def test_default_with_dict(self):
features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
self.assertEqual(batch["labels"].dtype, torch.long)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
# With label_ids
features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue(batch["labels"].equal(torch.tensor([[0, 1, 2]] * 8)))
self.assertEqual(batch["labels"].dtype, torch.long)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
# Features can already be tensors
features = [{"label": i, "inputs": torch.randint(10, [10])} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
self.assertEqual(batch["labels"].dtype, torch.long)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
# Labels can already be tensors
features = [{"label": torch.tensor(i), "inputs": torch.randint(10, [10])} for i in range(8)]
batch = default_data_collator(features)
self.assertEqual(batch["labels"].dtype, torch.long)
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
self.assertEqual(batch["labels"].dtype, torch.long)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
def test_default_with_no_labels(self):
features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue("labels" not in batch)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
# With label_ids
features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue("labels" not in batch)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
def __call__(self, video_id, video_feature, text_feature):
from transformers import default_data_collator
if self.subsampling is not None and self.subsampling > 1:
batch = []
for _ in range(self.subsampling):
centerclip_idx = random.randint(0,
len(text_feature["start"]) - 1)
sampled_max_text_len = random.randint(self.sampled_min_len,
self.sampled_max_len)
batch.append(
self.sampling(
video_id,
video_feature,
text_feature,
centerclip_idx,
sampled_max_text_len,
))
batch = self.batch_post_processing(batch, video_feature)
batch = default_data_collator(batch)
else:
batch = self.sampling(video_id, video_feature, text_feature)
batch = self.batch_post_processing(batch, video_feature)
batch["video_id"] = video_id if isinstance(video_id, str) \
else video_id[0]
return batch
def __call__(self, batch):
size = len(batch)
batch = default_data_collator(batch)
if size < self.batch_size:
for k in batch.keys():
batch[k] = self.pad_tensor(batch[k], self.padding_val_dict[k])
return batch
def get_data_from_features_or_inputs(
tokenizer: BertTokenizer,
label_list: List[str],
feature: Optional[InputFeatures] = None,
inputs: Optional[Dict[str, torch.Tensor]] = None,
) -> Tuple[str, str, str]:
if feature is not None and inputs is None:
inputs = default_data_collator([feature])
elif feature is None and inputs is not None:
pass
elif feature is None and inputs is None:
raise ValueError
elif feature is not None and inputs is not None:
raise ValueError
X, Y = decode_one_example(tokenizer=tokenizer,
label_list=label_list,
inputs=inputs,
logits=None)
premise, hypothesis = X.split("[CLS]")[1].split("[SEP]")[:2]
return premise.strip(), hypothesis.strip(), Y
def __call__(self, video_id, video_feature, text_feature):
from transformers import default_data_collator
video_idx = video_id[1]
if self.subsampling is not None and self.subsampling >= 1:
batch = []
for _ in range(self.subsampling):
centerclip_idx = random.randint(0,
len(text_feature["start"]) - 1)
batch.append(
self.sampling(video_idx, video_feature, text_feature,
centerclip_idx, self._get_text_maxlen()))
batch = self.batch_post_processing(batch, video_feature)
batch = default_data_collator(batch)
else:
raise ValueError(
"dataset.subsampling must be >= 1 for efficient video loading."
)
batch = self.sampling(video_idx, video_feature, text_feature)
batch = self.batch_post_processing(batch, video_feature)
batch["video_id"] = video_id if isinstance(video_id, str) \
else video_id[0]
# e2e: make sure frame ids is into tensor.
assert torch.is_tensor(batch["vfeats"])
return batch
def collate(self, samples: Any) -> Tensor:
"""Override to convert a set of samples to a batch."""
if self.padding != "max_length":
data_collator = DataCollatorWithPadding(
AutoTokenizer.from_pretrained(self.backbone, use_fast=True))
return data_collator(samples)
return default_data_collator(samples)
def sample_batch_of_heuristic(
self,
mode: str,
heuristic: str,
size: int,
return_raw_data: bool = False) -> np.ndarray:
if mode not in ["train", "eval"]:
raise ValueError
if mode == "train":
dataset = self._hans_train_dataset
else:
dataset = self._hans_eval_dataset
if dataset is None:
raise ValueError("`dataset` is None")
indices = self.get_indices_of_heuristic(
mode=mode, heuristic=heuristic)
sampled_indices = np.random.choice(
indices, size=size, replace=False)
sampled_data = [dataset[index] for index in sampled_indices]
batched_data = default_data_collator(sampled_data)
if return_raw_data is False:
return batched_data
return batched_data, sampled_data
def hans_data_collator(
features: List[InputFeatures]) -> Dict[str, torch.Tensor]:
"""
Data collator that removes the "pairID" key if present.
"""
batch = default_data_collator(features)
_ = batch.pop("pairID", None)
return batch
def test_default_with_no_labels(self):
features = [{
"label": None,
"inputs": [0, 1, 2, 3, 4, 5]
} for i in range(8)]
batch = default_data_collator(features, return_tensors="np")
self.assertTrue("labels" not in batch)
self.assertEqual(batch["inputs"].shape, (8, 6))
# With label_ids
features = [{
"label_ids": None,
"inputs": [0, 1, 2, 3, 4, 5]
} for i in range(8)]
batch = default_data_collator(features, return_tensors="np")
self.assertTrue("labels" not in batch)
self.assertEqual(batch["inputs"].shape, (8, 6))
def test_default_with_dict(self):
features = [{
"label": i,
"inputs": [0, 1, 2, 3, 4, 5]
} for i in range(8)]
batch = default_data_collator(features, return_tensors="np")
self.assertEqual(batch["labels"].tolist(), list(range(8)))
self.assertEqual(batch["labels"].dtype, np.int64)
self.assertEqual(batch["inputs"].shape, (8, 6))
# With label_ids
features = [{
"label_ids": [0, 1, 2],
"inputs": [0, 1, 2, 3, 4, 5]
} for i in range(8)]
batch = default_data_collator(features, return_tensors="np")
self.assertEqual(batch["labels"].tolist(), [[0, 1, 2]] * 8)
self.assertEqual(batch["labels"].dtype, np.int64)
self.assertEqual(batch["inputs"].shape, (8, 6))
# Features can already be tensors
features = [{
"label": i,
"inputs": np.random.randint(0, 10, [10])
} for i in range(8)]
batch = default_data_collator(features, return_tensors="np")
self.assertEqual(batch["labels"].tolist(), list(range(8)))
self.assertEqual(batch["labels"].dtype, np.int64)
self.assertEqual(batch["inputs"].shape, (8, 10))
# Labels can already be tensors
features = [{
"label": np.array(i),
"inputs": np.random.randint(0, 10, [10])
} for i in range(8)]
batch = default_data_collator(features, return_tensors="np")
self.assertEqual(batch["labels"].dtype, np.int64)
self.assertEqual(batch["labels"].tolist(), (list(range(8))))
self.assertEqual(batch["labels"].dtype, np.int64)
self.assertEqual(batch["inputs"].shape, (8, 10))
def __call__(self, sharded_video_idxs, video_features, text_features):
from transformers import default_data_collator
batch, video_ids = [], []
for video_id, video_feature, text_feature in \
zip(sharded_video_idxs, video_features, text_features):
sub_batch = super().__call__(video_id, video_feature, text_feature)
batch.append(sub_batch)
if isinstance(video_id, tuple):
video_id = video_id[0]
video_ids.append(video_id)
batch = default_data_collator(batch)
batch["video_id"] = video_ids
return batch
def meta_text_labels(self, config):
from transformers import default_data_collator
from ..utils import get_local_rank
text_processor = TextProcessor(config)
binarizer = MetaTextBinarizer(config)
# TODO: add prompts to .yaml.
text_labels = [label for label in self.text_labels]
if get_local_rank() == 0:
print(text_labels)
outputs = []
for text_label in text_labels:
text_feature = text_processor(text_label)
outputs.append(binarizer(text_feature))
return default_data_collator(outputs)
def collate(self, samples: Any) -> Tensor:
"""Override to convert a set of samples to a batch"""
if isinstance(samples, dict):
samples = [samples]
return default_data_collator(samples)
def one_experiment(
use_parallel: bool,
train_heuristic: str,
eval_heuristics: List[str],
experiment_type: str,
hans_helper: HansHelper,
train_dataset: CustomGlueDataset,
task_model: torch.nn.Module,
faiss_index: faiss_utils.FAISSIndex,
s_test_damp: float,
s_test_scale: float,
s_test_num_samples: int,
trainer: transformers.Trainer,
version: str,
version_2_num_datapoints: Optional[int],
version_2_learning_rate: Optional[float],
hans_eval_heuristic_inputs: Dict[str, Any],
hans_eval_heuristic_raw_inputs: List[InputFeatures],
) -> Tuple[Dict[str, Any], Optional[torch.nn.Module]]:
if task_model.device.type != "cuda":
raise ValueError("The model is supposed to be on CUDA")
if version_2_num_datapoints is None:
raise ValueError
if version_2_learning_rate is None:
raise ValueError
if experiment_type in ["most-harmful", "most-helpful"]:
influences = influence_helpers.compute_influences_simplified(
k=DEFAULT_KNN_K,
faiss_index=faiss_index,
model=task_model,
inputs=hans_eval_heuristic_inputs,
train_dataset=train_dataset,
use_parallel=use_parallel,
s_test_damp=s_test_damp,
s_test_scale=s_test_scale,
s_test_num_samples=s_test_num_samples,
device_ids=[1, 2, 3],
precomputed_s_test=None,
faiss_index_use_mean_features_as_query=True,
)
helpful_indices, harmful_indices = misc_utils.get_helpful_harmful_indices_from_influences_dict(
influences, n=version_2_num_datapoints)
if experiment_type == "most-helpful":
datapoint_indices = helpful_indices
if experiment_type == "most-harmful":
datapoint_indices = harmful_indices
if experiment_type == "random":
# s_test = None
influences = None
hans_eval_heuristic_inputs = None
# Essentially shuffle the indices
datapoint_indices = np.random.choice(len(train_dataset),
size=len(train_dataset),
replace=False)
loss_collections = {}
accuracy_collections = {}
# num_datapoints = 1
# learning_rate = 1e-4
num_datapoints = version_2_num_datapoints
learning_rate = version_2_learning_rate
if version == "new-only-z":
datapoints = [
train_dataset[index]
for index in datapoint_indices[:num_datapoints]
]
if version == "new-only-ztest":
datapoints = hans_eval_heuristic_raw_inputs
if version == "new-z-and-ztest":
datapoints = [
train_dataset[index]
for index in datapoint_indices[:num_datapoints]
] + hans_eval_heuristic_raw_inputs
batch = default_data_collator(datapoints)
new_model, _ = pseudo_gradient_step(model=task_model,
inputs=batch,
learning_rate=learning_rate)
for heuristic in eval_heuristics:
new_model_loss, new_model_accuracy = evaluate_heuristic(
hans_helper=hans_helper,
heuristic=heuristic,
trainer=trainer,
model=new_model)
loss_collections[heuristic] = new_model_loss
accuracy_collections[heuristic] = new_model_accuracy
# print(f"Finished {num_datapoints}-{learning_rate}")
output_collections = {
# "s_test": s_test,
"influences": influences,
"loss": loss_collections,
"accuracy": accuracy_collections,
"datapoint_indices": datapoint_indices,
"learning_rate": learning_rate,
"num_datapoints": num_datapoints,
"hans_eval_heuristic_inputs": hans_eval_heuristic_inputs,
}
# Warning: Check again whether using this `new_model` is a good idea
return output_collections, new_model
def collate(self, samples: Any) -> Tensor:
"""Override to convert a set of samples to a batch."""
return default_data_collator(samples)
def _glue_data_collator(features):
"""Wrap the data collator when we return tuple from dataset."""
# since we are returning two dicts from the dataset, we have to zip it
inputs, labels = zip(*features)
return default_data_collator(inputs), torch.tensor(labels)
