
|
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
|
|
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
|
|
|
|
self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch")
|
|
|
|
# convert to the case of `tuple`
|
|
# appending each key to the current (string) `name`
|
|
attributes = tuple([f"{name}.{k}" for k in fx_keys])
|
|
self.check_pt_flax_outputs(
|
|
fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
|
|
)
|
|
|
|
# Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
|
|
elif type(fx_outputs) in [tuple, list]:
|
|
self.assertEqual(
|
|
type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch"
|
|
)
|
|
self.assertEqual(
|
|
len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch"
|
|
)
|
|
|
|
if attributes is not None:
|
|
# case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
|
|
self.assertEqual(
|
|
len(attributes),
|
|
len(fx_outputs),
|
|
f"{name}: The tuple `attributes` should have the same length as `fx_outputs`",
|
|
)
|
|
else:
|
|
# case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name`
|
|
attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))])
|
|
|
|
for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes):
|
|
self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr)
|
|
|
|
elif isinstance(fx_outputs, jnp.ndarray):
|
|
self.assertTrue(
|
|
isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is"
|
|
)
|
|
|
|
# Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`.
|
|
fx_outputs = np.array(fx_outputs)
|
|
pt_outputs = pt_outputs.detach().to("cpu").numpy()
|
|
|
|
self.assertEqual(
|
|
fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch"
|
|
)
|
|
|
|
# deal with NumPy's scalars to make replacing nan values by 0 work.
|
|
if np.isscalar(fx_outputs):
|
|
fx_outputs = np.array([fx_outputs])
|
|
pt_outputs = np.array([pt_outputs])
|
|
|
|
fx_nans = np.isnan(fx_outputs)
|
|
pt_nans = np.isnan(pt_outputs)
|
|
|
|
pt_outputs[fx_nans] = 0
|
|
fx_outputs[fx_nans] = 0
|
|
pt_outputs[pt_nans] = 0
|
|
fx_outputs[pt_nans] = 0
|
|
|
|
max_diff = np.amax(np.abs(fx_outputs - pt_outputs))
|
|
self.assertLessEqual(
|
|
max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})."
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got"
|
|
f" {type(fx_outputs)} instead."
|
|
)
|
|
|
|
@is_pt_flax_cross_test
|
|
def test_equivalence_pt_to_flax(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
with self.subTest(model_class.__name__):
|
|
fx_model_class_name = "Flax" + model_class.__name__
|
|
|
|
if not hasattr(transformers, fx_model_class_name):
|
|
# no flax model exists for this class
|
|
return
|
|
|
|
# Output all for aggressive testing
|
|
config.output_hidden_states = True
|
|
config.output_attentions = self.has_attentions
|
|
|
|
fx_model_class = getattr(transformers, fx_model_class_name)
|
|
|
|
# load PyTorch class
|
|
pt_model = model_class(config).eval()
|
|
# Flax models don't use the `use_cache` option and cache is not returned as a default.
|
|
# So we disable `use_cache` here for PyTorch model.
|
|
pt_model.config.use_cache = False
|
|
|
|
# load Flax class
|
|
fx_model = fx_model_class(config, dtype=jnp.float32)
|
|
|
|
# make sure only flax inputs are forward that actually exist in function args
|
|
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
|
|
|
|
# prepare inputs
|
|
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
# remove function args that don't exist in Flax
|
|
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
|
|
|
|
# send pytorch inputs to the correct device
|
|
pt_inputs = {
|
|
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
|
|
}
|
|
|
|
# convert inputs to Flax
|
|
fx_inputs = {k: np.array(v) for k, v in pt_inputs.items() if torch.is_tensor(v)}
|
|
|
|
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
|
|
fx_model.params = fx_state
|
|
|
|
# send pytorch model to the correct device
|
|
pt_model.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
pt_outputs = pt_model(**pt_inputs)
|
|
fx_outputs = fx_model(**fx_inputs)
|
|
|
|
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
|
|
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
|
|
|
|
self.assertEqual(fx_keys, pt_keys)
|
|
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pt_model.save_pretrained(tmpdirname)
|
|
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True)
|
|
|
|
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
|
|
|
|
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
|
|
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
|
|
|
|
self.assertEqual(fx_keys, pt_keys)
|
|
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
|
|
|
|
@is_pt_flax_cross_test
|
|
def test_equivalence_flax_to_pt(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
with self.subTest(model_class.__name__):
|
|
fx_model_class_name = "Flax" + model_class.__name__
|
|
|
|
if not hasattr(transformers, fx_model_class_name):
|
|
# no flax model exists for this class
|
|
return
|
|
|
|
# Output all for aggressive testing
|
|
config.output_hidden_states = True
|
|
config.output_attentions = self.has_attentions
|
|
|
|
fx_model_class = getattr(transformers, fx_model_class_name)
|
|
|
|
# load PyTorch class
|
|
pt_model = model_class(config).eval()
|
|
# Flax models don't use the `use_cache` option and cache is not returned as a default.
|
|
# So we disable `use_cache` here for PyTorch model.
|
|
pt_model.config.use_cache = False
|
|
|
|
# load Flax class
|
|
fx_model = fx_model_class(config, dtype=jnp.float32)
|
|
|
|
# make sure only flax inputs are forward that actually exist in function args
|
|
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
|
|
|
|
# prepare inputs
|
|
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
# remove function args that don't exist in Flax
|
|
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
|
|
|
|
# send pytorch inputs to the correct device
|
|
pt_inputs = {
|
|
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
|
|
}
|
|
|
|
# convert inputs to Flax
|
|
fx_inputs = {k: np.array(v) for k, v in pt_inputs.items() if torch.is_tensor(v)}
|
|
|
|
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
|
|
|
|
# make sure weights are tied in PyTorch
|
|
pt_model.tie_weights()
|
|
|
|
# send pytorch model to the correct device
|
|
pt_model.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
pt_outputs = pt_model(**pt_inputs)
|
|
fx_outputs = fx_model(**fx_inputs)
|
|
|
|
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
|
|
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
|
|
|
|
self.assertEqual(fx_keys, pt_keys)
|
|
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
fx_model.save_pretrained(tmpdirname)
|
|
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
|
|
|
|
# send pytorch model to the correct device
|
|
pt_model_loaded.to(torch_device)
|
|
pt_model_loaded.eval()
|
|
|
|
with torch.no_grad():
|
|
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
|
|
|
|
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
|
|
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
|
|
|
|
self.assertEqual(fx_keys, pt_keys)
|
|
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
|
|
|
|
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(len(model.device_map.keys())):
|
|
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 check_device_map_is_respected(self, model, device_map):
|
|
for param_name, param in model.named_parameters():
|
|
# Find device in device_map
|
|
while len(param_name) > 0 and param_name not in device_map:
|
|
param_name = ".".join(param_name.split(".")[:-1])
|
|
if param_name not in device_map:
|
|
raise ValueError("device map is incomplete, it does not contain any device for `param_name`.")
|
|
|
|
param_device = device_map[param_name]
|
|
if param_device in ["cpu", "disk"]:
|
|
self.assertEqual(param.device, torch.device("meta"))
|
|
else:
|
|
self.assertEqual(param.device, torch.device(param_device))
|
|
|
|
@require_accelerate
|
|
@require_torch_gpu
|
|
def test_disk_offload(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class._no_split_modules is None:
|
|
continue
|
|
|
|
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
|
|
model = model_class(config).eval()
|
|
model = model.to(torch_device)
|
|
torch.manual_seed(0)
|
|
base_output = model(**inputs_dict_class)
|
|
|
|
model_size = compute_module_sizes(model)[""]
|
|
max_size = int(self.model_split_percents[0] * model_size)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
max_memory = {0: max_size, "cpu": max_size}
|
|
with self.assertRaises(ValueError):
|
|
# This errors out cause it's missing an offload folder
|
|
new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
|
|
|
|
new_model = model_class.from_pretrained(
|
|
tmp_dir, device_map="auto", max_memory=max_memory, offload_folder=tmp_dir
|
|
)
|
|
|
|
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
|
|
torch.manual_seed(0)
|
|
new_output = new_model(**inputs_dict_class)
|
|
|
|
self.assertTrue(torch.allclose(base_output[0], new_output[0]))
|
|
|
|
@require_accelerate
|
|
@require_torch_gpu
|
|
def test_cpu_offload(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class._no_split_modules is None:
|
|
continue
|
|
|
|
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
|
|
model = model_class(config).eval()
|
|
model = model.to(torch_device)
|
|
|
|
torch.manual_seed(0)
|
|
base_output = model(**inputs_dict_class)
|
|
|
|
model_size = compute_module_sizes(model)[""]
|
|
# We test several splits of sizes to make sure it works.
|
|
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents]
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
for max_size in max_gpu_sizes:
|
|
max_memory = {0: max_size, "cpu": model_size * 2}
|
|
new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
|
|
# Making sure part of the model will actually end up offloaded
|
|
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"})
|
|
|
|
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
|
|
|
|
torch.manual_seed(0)
|
|
new_output = new_model(**inputs_dict_class)
|
|
|
|
self.assertTrue(torch.allclose(base_output[0], new_output[0]))
|
|
|
|
@require_accelerate
|
|
@require_torch_multi_gpu
|
|
def test_model_parallelism(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class._no_split_modules is None:
|
|
continue
|
|
|
|
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
|
|
model = model_class(config).eval()
|
|
model = model.to(torch_device)
|
|
|
|
torch.manual_seed(0)
|
|
base_output = model(**inputs_dict_class)
|
|
|
|
model_size = compute_module_sizes(model)[""]
|
|
# We test several splits of sizes to make sure it works.
|
|
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents]
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
for max_size in max_gpu_sizes:
|
|
max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2}
|
|
new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
|
|
# Making sure part of the model will actually end up offloaded
|
|
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1})
|
|
|
|
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
|
|
|
|
torch.manual_seed(0)
|
|
new_output = new_model(**inputs_dict_class)
|
|
|
|
self.assertTrue(torch.allclose(base_output[0], new_output[0]))
|
|
|
|
def test_problem_types(self):
|
|
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),
|
|
*get_values(MODEL_FOR_IMAGE_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
|
|
for w in warning_list:
|
|
if "Using a target size that is different to the input size" in str(w.message):
|
|
raise ValueError(
|
|
f"Something is going wrong in the regression problem: intercepted {w.message}"
|
|
)
|
|
|
|
loss.backward()
|
|
|
|
def test_load_with_mismatched_shapes(self):
|
|
if not self.test_mismatched_shapes:
|
|
return
|
|
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):
|
|
new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
|
|
with self.assertRaises(RuntimeError):
|
|
new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10)
|
|
|
|
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)
|
|
|
|
with CaptureLogger(logger) as cl:
|
|
new_model_without_prefix = AutoModel.from_pretrained(
|
|
tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
|
|
)
|
|
self.assertIn("the shapes did not match", cl.out)
|
|
input_ids = ids_tensor((2, 8), 10)
|
|
new_model_without_prefix.to(torch_device)
|
|
if self.is_encoder_decoder:
|
|
new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
|
|
else:
|
|
new_model_without_prefix(input_ids)
|
|
|
|
|
|
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()
|
|
|
|
|
|
def check_models_equal(model1, model2):
|
|
models_are_equal = True
|
|
for model1_p, model2_p in zip(model1.parameters(), model2.parameters()):
|
|
if model1_p.data.ne(model2_p.data).sum() > 0:
|
|
models_are_equal = False
|
|
|
|
return models_are_equal
|
|
|
|
|
|
@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)
|
|
|
|
self.assertEqual(len(loading_info["missing_keys"]), 0)
|
|
self.assertEqual(len(loading_info["unexpected_keys"]), 8)
|
|
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
|
|
self.assertEqual(len(loading_info["error_msgs"]), 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_subfolder(self):
|
|
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
model = BertModel(config)
|
|
|
|
subfolder = "bert"
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(os.path.join(tmp_dir, subfolder))
|
|
|
|
with self.assertRaises(OSError):
|
|
_ = BertModel.from_pretrained(tmp_dir)
|
|
|
|
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
|
|
|
|
self.assertTrue(check_models_equal(model, model_loaded))
|
|
|
|
def test_model_from_pretrained_subfolder_sharded(self):
|
|
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
model = BertModel(config)
|
|
|
|
subfolder = "bert"
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
|
|
|
|
with self.assertRaises(OSError):
|
|
_ = BertModel.from_pretrained(tmp_dir)
|
|
|
|
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
|
|
|
|
self.assertTrue(check_models_equal(model, model_loaded))
|
|
|
|
def test_model_from_pretrained_hub_subfolder(self):
|
|
subfolder = "bert"
|
|
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
|
|
with self.assertRaises(OSError):
|
|
_ = BertModel.from_pretrained(model_id)
|
|
|
|
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
|
|
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_model_from_pretrained_hub_subfolder_sharded(self):
|
|
subfolder = "bert"
|
|
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
|
|
with self.assertRaises(OSError):
|
|
_ = BertModel.from_pretrained(model_id)
|
|
|
|
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
|
|
|
|
self.assertIsNotNone(model)
|
|
|
|
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. explicit from_pretrained's torch_dtype argument
|
|
# 2. via autodiscovery by looking at model weights (torch_dtype="auto")
|
|
# so if a model.half() was saved, we want it to be instantiated as such.
|
|
#
|
|
# test an explicit model class, but also AutoModel separately as the latter goes through a different code path
|
|
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, torch.float16)
|
|
self.assertEqual(model.dtype, torch.float16)
|
|
|
|
# tests `config.torch_dtype` saving
|
|
with open(f"{model_path}/config.json") as f:
|
|
config_dict = json.load(f)
|
|
self.assertEqual(config_dict["torch_dtype"], "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)
|
|
|
|
# test AutoModel separately as it goes through a different path
|
|
# test auto-detection
|
|
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
|
|
self.assertEqual(model.dtype, torch.float32)
|
|
# test forcing an explicit dtype
|
|
model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
|
|
self.assertEqual(model.dtype, torch.float16)
|
|
|
|
# test model whose first param is not of a floating type, but int
|
|
model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto")
|
|
self.assertEqual(model.dtype, torch.float32)
|
|
|
|
def test_no_super_init_config_and_model(self):
|
|
config = NoSuperInitConfig(attribute=32)
|
|
model = NoSuperInitModel(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(tmp_dir)
|
|
|
|
new_model = NoSuperInitModel.from_pretrained(tmp_dir)
|
|
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.equal(p1, p2))
|
|
|
|
def test_shard_checkpoint(self):
|
|
# This is the model we will use, total size 340,000 bytes.
|
|
model = torch.nn.Sequential(
|
|
torch.nn.Linear(100, 200, bias=False), # size 80,000
|
|
torch.nn.Linear(200, 200, bias=False), # size 160,000
|
|
torch.nn.Linear(200, 100, bias=False), # size 80,000
|
|
torch.nn.Linear(100, 50, bias=False), # size 20,000
|
|
)
|
|
state_dict = model.state_dict()
|
|
|
|
with self.subTest("No shard when max size is bigger than model size"):
|
|
shards, index = shard_checkpoint(state_dict)
|
|
self.assertIsNone(index)
|
|
self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict})
|
|
|
|
with self.subTest("Test sharding, no weights bigger than max size"):
|
|
shards, index = shard_checkpoint(state_dict, max_shard_size="300kB")
|
|
# Split is first two layers then last two.
|
|
self.assertDictEqual(
|
|
index,
|
|
{
|
|
"metadata": {"total_size": 340000},
|
|
"weight_map": {
|
|
"0.weight": "pytorch_model-00001-of-00002.bin",
|
|
"1.weight": "pytorch_model-00001-of-00002.bin",
|
|
"2.weight": "pytorch_model-00002-of-00002.bin",
|
|
"3.weight": "pytorch_model-00002-of-00002.bin",
|
|
},
|
|
},
|
|
)
|
|
|
|
shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]}
|
|
shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
|
|
self.assertDictEqual(
|
|
shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2}
|
|
)
|
|
|
|
with self.subTest("Test sharding with weights bigger than max size"):
|
|
shards, index = shard_checkpoint(state_dict, max_shard_size="100kB")
|
|
# Split is first layer, second layer then last 2.
|
|
self.assertDictEqual(
|
|
index,
|
|
{
|
|
"metadata": {"total_size": 340000},
|
|
"weight_map": {
|
|
"0.weight": "pytorch_model-00001-of-00003.bin",
|
|
"1.weight": "pytorch_model-00002-of-00003.bin",
|
|
"2.weight": "pytorch_model-00003-of-00003.bin",
|
|
"3.weight": "pytorch_model-00003-of-00003.bin",
|
|
},
|
|
},
|
|
)
|
|
|
|
shard1 = {"0.weight": state_dict["0.weight"]}
|
|
shard2 = {"1.weight": state_dict["1.weight"]}
|
|
shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
|
|
self.assertDictEqual(
|
|
shards,
|
|
{
|
|
"pytorch_model-00001-of-00003.bin": shard1,
|
|
"pytorch_model-00002-of-00003.bin": shard2,
|
|
"pytorch_model-00003-of-00003.bin": shard3,
|
|
},
|
|
)
|
|
|
|
def test_checkpoint_sharding_local(self):
|
|
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
|
|
for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]:
|
|
model.save_pretrained(tmp_dir, max_shard_size=max_size)
|
|
|
|
# Get each shard file and its size
|
|
shard_to_size = {}
|
|
for shard in os.listdir(tmp_dir):
|
|
if shard.endswith(".bin"):
|
|
shard_file = os.path.join(tmp_dir, shard)
|
|
shard_to_size[shard_file] = os.path.getsize(shard_file)
|
|
|
|
index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)
|
|
# Check there is an index but no regular weight file
|
|
self.assertTrue(os.path.isfile(index_file))
|
|
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
|
|
|
|
# Check a file is bigger than max_size only when it has a single weight
|
|
for shard_file, size in shard_to_size.items():
|
|
if max_size.endswith("kiB"):
|
|
max_size_int = int(max_size[:-3]) * 2**10
|
|
else:
|
|
max_size_int = int(max_size[:-2]) * 10**3
|
|
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
|
|
# the size asked for (since we count parameters)
|
|
if size >= max_size_int + 50000:
|
|
state_dict = torch.load(shard_file)
|
|
self.assertEqual(len(state_dict), 1)
|
|
|
|
# Check the index and the shard files found match
|
|
with open(index_file, "r", encoding="utf-8") as f:
|
|
index = json.loads(f.read())
|
|
|
|
all_shards = set(index["weight_map"].values())
|
|
shards_found = set(f for f in os.listdir(tmp_dir) if f.endswith(".bin"))
|
|
self.assertSetEqual(all_shards, shards_found)
|
|
|
|
# Finally, check the model can be reloaded
|
|
new_model = BertModel.from_pretrained(tmp_dir)
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
def test_checkpoint_sharding_from_hub(self):
|
|
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
|
|
# the model above is the same as the model below, just a sharded version.
|
|
ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
for p1, p2 in zip(model.parameters(), ref_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
@require_accelerate
|
|
def test_from_pretrained_low_cpu_mem_usage_functional(self):
|
|
# test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and
|
|
# sharded models
|
|
|
|
mnames = [
|
|
"hf-internal-testing/tiny-random-bert-sharded",
|
|
"hf-internal-testing/tiny-random-bert",
|
|
]
|
|
for mname in mnames:
|
|
_ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True)
|
|
|
|
@require_usr_bin_time
|
|
@require_accelerate
|
|
def test_from_pretrained_low_cpu_mem_usage_measured(self):
|
|
# test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default
|
|
|
|
mname = "bert-base-cased"
|
|
|
|
preamble = "from transformers import AutoModel"
|
|
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)'
|
|
max_rss_normal = self.python_one_liner_max_rss(one_liner_str)
|
|
# print(f"{max_rss_normal=}")
|
|
|
|
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)'
|
|
max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str)
|
|
# print(f"{max_rss_low_mem=}")
|
|
|
|
diff_bytes = max_rss_normal - max_rss_low_mem
|
|
diff_percent = diff_bytes / max_rss_low_mem
|
|
# print(f"{diff_bytes=}, {diff_percent=}")
|
|
# ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but
|
|
# measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that
|
|
# it's at least 15% less cpu memory consumed
|
|
|
|
self.assertGreater(
|
|
diff_percent,
|
|
0.15,
|
|
"should use less CPU memory for low_cpu_mem_usage=True, "
|
|
f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}",
|
|
)
|
|
|
|
# if you want to compare things manually, let's first look at the size of the model in bytes
|
|
# model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False)
|
|
# total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
|
|
# total_bytes = total_numel * 4 # 420MB
|
|
# Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent.
|
|
# The easiest way to test this is to switch the model and torch.load to do all the work on
|
|
# gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add
|
|
# functionality to load models directly on gpu, this test can be rewritten to use torch's
|
|
# cuda memory tracking and then we should be able to do a much more precise test.
|
|
|
|
@require_accelerate
|
|
@require_torch_multi_gpu
|
|
@slow
|
|
def test_model_parallelism_gpt2(self):
|
|
device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1}
|
|
for i in range(12):
|
|
device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1
|
|
|
|
model = AutoModelForCausalLM.from_pretrained("gpt2", device_map=device_map)
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("gpt2")
|
|
inputs = tokenizer("Hello, my name is", return_tensors="pt")
|
|
output = model.generate(inputs["input_ids"].to(0))
|
|
|
|
text_output = tokenizer.decode(output[0].tolist())
|
|
self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm")
|
|
|
|
@require_accelerate
|
|
@require_torch_gpu
|
|
def test_from_pretrained_disk_offload_task_model(self):
|
|
model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2")
|
|
device_map = {
|
|
"transformer.wte": 0,
|
|
"transformer.wpe": 0,
|
|
"transformer.h.0": "cpu",
|
|
"transformer.h.1": "cpu",
|
|
"transformer.h.2": "cpu",
|
|
"transformer.h.3": "disk",
|
|
"transformer.h.4": "disk",
|
|
"transformer.ln_f": 0,
|
|
"lm_head": 0,
|
|
}
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
inputs = torch.tensor([[1, 2, 3]]).to(0)
|
|
|
|
model.save_pretrained(tmp_dir)
|
|
new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0)
|
|
outputs1 = new_model.to(0)(inputs)
|
|
|
|
offload_folder = os.path.join(tmp_dir, "offload")
|
|
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
|
|
tmp_dir, device_map=device_map, offload_folder=offload_folder
|
|
)
|
|
outputs2 = new_model_with_offload(inputs)
|
|
|
|
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
|
|
|
|
# With state dict temp offload
|
|
offload_folder = os.path.join(tmp_dir, "offload")
|
|
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
|
|
tmp_dir,
|
|
device_map=device_map,
|
|
offload_folder=offload_folder,
|
|
offload_state_dict=True,
|
|
)
|
|
outputs2 = new_model_with_offload(inputs)
|
|
|
|
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
|
|
|
|
def test_cached_files_are_used_when_internet_is_down(self):
|
|
# A mock response for an HTTP head request to emulate server down
|
|
response_mock = mock.Mock()
|
|
response_mock.status_code = 500
|
|
response_mock.headers = {}
|
|
response_mock.raise_for_status.side_effect = HTTPError
|
|
response_mock.json.return_value = {}
|
|
|
|
# Download this model to make sure it's in the cache.
|
|
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
|
|
# Under the mock environment we get a 500 error when trying to reach the model.
|
|
with mock.patch("requests.request", return_value=response_mock) as mock_head:
|
|
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
# This check we did call the fake head request
|
|
mock_head.assert_called()
|
|
|
|
def test_load_from_one_file(self):
|
|
try:
|
|
tmp_file = tempfile.mktemp()
|
|
with open(tmp_file, "wb") as f:
|
|
http_get(
|
|
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", f
|
|
)
|
|
|
|
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
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|
_ = BertModel.from_pretrained(tmp_file, config=config)
|
|
finally:
|
|
os.remove(tmp_file)
|
|
|
|
def test_legacy_load_from_url(self):
|
|
# This test is for deprecated behavior and can be removed in v5
|
|
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
_ = BertModel.from_pretrained(
|
|
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", config=config
|
|
)
|
|
|
|
@require_safetensors
|
|
def test_safetensors_save_and_load(self):
|
|
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(tmp_dir, safe_serialization=True)
|
|
# No pytorch_model.bin file, only a model.safetensors
|
|
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
|
|
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
|
|
|
|
new_model = BertModel.from_pretrained(tmp_dir)
|
|
|
|
# Check models are equal
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
@require_safetensors
|
|
def test_safetensors_load_from_hub(self):
|
|
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
|
|
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
|
|
# Check models are equal
|
|
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
@require_safetensors
|
|
def test_safetensors_save_and_load_sharded(self):
|
|
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB")
|
|
# No pytorch_model.bin index file, only a model.safetensors index
|
|
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
|
|
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
|
|
# No regular weights file
|
|
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
|
|
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
|
|
|
|
new_model = BertModel.from_pretrained(tmp_dir)
|
|
|
|
# Check models are equal
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
@require_safetensors
|
|
def test_safetensors_load_from_hub_sharded(self):
|
|
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors")
|
|
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
|
|
|
|
# Check models are equal
|
|
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
def test_base_model_to_head_model_load(self):
|
|
base_model = BaseModel(PretrainedConfig())
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
base_model.save_pretrained(tmp_dir)
|
|
|
|
# Can load a base model in a model with head
|
|
model = ModelWithHead.from_pretrained(tmp_dir)
|
|
for p1, p2 in zip(model.base.parameters(), base_model.parameters()):
|
|
self.assertTrue(torch.allclose(p1, p2))
|
|
|
|
# It doesn't work if the state dict has a mix of keys of the head and base without prefix though.
|
|
base_state_dict = base_model.state_dict()
|
|
head_state_dict = model.state_dict()
|
|
base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"]
|
|
base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"]
|
|
torch.save(base_state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError, "The state dictionary of the model you are trying to load is corrupted."
|
|
):
|
|
_ = ModelWithHead.from_pretrained(tmp_dir)
|
|
|
|
|
|
@require_torch
|
|
@is_staging_test
|
|
class ModelPushToHubTester(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls._token = TOKEN
|
|
set_access_token(TOKEN)
|
|
HfFolder.save_token(TOKEN)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
try:
|
|
delete_repo(token=cls._token, repo_id="test-model")
|
|
except HTTPError:
|
|
pass
|
|
|
|
try:
|
|
delete_repo(token=cls._token, repo_id="valid_org/test-model-org")
|
|
except HTTPError:
|
|
pass
|
|
|
|
try:
|
|
delete_repo(token=cls._token, repo_id="test-dynamic-model")
|
|
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)
|
|
model.push_to_hub("test-model", 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))
|
|
|
|
# Reset repo
|
|
delete_repo(token=self._token, repo_id="test-model")
|
|
|
|
# Push to hub via save_pretrained
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(tmp_dir, repo_id="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)
|
|
model.push_to_hub("valid_org/test-model-org", use_auth_token=self._token)
|
|
|
|
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))
|
|
|
|
# Reset repo
|
|
delete_repo(token=self._token, repo_id="valid_org/test-model-org")
|
|
|
|
# Push to hub via save_pretrained
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(
|
|
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-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))
|
|
|
|
def test_push_to_hub_dynamic_model(self):
|
|
CustomConfig.register_for_auto_class()
|
|
CustomModel.register_for_auto_class()
|
|
|
|
config = CustomConfig(hidden_size=32)
|
|
model = CustomModel(config)
|
|
|
|
model.push_to_hub("test-dynamic-model", use_auth_token=self._token)
|
|
# checks
|
|
self.assertDictEqual(
|
|
config.auto_map,
|
|
{"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"},
|
|
)
|
|
|
|
new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
|
|
# Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module
|
|
self.assertEqual(new_model.__class__.__name__, "CustomModel")
|
|
for p1, p2 in zip(model.parameters(), new_model.parameters()):
|
|
self.assertTrue(torch.equal(p1, p2))
|
|
|
|
config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
|
|
new_model = AutoModel.from_config(config, trust_remote_code=True)
|
|
self.assertEqual(new_model.__class__.__name__, "CustomModel")
|