Line endings should be LF across repo and not CRLF (#10119)

0d8e554d · Lysandre Debut · GitHub · 937f6707 · 0d8e554d · 0d8e554d
Unverified Commit 0d8e554d authored Feb 10, 2021 by Lysandre Debut Committed by GitHub Feb 10, 2021
3 changed files
--- a/.gitattributes
+++ b/.gitattributes
+*.py	eol=lf
+*.rst	eol=lf
+*.md	eol=lf
\ No newline at end of file
--- a/examples/research_projects/bertology/run_prune_gpt.py
+++ b/examples/research_projects/bertology/run_prune_gpt.py
 #!/usr/bin/env python3
 """ This script is adapted from the Bertology pruning code (https://github.com/huggingface/transformers/blob/783d7d2629e97c5f0c5f9ef01b8c66410275c204/examples/research_projects/bertology/run_bertology.py)
 to prune GPT-like models. The author is @altsoph.
 """
 import argparse
 import logging
 import os
 from datetime import datetime
 import numpy as np
 import torch
 from torch.utils.data import DataLoader, RandomSampler, TensorDataset
 from tqdm import tqdm
 from transformers import GPT2LMHeadModel
 logger = logging.getLogger(__name__)
 def save_model(model, dirpath):
    # save results
    if os.path.exists(dirpath):
        if os.path.exists(os.path.join(dirpath, "config.json")) and os.path.isfile(
            os.path.join(dirpath, "config.json")
        ):
            os.remove(os.path.join(dirpath, "config.json"))
        if os.path.exists(os.path.join(dirpath, "pytorch_model.bin")) and os.path.isfile(
            os.path.join(dirpath, "pytorch_model.bin")
        ):
            os.remove(os.path.join(dirpath, "pytorch_model.bin"))
    else:
        os.makedirs(dirpath)
    model.save_pretrained(dirpath)
 def entropy(p, unlogit=False):
    """ Compute the entropy of a probability distribution """
    exponent = 2
    if unlogit:
        p = torch.pow(p, exponent)
    plogp = p * torch.log(p)
    plogp[p == 0] = 0
    return -plogp.sum(dim=-1)
 def print_2d_tensor(tensor):
    """ Print a 2D tensor """
    logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(tensor))))
    for row in range(len(tensor)):
        if tensor.dtype != torch.long:
            logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:.5f}" for x in tensor[row].cpu().data))
        else:
            logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:d}" for x in tensor[row].cpu().data))
 def compute_heads_importance(
    args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None, actually_pruned=False
 ):
    """This method shows how to compute:
    - head attention entropy
    - head importance scores according to http://arxiv.org/abs/1905.10650
    """
    # Prepare our tensors
    n_layers, n_heads = model.config.num_hidden_layers, model.config.num_attention_heads
    head_importance = torch.zeros(n_layers, n_heads).to(args.device)
    attn_entropy = torch.zeros(n_layers, n_heads).to(args.device)
    if head_mask is None:
        head_mask = torch.ones(n_layers, n_heads).to(args.device)
    head_mask.requires_grad_(requires_grad=True)
    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
    if actually_pruned:
        head_mask = None
    tot_tokens = 0.0
    total_loss = 0.0
    for step, inputs in enumerate(tqdm(eval_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
        inputs = tuple(t.to(args.device) for t in inputs)
        (input_ids,) = inputs
        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
        outputs = model(input_ids, labels=input_ids, head_mask=head_mask)
        #  (loss), lm_logits, presents, (all hidden_states), (attentions)
        loss, _, all_attentions = (
            outputs[0],
            outputs[1],
            outputs[-1],
        )  # Loss and logits are the first, attention the last
        loss.backward()  # Backpropagate to populate the gradients in the head mask
        total_loss += loss.detach().cpu().numpy()
        if compute_entropy:
            for layer, attn in enumerate(all_attentions):
                masked_entropy = entropy(attn.detach(), True)
                attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
        if compute_importance:
            head_importance += head_mask.grad.abs().detach()
        tot_tokens += torch.ones_like(input_ids).float().detach().sum().data
    # Normalize
    attn_entropy /= tot_tokens
    head_importance /= tot_tokens
    # Layerwise importance normalization
    if not args.dont_normalize_importance_by_layer:
        exponent = 2
        norm_by_layer = torch.pow(torch.pow(head_importance, exponent).sum(-1), 1 / exponent)
        head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
    if not args.dont_normalize_global_importance:
        head_importance = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
    # Print matrices
    if compute_entropy:
        logger.info("Attention entropies")
        print_2d_tensor(attn_entropy)
    if compute_importance:
        logger.info("Head importance scores")
        print_2d_tensor(head_importance)
    logger.info("Head ranked by importance scores")
    head_ranks = torch.zeros(head_importance.numel(), dtype=torch.long, device=args.device)
    head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(
        head_importance.numel(), device=args.device
    )
    head_ranks = head_ranks.view_as(head_importance)
    print_2d_tensor(head_ranks)
    return attn_entropy, head_importance, total_loss
 def mask_heads(args, model, eval_dataloader):
    """This method shows how to mask head (set some heads to zero), to test the effect on the network,
    based on the head importance scores, as described in Michel et al. (http://arxiv.org/abs/1905.10650)
    """
    _, head_importance, loss = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False)
    original_score = 1 / loss  # instead of downsteam score use the LM loss
    logger.info("Pruning: original score: %f, threshold: %f", original_score, original_score * args.masking_threshold)
    new_head_mask = torch.ones_like(head_importance)
    num_to_mask = max(1, int(new_head_mask.numel() * args.masking_amount))
    current_score = original_score
    while current_score >= original_score * args.masking_threshold:
        head_mask = new_head_mask.clone().detach()  # save current head mask
        # heads from least important to most - keep only not-masked heads
        head_importance[head_mask == 0.0] = float("Inf")
        current_heads_to_mask = head_importance.view(-1).sort()[1]
        if len(current_heads_to_mask) <= num_to_mask:
            print("BREAK BY num_to_mask")
            break
        # mask heads
        current_heads_to_mask = current_heads_to_mask[:num_to_mask]
        logger.info("Heads to mask: %s", str(current_heads_to_mask.tolist()))
        new_head_mask = new_head_mask.view(-1)
        new_head_mask[current_heads_to_mask] = 0.0
        new_head_mask = new_head_mask.view_as(head_mask)
        new_head_mask = new_head_mask.clone().detach()
        print_2d_tensor(new_head_mask)
        # Compute metric and head importance again
        _, head_importance, loss = compute_heads_importance(
            args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask
        )
        current_score = 1 / loss
        logger.info(
            "Masking: current score: %f, remaining heads %d (%.1f percents)",
            current_score,
            new_head_mask.sum(),
            new_head_mask.sum() / new_head_mask.numel() * 100,
        )
    logger.info("Final head mask")
    print_2d_tensor(head_mask)
    np.save(os.path.join(args.output_dir, "head_mask.npy"), head_mask.detach().cpu().numpy())
    return head_mask
 def prune_heads(args, model, eval_dataloader, head_mask):
    """This method shows how to prune head (remove heads weights) based on
    the head importance scores as described in Michel et al. (http://arxiv.org/abs/1905.10650)
    """
    # Try pruning and test time speedup
    # Pruning is like masking but we actually remove the masked weights
    before_time = datetime.now()
    _, _, loss = compute_heads_importance(
        args, model, eval_dataloader, compute_entropy=False, compute_importance=False, head_mask=head_mask
    )
    score_masking = 1 / loss
    original_time = datetime.now() - before_time
    original_num_params = sum(p.numel() for p in model.parameters())
    heads_to_prune = dict(
        (layer, (1 - head_mask[layer].long()).nonzero().squeeze().tolist()) for layer in range(len(head_mask))
    )
    for k, v in heads_to_prune.items():
        if isinstance(v, int):
            heads_to_prune[k] = [
                v,
            ]
    assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
    model.prune_heads(heads_to_prune)
    pruned_num_params = sum(p.numel() for p in model.parameters())
    before_time = datetime.now()
    _, _, loss = compute_heads_importance(
        args,
        model,
        eval_dataloader,
        compute_entropy=False,
        compute_importance=False,
        head_mask=None,
        actually_pruned=True,
    )
    score_pruning = 1 / loss
    new_time = datetime.now() - before_time
    logger.info(
        "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)",
        original_num_params,
        pruned_num_params,
        pruned_num_params / original_num_params * 100,
    )
    logger.info("Pruning: score with masking: %f score with pruning: %f", score_masking, score_pruning)
    logger.info("Pruning: speed ratio (original timing / new timing): %f percents", original_time / new_time * 100)
    save_model(model, args.output_dir)
 def main():
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--data_dir",
        default=None,
        type=str,
        required=True,
        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
    )
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        required=True,
        help="Path to pretrained model or model identifier from huggingface.co/models",
    )
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    # Other parameters
    parser.add_argument(
        "--config_name",
        default="",
        type=str,
        help="Pretrained config name or path if not the same as model_name_or_path",
    )
    parser.add_argument(
        "--tokenizer_name",
        default="",
        type=str,
        help="Pretrained tokenizer name or path if not the same as model_name_or_path",
    )
    parser.add_argument(
        "--cache_dir",
        default=None,
        type=str,
        help="Where do you want to store the pre-trained models downloaded from s3",
    )
    parser.add_argument(
        "--data_subset", type=int, default=-1, help="If > 0: limit the data to a subset of data_subset instances."
    )
    parser.add_argument(
        "--overwrite_output_dir", action="store_true", help="Whether to overwrite data in output directory"
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--dont_normalize_importance_by_layer", action="store_true", help="Don't normalize importance score by layers"
    )
    parser.add_argument(
        "--dont_normalize_global_importance",
        action="store_true",
        help="Don't normalize all importance scores between 0 and 1",
    )
    parser.add_argument(
        "--try_masking", action="store_true", help="Whether to try to mask head until a threshold of accuracy."
    )
    parser.add_argument(
        "--masking_threshold",
        default=0.9,
        type=float,
        help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value).",
    )
    parser.add_argument(
        "--masking_amount", default=0.1, type=float, help="Amount to heads to masking at each masking step."
    )
    parser.add_argument("--metric_name", default="acc", type=str, help="Metric to use for head masking.")
    parser.add_argument(
        "--max_seq_length",
        default=128,
        type=int,
        help="The maximum total input sequence length after WordPiece tokenization. \n"
        "Sequences longer than this will be truncated, sequences shorter padded.",
    )
    parser.add_argument("--batch_size", default=1, type=int, help="Batch size.")
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
    parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
    args = parser.parse_args()
    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd
        print("Waiting for debugger attach")
        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
        ptvsd.wait_for_attach()
    # Setup devices and distributed training
    if args.local_rank == -1 or args.no_cuda:
        args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank)
        args.device = torch.device("cuda", args.local_rank)
        args.n_gpu = 1
        torch.distributed.init_process_group(backend="nccl")  # Initializes the distributed backend
    # Setup logging
    logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
    logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device, args.n_gpu, bool(args.local_rank != -1)))
    model = GPT2LMHeadModel.from_pretrained(args.model_name_or_path)
    # Distributed and parallel training
    model.to(args.device)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
        )
    elif args.n_gpu > 1:
        model = torch.nn.DataParallel(model)
    # Print/save training arguments
    os.makedirs(args.output_dir, exist_ok=True)
    torch.save(args, os.path.join(args.output_dir, "run_args.bin"))
    logger.info("Training/evaluation parameters %s", args)
    # Prepare dataset
    numpy_data = np.concatenate(
        [
            np.loadtxt(args.data_dir, dtype=np.int64),
        ]
    )
    train_tensor_dataset = (torch.from_numpy(numpy_data),)
    train_data = TensorDataset(*train_tensor_dataset)
    train_sampler = RandomSampler(train_data)
    eval_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.batch_size)
    # Compute head entropy and importance score
    compute_heads_importance(args, model, eval_dataloader)
    # Try head masking (set heads to zero until the score goes under a threshole)
    # and head pruning (remove masked heads and see the effect on the network)
    if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
        head_mask = mask_heads(args, model, eval_dataloader)
        prune_heads(args, model, eval_dataloader, head_mask)
 if __name__ == "__main__":
    main()
--- a/tests/test_modeling_deberta.py
+++ b/tests/test_modeling_deberta.py
 # coding=utf-8
 # Copyright 2018 Microsoft Authors and the HuggingFace Inc. team.
 #
 # 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 random
 import unittest
 import numpy as np
 from transformers import is_torch_available
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 from .test_configuration_common import ConfigTester
 from .test_modeling_common import ModelTesterMixin, ids_tensor
 if is_torch_available():
    import torch
    from transformers import (
        DebertaConfig,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
    )
    from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
 @require_torch
 class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            DebertaModel,
            DebertaForMaskedLM,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    test_torchscript = False
    test_pruning = False
    test_head_masking = False
    is_encoder_decoder = False
    class DebertaModelTester(object):
        def __init__(
            self,
            parent,
            batch_size=13,
            seq_length=7,
            is_training=True,
            use_input_mask=True,
            use_token_type_ids=True,
            use_labels=True,
            vocab_size=99,
            hidden_size=32,
            num_hidden_layers=5,
            num_attention_heads=4,
            intermediate_size=37,
            hidden_act="gelu",
            hidden_dropout_prob=0.1,
            attention_probs_dropout_prob=0.1,
            max_position_embeddings=512,
            type_vocab_size=16,
            type_sequence_label_size=2,
            initializer_range=0.02,
            relative_attention=False,
            position_biased_input=True,
            pos_att_type="None",
            num_labels=3,
            num_choices=4,
            scope=None,
        ):
            self.parent = parent
            self.batch_size = batch_size
            self.seq_length = seq_length
            self.is_training = is_training
            self.use_input_mask = use_input_mask
            self.use_token_type_ids = use_token_type_ids
            self.use_labels = use_labels
            self.vocab_size = vocab_size
            self.hidden_size = hidden_size
            self.num_hidden_layers = num_hidden_layers
            self.num_attention_heads = num_attention_heads
            self.intermediate_size = intermediate_size
            self.hidden_act = hidden_act
            self.hidden_dropout_prob = hidden_dropout_prob
            self.attention_probs_dropout_prob = attention_probs_dropout_prob
            self.max_position_embeddings = max_position_embeddings
            self.type_vocab_size = type_vocab_size
            self.type_sequence_label_size = type_sequence_label_size
            self.initializer_range = initializer_range
            self.num_labels = num_labels
            self.num_choices = num_choices
            self.relative_attention = relative_attention
            self.position_biased_input = position_biased_input
            self.pos_att_type = pos_att_type
            self.scope = scope
        def prepare_config_and_inputs(self):
            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
            input_mask = None
            if self.use_input_mask:
                input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
            token_type_ids = None
            if self.use_token_type_ids:
                token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
            sequence_labels = None
            token_labels = None
            choice_labels = None
            if self.use_labels:
                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
                choice_labels = ids_tensor([self.batch_size], self.num_choices)
            config = DebertaConfig(
                vocab_size=self.vocab_size,
                hidden_size=self.hidden_size,
                num_hidden_layers=self.num_hidden_layers,
                num_attention_heads=self.num_attention_heads,
                intermediate_size=self.intermediate_size,
                hidden_act=self.hidden_act,
                hidden_dropout_prob=self.hidden_dropout_prob,
                attention_probs_dropout_prob=self.attention_probs_dropout_prob,
                max_position_embeddings=self.max_position_embeddings,
                type_vocab_size=self.type_vocab_size,
                initializer_range=self.initializer_range,
                relative_attention=self.relative_attention,
                position_biased_input=self.position_biased_input,
                pos_att_type=self.pos_att_type,
            )
            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        def check_loss_output(self, result):
            self.parent.assertListEqual(list(result.loss.size()), [])
        def create_and_check_deberta_model(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = DebertaModel(config=config)
            model.to(torch_device)
            model.eval()
            sequence_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)[0]
            sequence_output = model(input_ids, token_type_ids=token_type_ids)[0]
            sequence_output = model(input_ids)[0]
            self.parent.assertListEqual(
                list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size]
            )
        def create_and_check_deberta_for_masked_lm(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = DebertaForMaskedLM(config=config)
            model.to(torch_device)
            model.eval()
            result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        def create_and_check_deberta_for_sequence_classification(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_labels = self.num_labels
            model = DebertaForSequenceClassification(config)
            model.to(torch_device)
            model.eval()
            result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
            self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
            self.check_loss_output(result)
        def create_and_check_deberta_for_token_classification(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_labels = self.num_labels
            model = DebertaForTokenClassification(config=config)
            model.to(torch_device)
            model.eval()
            result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
        def create_and_check_deberta_for_question_answering(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = DebertaForQuestionAnswering(config=config)
            model.to(torch_device)
            model.eval()
            result = model(
                input_ids,
                attention_mask=input_mask,
                token_type_ids=token_type_ids,
                start_positions=sequence_labels,
                end_positions=sequence_labels,
            )
            self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
            self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
        def prepare_config_and_inputs_for_common(self):
            config_and_inputs = self.prepare_config_and_inputs()
            (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
            ) = config_and_inputs
            inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict
    def setUp(self):
        self.model_tester = DebertaModelTest.DebertaModelTester(self)
        self.config_tester = ConfigTester(self, config_class=DebertaConfig, hidden_size=37)
    def test_config(self):
        self.config_tester.run_common_tests()
    def test_deberta_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*config_and_inputs)
    def test_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*config_and_inputs)
    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*config_and_inputs)
    def test_for_question_answering(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*config_and_inputs)
    def test_for_token_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*config_and_inputs)
    @slow
    def test_model_from_pretrained(self):
        for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = DebertaModel.from_pretrained(model_name)
            self.assertIsNotNone(model)
 @require_torch
 @require_sentencepiece
 @require_tokenizers
 class DebertaModelIntegrationTest(unittest.TestCase):
    @unittest.skip(reason="Model not available yet")
    def test_inference_masked_lm(self):
        pass
    @slow
    def test_inference_no_head(self):
        random.seed(0)
        np.random.seed(0)
        torch.manual_seed(0)
        torch.cuda.manual_seed_all(0)
        model = DebertaModel.from_pretrained("microsoft/deberta-base")
        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        # compare the actual values for a slice.
        expected_slice = torch.tensor(
            [[[-0.0218, -0.6641, -0.3665], [-0.3907, -0.4716, -0.6640], [0.7461, 1.2570, -0.9063]]]
        )
        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4), f"{output[:, :3, :3]}")