# 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 collections
import copy
import gc
import inspect
import os
import os.path
import pickle
import random
import re
import tempfile
import warnings
from collections import defaultdict
from typing import Dict, List, Tuple
import numpy as np
from pytest import mark
import transformers
from transformers import (
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 (
CaptureLogger,
is_pt_flax_cross_test,
is_pt_tf_cross_test,
require_accelerate,
require_safetensors,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
torch_device,
)
from transformers.utils import (
CONFIG_NAME,
GENERATION_CONFIG_NAME,
WEIGHTS_NAME,
is_accelerate_available,
is_flax_available,
is_tf_available,
is_torch_fx_available,
)
from transformers.utils.generic import ModelOutput
if is_accelerate_available():
from accelerate.utils import compute_module_sizes
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, AdaptiveEmbedding
from transformers.pytorch_utils import id_tensor_storage
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
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()
@require_torch
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],
)
@slow
def test_torchscript_simple(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torchscript(config, inputs_dict)
@slow
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
@slow
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
# This is copied from `torch/testing/_internal/jit_utils.py::clear_class_registry`
def clear_torch_jit_class_registry(self):
torch._C._jit_clear_class_registry()
torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
# torch 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)
# TODO: To have this check, we will need at least 3 layers. Do we really need it?
# 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)
# TODO: To have this check, we will need at least 3 layers. Do we really need it?
# 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)
# TODO: To have this check, we will need at least 3 layers. Do we really need it?
# 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 = {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 - 0)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
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 - 0)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
heads_to_prune = {0: [0], 1: [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.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [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)
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
self.assertTrue(model.config.vocab_size + 10, model_vocab_size)
model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0] // 64, 0)
model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0] // 64, 0)
with self.assertRaisesRegex(
ValueError,
"Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer",
):
model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
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))
@require_safetensors
def test_can_use_safetensors(self):
config, _ = 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 there was no complain of missing weights
self.assertEqual(infos["missing_keys"], [])
# 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_load_save_without_tied_weights(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.tie_word_embeddings = False
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as d:
model.save_pretrained(d)
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.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 there was no complain of missing weights
self.assertEqual(infos["missing_keys"], [])
def test_tied_weights_keys(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.tie_word_embeddings = True
for model_class in self.all_model_classes:
model_tied = model_class(config)
ptrs = collections.defaultdict(list)
for name, tensor in model_tied.state_dict().items():
ptrs[id_tensor_storage(tensor)].append(name)
# These are all the pointers of shared tensors.
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
# Detect we get a hit for each key
for key in tied_weight_keys:
if not any(re.search(key, p) for group in tied_params for p in group):
raise ValueError(f"{key} is not a tied weight key for {model_class}.")
# Removed tied weights found from tied params -> there should only be one left after
for key in tied_weight_keys:
for i in range(len(tied_params)):
tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
tied_params = [group for group in tied_params if len(group) > 1]
self.assertListEqual(
tied_params,
[],
f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
)
def test_model_weights_reload_no_missing_tied_weights(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
# 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(tmp_dir, "pytorch_model.bin"), "wb") as f:
torch.save({}, f)
model_reloaded, infos = model_class.from_pretrained(tmp_dir, output_loading_info=True)
prefix = f"{model_reloaded.base_model_prefix}."
params = dict(model_reloaded.named_parameters())
params.update(dict(model_reloaded.named_buffers()))
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
# Remove tied weights from extra missing: they are normally not warned as missing if their tied
# counterpart is present but here there are no weights at all so we do get the warning.
ptrs = collections.defaultdict(list)
for name, tensor in model_reloaded.state_dict().items():
ptrs[id_tensor_storage(tensor)].append(name)
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
for group in tied_params:
group = {k[len(prefix) :] if k.startswith(prefix) else k for k in group}
# We remove the group from extra_missing if not all weights from group are in it
if len(group - extra_missing) > 0:
extra_missing = extra_missing - set(group)
self.assertEqual(
extra_missing,
set(),
f"This model {model_class.__name__} might be missing some `keys_to_ignore`: {extra_missing}. "
f"For debugging, tied parameters are {tied_params}",
)
missed_missing = param_names - missing_keys
# Remove nonpersistent buffers from missed_missing
buffers = [n for n, _ in model_reloaded.named_buffers()]
nonpersistent_buffers = {n for n in buffers if n not in model_reloaded.state_dict()}
nonpersistent_buffers = {
k[len(prefix) :] if k.startswith(prefix) else k for k in nonpersistent_buffers
}
missed_missing = missed_missing - nonpersistent_buffers
if model_reloaded._keys_to_ignore_on_load_missing is None:
expected_missing = set()
else:
expected_missing = set(model_reloaded._keys_to_ignore_on_load_missing)
self.assertEqual(
missed_missing,
expected_missing,
f"This model {model_class.__name__} ignores keys {missed_missing} but they look like real"
" parameters. If they are non persistent buffers make sure to instantiate them with"
" `persistent=False`",
)
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))
@is_pt_tf_cross_test
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."
)
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
@require_torch_multi_gpu
def test_multi_gpu_data_parallel_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# some params shouldn't be scattered by nn.DataParallel
# so just remove them if they are present.
blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
for k in blacklist_non_batched_params:
inputs_dict.pop(k, None)
# move input tensors to cuda:O
for k, v in inputs_dict.items():
if torch.is_tensor(v):
inputs_dict[k] = v.to(0)
for model_class in self.all_model_classes:
model = model_class(config=config)
model.to(0)
model.eval()
# Wrap model in nn.DataParallel
model = nn.DataParallel(model)
with torch.no_grad():
_ = model(**self._prepare_for_class(inputs_dict, model_class))
@require_torch_multi_gpu
def test_model_parallelization(self):
if not self.test_model_parallel:
return
# a candidate for testing_utils
def get_current_gpu_memory_use():
"""returns a list of cuda memory allocations per GPU in MBs"""
per_device_memory = []
for id in range(torch.cuda.device_count()):
with torch.cuda.device(id):
per_device_memory.append(torch.cuda.memory_allocated() >> 20)
return per_device_memory
# Needs a large model to see the difference.
config = self.model_tester.get_large_model_config()
for model_class in self.all_parallelizable_model_classes:
torch.cuda.empty_cache()
# 1. single gpu memory load + unload + memory measurements
# Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests)
memory_at_start = get_current_gpu_memory_use()
# Put model on device 0 and take a memory snapshot
model = model_class(config)
model.to("cuda:0")
memory_after_model_load = get_current_gpu_memory_use()
# The memory use on device 0 should be higher than it was initially.
self.assertGreater(memory_after_model_load[0], memory_at_start[0])
del model
gc.collect()
torch.cuda.empty_cache()
# 2. MP test
# it's essential to re-calibrate the usage before the next stage
memory_at_start = get_current_gpu_memory_use()
# Spread model layers over multiple devices
model = model_class(config)
model.parallelize()
memory_after_parallelization = get_current_gpu_memory_use()
# Assert that the memory use on all devices is higher than it was when loaded only on CPU
for n in range(len(model.device_map.keys())):
self.assertGreater(memory_after_parallelization[n], memory_at_start[n])
# Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it
self.assertLess(memory_after_parallelization[0], memory_after_model_load[0])
# Assert that the memory use of device 1 is higher than it was when the entire model was loaded
# on device 0 and device 1 wasn't used at all
self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1])
del model
gc.collect()
torch.cuda.empty_cache()
@require_torch_multi_gpu
def test_model_parallel_equal_results(self):
if not self.test_model_parallel:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_parallelizable_model_classes:
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
def cast_to_device(dictionary, device):
output = {}
for k, v in dictionary.items():
if isinstance(v, torch.Tensor):
output[k] = v.to(device)
else:
output[k] = v
return output
model = model_class(config)
output = model(**cast_to_device(inputs_dict, "cpu"))
model.parallelize()
parallel_output = model(**cast_to_device(inputs_dict, "cuda:0"))
for value, parallel_value in zip(output, parallel_output):
if isinstance(value, torch.Tensor):
self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7))
elif isinstance(value, (Tuple, List)):
for value_, parallel_value_ in zip(value, parallel_value):
self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7))
@require_torch_multi_gpu
def test_model_parallel_beam_search(self):
if not self.test_model_parallel:
return
all_generative_and_parallelizable_model_classes = tuple(
set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes)
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in all_generative_and_parallelizable_model_classes:
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
def cast_to_device(dictionary, device):
output = {}
for k, v in dictionary.items():
if isinstance(v, torch.Tensor):
output[k] = v.to(device)
else:
output[k] = v
return output
model.parallelize()
model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
def check_device_map_is_respected(self, model, device_map):
for param_name, param in model.named_parameters():
# Find device in device_map
while len(param_name) > 0 and param_name not in device_map:
param_name = ".".join(param_name.split(".")[:-1])
if param_name not in device_map:
raise ValueError("device map is incomplete, it does not contain any device for `param_name`.")
param_device = device_map[param_name]
if param_device in ["cpu", "disk"]:
self.assertEqual(param.device, torch.device("meta"))
else:
self.assertEqual(param.device, torch.device(param_device))
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_disk_offload(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class._no_split_modules is None:
continue
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict_class)
model_size = compute_module_sizes(model)[""]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
with self.assertRaises(ValueError):
max_size = int(self.model_split_percents[0] * model_size)
max_memory = {0: max_size, "cpu": max_size}
# 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)
max_size = int(self.model_split_percents[1] * model_size)
max_memory = {0: max_size, "cpu": max_size}
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))
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_cpu_offload(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class._no_split_modules is None:
continue
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict_class)
model_size = compute_module_sizes(model)[""]
# We test several splits of sizes to make sure it works.
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]]
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))
@require_accelerate
@mark.accelerate_tests
@require_torch_multi_gpu
def test_model_parallelism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class._no_split_modules is None:
continue
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict_class)
model_size = compute_module_sizes(model)[""]
# We test several splits of sizes to make sure it works.
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]]
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)
def test_model_is_small(self):
# Just a consistency check to make sure we are not running tests on 80M parameter models.
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
num_params = model.num_parameters()
assert (
num_params < 1000000
), f"{model_class} is too big for the common tests ({num_params})! It should have 1M max."
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()