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Commit ff648221 authored by Patrick von Platen's avatar Patrick von Platen
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fix conflicts

parent c0d9dd3b
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Showing with 174 additions and 131 deletions
src/transformers/configuration_utils.py
View file @ ff648221
......@@ -81,6 +81,7 @@ class PretrainedConfig(object):
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.eos_token_ids = kwargs.pop("eos_token_ids", None)
self.length_penalty = kwargs.pop("length_penalty", 1.0)
self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
# Fine-tuning task arguments
......
src/transformers/modeling_bart.py
View file @ ff648221
......@@ -16,7 +16,6 @@
import logging
import math
import random
import ipdb
from typing import Dict, List, Optional, Tuple
import torch
......
src/transformers/modeling_utils.py
View file @ ff648221
......@@ -173,7 +173,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
if getattr(output_embeddings, "bias", None) is not None:
output_embeddings.bias.data = torch.nn.functional.pad(
output_embeddings.bias.data,
(0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0]),
(0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],),
"constant",
0,
)
......@@ -411,7 +411,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
else:
raise EnvironmentError(
"Error no file named {} found in directory {} or `from_tf` set to False".format(
[WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"], pretrained_model_name_or_path
[WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index",],
pretrained_model_name_or_path,
)
)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
......@@ -425,7 +426,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
archive_file = pretrained_model_name_or_path + ".index"
else:
archive_file = hf_bucket_url(
pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME)
pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),
)
# redirect to the cache, if necessary
......@@ -520,7 +521,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
def load(module: nn.Module, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs,
)
for name, child in module._modules.items():
if child is not None:
......@@ -620,6 +621,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
pad_token_id=None,
eos_token_ids=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
):
r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling
......@@ -725,6 +727,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_ids = eos_token_ids if eos_token_ids is not None else self.config.eos_token_ids
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
no_repeat_ngram_size = (
no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
)
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
......@@ -754,6 +759,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
isinstance(eos_token_ids, (list, tuple)) and ((isinstance(e, int) and e >= 0) for e in eos_token_ids)
), "`eos_token_ids` should be a positive integer or a list/tuple of positive integers."
assert length_penalty > 0, "`length_penalty` should be strictly positive."
assert (
isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
), "`no_repeat_ngram_size` should be a positive integer."
assert (
isinstance(num_return_sequences, int) and num_return_sequences > 0
), "`num_return_sequences` should be a strictly positive integer."
......@@ -764,7 +772,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
)
input_ids = torch.full(
(batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device
(batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device,
)
else:
assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."
......@@ -811,23 +819,17 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
# TODO (PVP): check eos_token_id
# TODO (PVP): probably not the best way to check whether model is encoder decoder
is_encoder_decoder = (
hasattr(self, "model")
and hasattr(self.model, "decoder")
and hasattr(self.model, "encoder")
hasattr(self, "model") and hasattr(self.model, "decoder") and hasattr(self.model, "encoder")
)
if is_encoder_decoder:
eos_token_id = eos_token_ids[0]
assert (
bos_token_id is not None
), "Encoder Decoder Models need to have a bos_token_id"
assert (
eos_token_id is not None
), "Encoder Decoder Models need to have a eos_token_id"
assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
assert eos_token_id is not None, "Encoder Decoder Models need to have a eos_token_id"
# encoder decoder need to start with empty input_ids and copy the input_ids to encoder_inputs
encoder_inputs = input_ids
input_ids = torch.full(
(effective_batch_size * num_beams, 1),
# eos_token_id, # Why eos_token_id here? bos_token_id makes more sense no?
# eos_token_id, # Why eos_token_id here? bos_token_id makes more sense no?
bos_token_id,
dtype=torch.long,
device=next(self.parameters()).device,
......@@ -849,6 +851,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
pad_token_id,
eos_token_ids,
effective_batch_size,
......@@ -869,6 +872,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
pad_token_id,
eos_token_ids,
effective_batch_size,
......@@ -888,6 +892,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
pad_token_id,
eos_token_ids,
batch_size,
......@@ -902,9 +907,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
past = None
while cur_len < max_length:
model_inputs = self.prepare_inputs_for_generation(
input_ids, past=past, encoder_inputs=encoder_inputs
)
model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, encoder_inputs=encoder_inputs)
outputs = self(**model_inputs)
next_token_logits = outputs[0][:, -1, :]
......@@ -917,9 +920,20 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
if repetition_penalty != 1.0:
self.enforce_repetition_penalty_(next_token_logits, batch_size, 1, input_ids, repetition_penalty)
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
if no_repeat_ngram_size > 0:
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
banned_tokens = calc_banned_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len)
for batch_idx in range(batch_size):
next_token_logits[
batch_idx, banned_tokens[batch_idx]
] = -10000.0 # set eos token prob to 0 as is done for attention masks
if eos_token_ids is not None and cur_len < min_length:
for eos_token_id in eos_token_ids:
next_token_logits[:, eos_token_id] = -10000.0 # set eos token prob to 0 as is done for attention masks
next_token_logits[
:, eos_token_id
] = -10000.0 # set eos token prob to 0 as is done for attention masks
if do_sample:
# Temperature (higher temperature => more likely to sample low probability tokens)
......@@ -981,6 +995,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
pad_token_id,
eos_token_ids,
batch_size,
......@@ -993,9 +1008,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
""" Generate sequences for each example with beam search.
"""
is_encoder_decoder = (
hasattr(self, "model")
and hasattr(self.model, "decoder")
and hasattr(self.model, "encoder")
hasattr(self, "model") and hasattr(self.model, "decoder") and hasattr(self.model, "encoder")
)
# generated hypotheses
......@@ -1017,9 +1030,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
done = [False for _ in range(batch_size)]
while cur_len < max_length:
model_inputs = self.prepare_inputs_for_generation(
input_ids, past=past, encoder_inputs=encoder_inputs
)
model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, encoder_inputs=encoder_inputs)
outputs = self(**model_inputs) # (batch_size * num_beams, cur_len, vocab_size)
next_token_logits = outputs[0][:, -1, :] # (batch_size * num_beams, vocab_size)
......@@ -1030,12 +1041,23 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0:
self.enforce_repetition_penalty_(
next_token_logits, batch_size, num_beams, input_ids, repetition_penalty
next_token_logits, batch_size, num_beams, input_ids, repetition_penalty,
)
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
if no_repeat_ngram_size > 0:
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
banned_tokens = calc_banned_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len)
for batch_idx in range(batch_size):
next_token_logits[
batch_idx, banned_tokens[batch_idx]
] = -10000.0 # set eos token prob to 0 as is done for attention masks
if eos_token_ids is not None and cur_len < min_length:
for eos_token_id in eos_token_ids:
next_token_logits[:, eos_token_id] = -10000.0 # set eos token prob to 0 as is done for attention masks
next_token_logits[
:, eos_token_id
] = -10000.0 # set eos token prob to 0 as is done for attention masks
if do_sample:
# Temperature (higher temperature => more likely to sample low probability tokens)
......@@ -1070,14 +1092,18 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
# do greedy beam search
scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size)
if is_encoder_decoder: # TODO(PVP) to be refactored later
# scores[scores != scores] = -math.inf # block nans => seems very hacky here
# scores[:, pad_token_id] = -math.inf => seems very hacky here
if is_encoder_decoder: # TODO(PVP) to be refactored later - do we need this boolean flag here?
# scores[scores != scores] = -math.inf # block nans => seems very hacky here
# scores[:, pad_token_id] = -math.inf => seems very hacky here
# TODO(SS): fairseq also takes out <unk> every step, and has unk at slot 3
# if cur_len == 0: # Force BOS to be chosen => also very hacky ... seems also to work without this line
# scores[:, self.config.bos_token_id + 1 :] = -math.inf
# if cur_len == 0: # Force BOS to be chosen => also very hacky ... seems also to work without this line
# scores[:, self.config.bos_token_id + 1 :] = -math.inf
if cur_len == max_length - 1: # FORCE EOS to be chosen
all_but_eos_mask = torch.tensor([x for x in range(vocab_size) if x not in eos_token_ids], dtype=torch.long, device=next(self.parameters()).device)
all_but_eos_mask = torch.tensor(
[x for x in range(vocab_size) if x not in eos_token_ids],
dtype=torch.long,
device=next(self.parameters()).device,
)
scores[:, all_but_eos_mask] = -10000.0
assert scores.size() == (batch_size * num_beams, vocab_size)
......@@ -1175,7 +1201,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
assert torch.all(
next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx]
next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx],
)
# need to add best num_beams hypotheses to generated hyps
......@@ -1218,7 +1244,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
assert (len(hypo) == max_length for hypo in best)
decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)
if is_encoder_decoder:
# do not return first <BOS> token
return decoded[:, 1:]
return decoded
@staticmethod
def _reorder_cache(past, beam_idx):
......@@ -1235,6 +1264,30 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
return past
def calc_banned_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, step):
# Copied from fairseq for no_repeat_ngram in beam_search"""
if step + 2 < no_repeat_ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = step + 2 - no_repeat_ngram_size
end_idx = step + 1
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:end_idx].tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
......@@ -1508,7 +1561,7 @@ class SQuADHead(nn.Module):
self.answer_class = PoolerAnswerClass(config)
def forward(
self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None
self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
):
outputs = ()
......@@ -1567,7 +1620,7 @@ class SQuADHead(nn.Module):
start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits,) + outputs
# return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
# or (if labels are provided) (total_loss,)
......@@ -1636,7 +1689,7 @@ class SequenceSummary(nn.Module):
output = hidden_states.mean(dim=1)
elif self.summary_type == "cls_index":
if cls_index is None:
cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long)
cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long,)
else:
cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
......
tests/test_modeling_bart.py
View file @ ff648221
This diff is collapsed.
tests/test_modeling_common.py
View file @ ff648221
......@@ -54,13 +54,13 @@ class ModelTesterMixin:
model_tester = None
all_model_classes = ()
all_generative_model_classes = ()
_A_test_torchscript = True
_A_test_pruning = True
_A_test_resize_embeddings = True
_A_test_head_masking = True
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
test_head_masking = True
is_encoder_decoder = False
def _A_test_save_load(self):
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
......@@ -85,7 +85,7 @@ class ModelTesterMixin:
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def _A_test_initialization(self):
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
......@@ -99,7 +99,7 @@ class ModelTesterMixin:
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
)
def _A_test_determinism(self):
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
......@@ -116,7 +116,7 @@ class ModelTesterMixin:
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def _A_test_attention_outputs(self):
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
......@@ -179,25 +179,25 @@ class ModelTesterMixin:
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def _A_test_torchscript(self):
def test_torchscript(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torchscript(config, inputs_dict)
def _A_test_torchscript_output_attentions(self):
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
def _A_test_torchscript_output_hidden_state(self):
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self._A_test_torchscript:
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
......@@ -245,8 +245,8 @@ class ModelTesterMixin:
self.assertTrue(models_equal)
def _A_test_headmasking(self):
if not self._A_test_head_masking:
def test_headmasking(self):
if not self.test_head_masking:
return
global_rng.seed(42)
......@@ -299,8 +299,8 @@ class ModelTesterMixin:
self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def _A_test_head_pruning(self):
if not self._A_test_pruning:
def test_head_pruning(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
......@@ -328,8 +328,8 @@ class ModelTesterMixin:
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 _A_test_head_pruning_save_load_from_pretrained(self):
if not self._A_test_pruning:
def test_head_pruning_save_load_from_pretrained(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
......@@ -361,8 +361,8 @@ class ModelTesterMixin:
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 _A_test_head_pruning_save_load_from_config_init(self):
if not self._A_test_pruning:
def test_head_pruning_save_load_from_config_init(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
......@@ -392,8 +392,8 @@ class ModelTesterMixin:
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 _A_test_head_pruning_integration(self):
if not self._A_test_pruning:
def test_head_pruning_integration(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
......@@ -449,7 +449,7 @@ class ModelTesterMixin:
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
def _A_test_hidden_states_output(self):
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
......@@ -474,9 +474,9 @@ class ModelTesterMixin:
],
)
def _A_test_resize_tokens_embeddings(self):
def test_resize_tokens_embeddings(self):
(original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
if not self._A_test_resize_embeddings:
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
......@@ -516,7 +516,7 @@ class ModelTesterMixin:
self.assertTrue(models_equal)
def _A_test_model_common_attributes(self):
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:
......@@ -594,7 +594,7 @@ class ModelTesterMixin:
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
def _A_test_inputs_embeds(self):
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.is_encoder_decoder:
......@@ -711,7 +711,7 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
@require_torch
class ModelUtilsTest(unittest.TestCase):
@slow
def _A_test_model_from_pretrained(self):
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = BertConfig.from_pretrained(model_name)
......@@ -736,7 +736,7 @@ class ModelUtilsTest(unittest.TestCase):
class UtilsFunctionsTest(unittest.TestCase):
# tests whether the top_k_top_p function behaves as expected
def _A_test_top_k_top_p_filtering(self):
def test_top_k_top_p_filtering(self):
logits = torch.tensor(
[
[
......
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