Spaces:
Running
Running
# coding=utf-8 | |
# Copyright 2019 HuggingFace Inc. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import copy | |
import gc | |
import glob | |
import inspect | |
import json | |
import os | |
import os.path | |
import pickle | |
import random | |
import sys | |
import tempfile | |
import unittest | |
import unittest.mock as mock | |
import warnings | |
from collections import defaultdict | |
from pathlib import Path | |
from typing import Dict, List, Tuple | |
import numpy as np | |
from huggingface_hub import HfFolder, delete_repo | |
from huggingface_hub.file_download import http_get | |
from pytest import mark | |
from requests.exceptions import HTTPError | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
AutoModel, | |
AutoModelForSequenceClassification, | |
PretrainedConfig, | |
is_torch_available, | |
logging, | |
) | |
from transformers.models.auto import get_values | |
from transformers.models.auto.modeling_auto import ( | |
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, | |
MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES, | |
MODEL_FOR_BACKBONE_MAPPING_NAMES, | |
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES, | |
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, | |
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES, | |
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, | |
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES, | |
MODEL_FOR_MASKED_LM_MAPPING_NAMES, | |
MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES, | |
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES, | |
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, | |
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, | |
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, | |
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, | |
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, | |
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES, | |
MODEL_MAPPING_NAMES, | |
) | |
from transformers.testing_utils import ( | |
TOKEN, | |
USER, | |
CaptureLogger, | |
TestCasePlus, | |
is_pt_flax_cross_test, | |
is_pt_tf_cross_test, | |
is_staging_test, | |
require_accelerate, | |
require_safetensors, | |
require_torch, | |
require_torch_gpu, | |
require_torch_multi_gpu, | |
require_usr_bin_time, | |
slow, | |
torch_device, | |
) | |
from transformers.utils import ( | |
CONFIG_NAME, | |
GENERATION_CONFIG_NAME, | |
SAFE_WEIGHTS_INDEX_NAME, | |
SAFE_WEIGHTS_NAME, | |
WEIGHTS_INDEX_NAME, | |
WEIGHTS_NAME, | |
is_accelerate_available, | |
is_flax_available, | |
is_tf_available, | |
is_torch_fx_available, | |
) | |
from transformers.utils.generic import ModelOutput | |
sys.path.append(str(Path(__file__).parent.parent / "utils")) | |
from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402 | |
if is_accelerate_available(): | |
from accelerate.utils import compute_module_sizes | |
if is_torch_available(): | |
import torch | |
from test_module.custom_modeling import CustomModel, NoSuperInitModel | |
from torch import nn | |
from transformers import ( | |
BERT_PRETRAINED_MODEL_ARCHIVE_LIST, | |
MODEL_MAPPING, | |
AdaptiveEmbedding, | |
AutoModelForCausalLM, | |
AutoTokenizer, | |
BertConfig, | |
BertModel, | |
CLIPTextModel, | |
PreTrainedModel, | |
T5Config, | |
T5ForConditionalGeneration, | |
) | |
from transformers.modeling_utils import shard_checkpoint | |
# Fake pretrained models for tests | |
class BaseModel(PreTrainedModel): | |
config_class = PretrainedConfig | |
def __init__(self, config): | |
super().__init__(config) | |
self.linear = nn.Linear(4, 5) | |
self.linear_2 = nn.Linear(5, 6) | |
def forward(self, x): | |
return self.linear_2(self.linear(x)) | |
class ModelWithHead(PreTrainedModel): | |
base_model_prefix = "base" | |
config_class = PretrainedConfig | |
def _init_weights(self, module): | |
pass | |
def __init__(self, config): | |
super().__init__(config) | |
self.base = BaseModel(config) | |
# linear is a common name between Base and Head on purpose. | |
self.linear = nn.Linear(6, 3) | |
self.linear2 = nn.Linear(3, 5) | |
def forward(self, x): | |
return self.linear2(self.linear(self.base(x))) | |
if is_tf_available(): | |
import tensorflow as tf | |
if is_flax_available(): | |
import jax.numpy as jnp | |
from transformers.modeling_flax_pytorch_utils import ( | |
convert_pytorch_state_dict_to_flax, | |
load_flax_weights_in_pytorch_model, | |
) | |
if is_torch_fx_available(): | |
from transformers.utils.fx import symbolic_trace | |
def _config_zero_init(config): | |
configs_no_init = copy.deepcopy(config) | |
for key in configs_no_init.__dict__.keys(): | |
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: | |
setattr(configs_no_init, key, 1e-10) | |
if isinstance(getattr(configs_no_init, key, None), PretrainedConfig): | |
no_init_subconfig = _config_zero_init(getattr(configs_no_init, key)) | |
setattr(configs_no_init, key, no_init_subconfig) | |
return configs_no_init | |
TINY_T5 = "patrickvonplaten/t5-tiny-random" | |
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification" | |
def _mock_init_weights(self, module): | |
for name, param in module.named_parameters(recurse=False): | |
# Use the first letter of the name to get a value and go from a <> -13 to z <> 12 | |
value = ord(name[0].lower()) - 110 | |
param.data.fill_(value) | |
def _mock_all_init_weights(self): | |
# Prune heads if needed | |
if self.config.pruned_heads: | |
self.prune_heads(self.config.pruned_heads) | |
import transformers.modeling_utils | |
if transformers.modeling_utils._init_weights: | |
for module in self.modules(): | |
module._is_hf_initialized = False | |
# Initialize weights | |
self.apply(self._initialize_weights) | |
# Tie weights should be skipped when not initializing all weights | |
# since from_pretrained(...) calls tie weights anyways | |
self.tie_weights() | |
class ModelTesterMixin: | |
model_tester = None | |
all_model_classes = () | |
all_generative_model_classes = () | |
fx_compatible = False | |
test_torchscript = True | |
test_pruning = True | |
test_resize_embeddings = True | |
test_resize_position_embeddings = False | |
test_head_masking = True | |
test_mismatched_shapes = True | |
test_missing_keys = True | |
test_model_parallel = False | |
is_encoder_decoder = False | |
has_attentions = True | |
model_split_percents = [0.5, 0.7, 0.9] | |
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): | |
inputs_dict = copy.deepcopy(inputs_dict) | |
if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES): | |
inputs_dict = { | |
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() | |
if isinstance(v, torch.Tensor) and v.ndim > 1 | |
else v | |
for k, v in inputs_dict.items() | |
} | |
elif model_class.__name__ in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES): | |
inputs_dict.pop("attention_mask") | |
if return_labels: | |
if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES): | |
inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) | |
elif model_class.__name__ in [ | |
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES), | |
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES), | |
]: | |
inputs_dict["start_positions"] = torch.zeros( | |
self.model_tester.batch_size, dtype=torch.long, device=torch_device | |
) | |
inputs_dict["end_positions"] = torch.zeros( | |
self.model_tester.batch_size, dtype=torch.long, device=torch_device | |
) | |
elif model_class.__name__ in [ | |
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES), | |
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES), | |
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES), | |
*get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES), | |
*get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES), | |
]: | |
inputs_dict["labels"] = torch.zeros( | |
self.model_tester.batch_size, dtype=torch.long, device=torch_device | |
) | |
elif model_class.__name__ in [ | |
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES), | |
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES), | |
*get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES), | |
*get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES), | |
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES), | |
]: | |
inputs_dict["labels"] = torch.zeros( | |
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device | |
) | |
elif model_class.__name__ in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES): | |
num_patches = self.model_tester.image_size // self.model_tester.patch_size | |
inputs_dict["bool_masked_pos"] = torch.zeros( | |
(self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device | |
) | |
elif model_class.__name__ in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES): | |
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape | |
inputs_dict["labels"] = torch.zeros( | |
[self.model_tester.batch_size, height, width], device=torch_device | |
).long() | |
return inputs_dict | |
def test_save_load(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
def check_save_load(out1, out2): | |
# make sure we don't have nans | |
out_2 = out2.cpu().numpy() | |
out_2[np.isnan(out_2)] = 0 | |
out_1 = out1.cpu().numpy() | |
out_1[np.isnan(out_1)] = 0 | |
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] | |
with tempfile.TemporaryDirectory() as tmpdirname: | |
model.save_pretrained(tmpdirname) | |
# the config file (and the generation config file, if it can generate) should be saved | |
self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME))) | |
self.assertEqual( | |
model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME)) | |
) | |
model = model_class.from_pretrained(tmpdirname) | |
model.to(torch_device) | |
with torch.no_grad(): | |
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_save_load(tensor1, tensor2) | |
else: | |
check_save_load(first, second) | |
def test_from_pretrained_no_checkpoint(self): | |
config, _ = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
model = model_class(config) | |
state_dict = model.state_dict() | |
new_model = model_class.from_pretrained( | |
pretrained_model_name_or_path=None, config=config, state_dict=state_dict | |
) | |
for p1, p2 in zip(model.parameters(), new_model.parameters()): | |
self.assertTrue(torch.equal(p1, p2)) | |
def test_save_load_keys_to_ignore_on_save(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) | |
_keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None) | |
if _keys_to_ignore_on_save is None: | |
continue | |
# check the keys are in the original state_dict | |
for k in _keys_to_ignore_on_save: | |
self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys())) | |
# check that certain keys didn't get saved with the model | |
with tempfile.TemporaryDirectory() as tmpdirname: | |
model.save_pretrained(tmpdirname) | |
output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME) | |
state_dict_saved = torch.load(output_model_file) | |
for k in _keys_to_ignore_on_save: | |
self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys())) | |
# Test we can load the state dict in the model, necessary for the checkpointing API in Trainer. | |
load_result = model.load_state_dict(state_dict_saved, strict=False) | |
self.assertTrue( | |
len(load_result.missing_keys) == 0 | |
or set(load_result.missing_keys) == set(model._keys_to_ignore_on_save) | |
) | |
self.assertTrue(len(load_result.unexpected_keys) == 0) | |
def test_gradient_checkpointing_backward_compatibility(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
if not model_class.supports_gradient_checkpointing: | |
continue | |
config.gradient_checkpointing = True | |
model = model_class(config) | |
self.assertTrue(model.is_gradient_checkpointing) | |
def test_gradient_checkpointing_enable_disable(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
if not model_class.supports_gradient_checkpointing: | |
continue | |
# at init model should have gradient checkpointing disabled | |
model = model_class(config) | |
self.assertFalse(model.is_gradient_checkpointing) | |
# check enable works | |
model.gradient_checkpointing_enable() | |
self.assertTrue(model.is_gradient_checkpointing) | |
# check disable works | |
model.gradient_checkpointing_disable() | |
self.assertFalse(model.is_gradient_checkpointing) | |
def test_save_load_fast_init_from_base(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
if config.__class__ not in MODEL_MAPPING: | |
return | |
base_class = MODEL_MAPPING[config.__class__] | |
if isinstance(base_class, tuple): | |
base_class = base_class[0] | |
for model_class in self.all_model_classes: | |
if model_class == base_class: | |
continue | |
# make a copy of model class to not break future tests | |
# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class | |
class CopyClass(model_class): | |
pass | |
model_class_copy = CopyClass | |
# make sure that all keys are expected for test | |
model_class_copy._keys_to_ignore_on_load_missing = [] | |
# make init deterministic, but make sure that | |
# non-initialized weights throw errors nevertheless | |
model_class_copy._init_weights = _mock_init_weights | |
model_class_copy.init_weights = _mock_all_init_weights | |
model = base_class(config) | |
state_dict = model.state_dict() | |
# this will often delete a single weight of a multi-weight module | |
# to test an edge case | |
random_key_to_del = random.choice(list(state_dict.keys())) | |
del state_dict[random_key_to_del] | |
# check that certain keys didn't get saved with the model | |
with tempfile.TemporaryDirectory() as tmpdirname: | |
model.save_pretrained(tmpdirname) | |
torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) | |
model_fast_init = model_class_copy.from_pretrained(tmpdirname) | |
model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False) | |
# Before we test anything | |
for key in model_fast_init.state_dict().keys(): | |
if isinstance(model_slow_init.state_dict()[key], torch.BoolTensor): | |
max_diff = (model_slow_init.state_dict()[key] ^ model_fast_init.state_dict()[key]).sum().item() | |
else: | |
max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item() | |
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") | |
def test_save_load_fast_init_to_base(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
if config.__class__ not in MODEL_MAPPING: | |
return | |
base_class = MODEL_MAPPING[config.__class__] | |
if isinstance(base_class, tuple): | |
base_class = base_class[0] | |
for model_class in self.all_model_classes: | |
if model_class == base_class: | |
continue | |
# make a copy of model class to not break future tests | |
# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class | |
class CopyClass(base_class): | |
pass | |
base_class_copy = CopyClass | |
# make sure that all keys are expected for test | |
base_class_copy._keys_to_ignore_on_load_missing = [] | |
# make init deterministic, but make sure that | |
# non-initialized weights throw errors nevertheless | |
base_class_copy._init_weights = _mock_init_weights | |
base_class_copy.init_weights = _mock_all_init_weights | |
model = model_class(config) | |
state_dict = model.state_dict() | |
# this will often delete a single weight of a multi-weight module | |
# to test an edge case | |
random_key_to_del = random.choice(list(state_dict.keys())) | |
del state_dict[random_key_to_del] | |
# check that certain keys didn't get saved with the model | |
with tempfile.TemporaryDirectory() as tmpdirname: | |
model.config.save_pretrained(tmpdirname) | |
torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) | |
model_fast_init = base_class_copy.from_pretrained(tmpdirname) | |
model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False) | |
for key in model_fast_init.state_dict().keys(): | |
if isinstance(model_slow_init.state_dict()[key], torch.BoolTensor): | |
max_diff = torch.max( | |
model_slow_init.state_dict()[key] ^ model_fast_init.state_dict()[key] | |
).item() | |
else: | |
max_diff = torch.max( | |
torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]) | |
).item() | |
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") | |
def test_initialization(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
configs_no_init = _config_zero_init(config) | |
for model_class in self.all_model_classes: | |
model = model_class(config=configs_no_init) | |
for name, param in model.named_parameters(): | |
if param.requires_grad: | |
self.assertIn( | |
((param.data.mean() * 1e9).round() / 1e9).item(), | |
[0.0, 1.0], | |
msg=f"Parameter {name} of model {model_class} seems not properly initialized", | |
) | |
def test_determinism(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
def check_determinism(first, second): | |
out_1 = first.cpu().numpy() | |
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.__name__ in [ | |
*get_values(MODEL_MAPPING_NAMES), | |
*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES), | |
]: | |
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.__name__ | |
in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)] | |
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): | |
if not self.has_attentions: | |
self.skipTest(reason="Model does not output attentions") | |
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.__name__ in [ | |
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES), | |
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES), | |
]: | |
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], | |
) | |
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) | |
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) | |
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 1.8 has no `_clear_class_state` in `torch.jit._state` | |
if hasattr(torch.jit._state, "_clear_class_state"): | |
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 model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.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_can_use_safetensors(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: | |
try: | |
model_tied.save_pretrained(d, safe_serialization=True) | |
except Exception as e: | |
raise Exception(f"Class {model_class.__name__} cannot be saved using safetensors: {e}") | |
model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True) | |
# Checking the state dicts are correct | |
reloaded_state = model_reloaded.state_dict() | |
for k, v in model_tied.state_dict().items(): | |
self.assertIn(k, reloaded_state, f"Key {k} is missing from reloaded") | |
torch.testing.assert_close( | |
v, reloaded_state[k], msg=lambda x: f"{model_class.__name__}: Tensor {k}: {x}" | |
) | |
# Checking the tensor sharing are correct | |
ptrs = defaultdict(list) | |
for k, v in model_tied.state_dict().items(): | |
ptrs[v.data_ptr()].append(k) | |
shared_ptrs = {k: v for k, v in ptrs.items() if len(v) > 1} | |
for _, shared_names in shared_ptrs.items(): | |
reloaded_ptrs = {reloaded_state[k].data_ptr() for k in shared_names} | |
self.assertEqual( | |
len(reloaded_ptrs), | |
1, | |
f"The shared pointers are incorrect, found different pointers for keys {shared_names}", | |
) | |
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 = {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 = {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} | |
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)) | |
def test_pt_tf_model_equivalence(self, allow_missing_keys=False): | |
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 not 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, allow_missing_keys=allow_missing_keys | |
) | |
pt_model = transformers.load_tf2_model_in_pytorch_model( | |
pt_model, tf_model, allow_missing_keys=allow_missing_keys | |
) | |
# 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, allow_missing_keys=allow_missing_keys | |
) | |
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, allow_missing_keys=allow_missing_keys | |
) | |
# 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." | |
) | |
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) | |
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] | |
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)) | |
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() | |
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)) | |
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)) | |
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], atol=1e-5)) | |
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], atol=1e-5)) | |
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], atol=1e-5)) | |
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.__name__ not in [ | |
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES), | |
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES), | |
]: | |
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.__name__ not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES): | |
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 | |
class ModelUtilsTest(TestCasePlus): | |
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) | |
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) | |
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) | |
def remove_torch_dtype(model_path): | |
file = f"{model_path}/config.json" | |
with open(file, "r", encoding="utf-8") as f: | |
s = json.load(f) | |
s.pop("torch_dtype") | |
with open(file, "w", encoding="utf-8") as f: | |
json.dump(s, f) | |
# 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) | |
# 1. test torch_dtype="auto" via `config.torch_dtype` | |
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") | |
self.assertEqual(model.dtype, torch.float32) | |
# 2. test torch_dtype="auto" via auto-derivation | |
# now remove the torch_dtype entry from config.json and try "auto" again which should | |
# perform auto-derivation from weights | |
remove_torch_dtype(model_path) | |
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) | |
# 1. test torch_dtype="auto" via `config.torch_dtype` | |
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") | |
# 2. test torch_dtype="auto" via auto-derivation | |
# now same with using config info | |
remove_torch_dtype(model_path) | |
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") | |
self.assertEqual(model.dtype, torch.float16) | |
# test fp16 save_pretrained, loaded with the explicit fp16 | |
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) | |
self.assertEqual(model.dtype, torch.float16) | |
# test AutoModel separately as it goes through a different path | |
# test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry | |
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto") | |
# test that the config object didn't get polluted with torch_dtype="auto" | |
# there was a bug that after this call we ended up with config.torch_dtype=="auto" | |
self.assertNotEqual(model.config.torch_dtype, "auto") | |
# now test the outcome | |
self.assertEqual(model.dtype, torch.float32) | |
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 = {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)) | |
def test_checkpoint_variant_local(self): | |
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
model.save_pretrained(tmp_dir, variant="v2") | |
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"]) | |
weights_file = os.path.join(tmp_dir, weights_name) | |
self.assertTrue(os.path.isfile(weights_file)) | |
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained(tmp_dir) | |
new_model = BertModel.from_pretrained(tmp_dir, variant="v2") | |
for p1, p2 in zip(model.parameters(), new_model.parameters()): | |
self.assertTrue(torch.allclose(p1, p2)) | |
def test_checkpoint_variant_local_sharded(self): | |
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB") | |
weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"]) | |
weights_index_file = os.path.join(tmp_dir, weights_index_name) | |
self.assertTrue(os.path.isfile(weights_index_file)) | |
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME))) | |
for i in range(1, 6): | |
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["bin"]) | |
weights_name_file = os.path.join(tmp_dir, weights_name) | |
self.assertTrue(os.path.isfile(weights_name_file)) | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained(tmp_dir) | |
new_model = BertModel.from_pretrained(tmp_dir, variant="v2") | |
for p1, p2 in zip(model.parameters(), new_model.parameters()): | |
self.assertTrue(torch.allclose(p1, p2)) | |
def test_checkpoint_variant_local_safe(self): | |
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True) | |
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"]) | |
weights_file = os.path.join(tmp_dir, weights_name) | |
self.assertTrue(os.path.isfile(weights_file)) | |
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained(tmp_dir) | |
new_model = BertModel.from_pretrained(tmp_dir, variant="v2") | |
for p1, p2 in zip(model.parameters(), new_model.parameters()): | |
self.assertTrue(torch.allclose(p1, p2)) | |
def test_checkpoint_variant_local_sharded_safe(self): | |
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True) | |
weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"]) | |
weights_index_file = os.path.join(tmp_dir, weights_index_name) | |
self.assertTrue(os.path.isfile(weights_index_file)) | |
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))) | |
for i in range(1, 6): | |
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["safetensors"]) | |
weights_name_file = os.path.join(tmp_dir, weights_name) | |
self.assertTrue(os.path.isfile(weights_name_file)) | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained(tmp_dir) | |
new_model = BertModel.from_pretrained(tmp_dir, variant="v2") | |
for p1, p2 in zip(model.parameters(), new_model.parameters()): | |
self.assertTrue(torch.allclose(p1, p2)) | |
def test_checkpoint_variant_hub(self): | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir) | |
model = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2" | |
) | |
self.assertIsNotNone(model) | |
def test_checkpoint_variant_hub_sharded(self): | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir | |
) | |
model = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2" | |
) | |
self.assertIsNotNone(model) | |
def test_checkpoint_variant_hub_safe(self): | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir) | |
model = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2" | |
) | |
self.assertIsNotNone(model) | |
def test_checkpoint_variant_hub_sharded_safe(self): | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
with self.assertRaises(EnvironmentError): | |
_ = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir | |
) | |
model = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2" | |
) | |
self.assertIsNotNone(model) | |
def test_checkpoint_variant_save_load(self): | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
model = BertModel.from_pretrained( | |
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2" | |
) | |
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"]) | |
model.save_pretrained(tmp_dir, variant="v2") | |
# saving will create a variant checkpoint | |
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name))) | |
model.save_pretrained(tmp_dir) | |
# saving shouldn't delete variant checkpoints | |
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"]) | |
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name))) | |
# there should be a normal checkpoint | |
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) | |
self.assertIsNotNone(model) | |
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) | |
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. | |
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") | |
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") | |
_ = 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 | |
) | |
def test_use_safetensors(self): | |
# test nice error message if no safetensor files available | |
with self.assertRaises(OSError) as env_error: | |
AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", use_safetensors=True) | |
self.assertTrue( | |
"model.safetensors or model.safetensors.index.json and thus cannot be loaded with `safetensors`" | |
in str(env_error.exception) | |
) | |
# test that error if only safetensors is available | |
with self.assertRaises(OSError) as env_error: | |
BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors", use_safetensors=False) | |
self.assertTrue("does not appear to have a file named pytorch_model.bin" in str(env_error.exception)) | |
# test that only safetensors if both available and use_safetensors=False | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
CLIPTextModel.from_pretrained( | |
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", | |
subfolder="text_encoder", | |
use_safetensors=False, | |
cache_dir=tmp_dir, | |
) | |
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*")) | |
self.assertTrue(any(f.endswith("bin") for f in all_downloaded_files)) | |
self.assertFalse(any(f.endswith("safetensors") for f in all_downloaded_files)) | |
# test that no safetensors if both available and use_safetensors=True | |
with tempfile.TemporaryDirectory() as tmp_dir: | |
CLIPTextModel.from_pretrained( | |
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", | |
subfolder="text_encoder", | |
use_safetensors=True, | |
cache_dir=tmp_dir, | |
) | |
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*")) | |
self.assertTrue(any(f.endswith("safetensors") for f in all_downloaded_files)) | |
self.assertFalse(any(f.endswith("bin") for f in all_downloaded_files)) | |
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)) | |
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)) | |
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)) | |
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) | |
def test_pretrained_low_mem_new_config(self): | |
# Checking for 1 model(the same one which was described in the issue) . | |
model_ids = ["gpt2"] | |
for model_id in model_ids: | |
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_id) | |
model_config.n_layer = 48 | |
model_config.n_head = 25 | |
model_config.n_embd = 1600 | |
model = AutoModelForCausalLM.from_pretrained( | |
pretrained_model_name_or_path=model_id, | |
config=model_config, | |
ignore_mismatched_sizes=True, | |
torch_dtype=torch.float16, | |
low_cpu_mem_usage=True, | |
) | |
model_ref = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id) | |
self.assertEqual(model.__class__.__name__, model_ref.__class__.__name__) | |
class ModelPushToHubTester(unittest.TestCase): | |
def setUpClass(cls): | |
cls._token = TOKEN | |
HfFolder.save_token(TOKEN) | |
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") | |