wehub-resource-sync
1379 lines
54 KiB
Python
1379 lines
54 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Sequence
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddlenlp.transformers.model_utils import PretrainedModel, register_base_model
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from ...utils.converter import StateDictNameMapping
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from ...utils.env import CONFIG_NAME
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from ..activations import ACT2FN
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from ..model_outputs import (
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BaseModelOutput,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from .configuration import (
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DEBERTA_PRETRAINED_INIT_CONFIGURATION,
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DEBERTA_PRETRAINED_RESOURCE_FILES_MAP,
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DebertaConfig,
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)
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__all__ = [
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"DebertaModel",
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"DebertaForSequenceClassification",
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"DebertaForQuestionAnswering",
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"DebertaForTokenClassification",
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"DebertaPreTrainedModel",
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"DebertaForMultipleChoice",
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]
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class DropoutContext(object):
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def __init__(self):
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self.dropout = 0
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self.mask = None
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self.scale = 1
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self.reuse_mask = True
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def get_mask(input, local_context):
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if not isinstance(local_context, DropoutContext):
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dropout = local_context
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mask = None
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else:
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dropout = local_context.dropout
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dropout *= local_context.scale
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mask = local_context.mask if local_context.reuse_mask else None
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if dropout > 0 and mask is None:
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# mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool)
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probability_matrix = paddle.full(paddle.empty_like(input).shape, 1 - dropout)
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mask = (1 - paddle.bernoulli(probability_matrix)).cast("bool")
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if isinstance(local_context, DropoutContext):
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if local_context.mask is None:
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local_context.mask = mask
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return mask, dropout
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class XDropout(paddle.autograd.PyLayer):
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"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
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@staticmethod
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def forward(ctx, input, local_ctx):
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mask, dropout = get_mask(input, local_ctx)
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ctx.scale = 1.0 / (1 - dropout)
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if dropout > 0:
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ctx.save_for_backward(mask)
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return input.masked_fill(mask, 0) * ctx.scale
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else:
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return input
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@staticmethod
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def backward(ctx, grad_output):
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if ctx.scale > 1:
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(mask,) = ctx.saved_tensor()
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return grad_output.masked_fill(mask, 0) * ctx.scale, None
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else:
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return grad_output, None
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class StableDropout(nn.Layer):
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"""
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Optimized dropout module for stabilizing the training
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Args:
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drop_prob (float): the dropout probabilities
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"""
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def __init__(self, drop_prob):
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super().__init__()
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self.drop_prob = drop_prob
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self.count = 0
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self.context_stack = None
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def forward(self, x):
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"""
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Call the module
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Args:
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x (`paddle.Tensor`): The input tensor to apply dropout
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"""
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if self.training and self.drop_prob > 0:
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return XDropout.apply(x, self.get_context())
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return x
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def clear_context(self):
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self.count = 0
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self.context_stack = None
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def init_context(self, reuse_mask=True, scale=1):
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if self.context_stack is None:
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self.context_stack = []
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self.count = 0
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for c in self.context_stack:
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c.reuse_mask = reuse_mask
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c.scale = scale
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def get_context(self):
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if self.context_stack is not None:
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if self.count >= len(self.context_stack):
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self.context_stack.append(DropoutContext())
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ctx = self.context_stack[self.count]
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ctx.dropout = self.drop_prob
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self.count += 1
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return ctx
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else:
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return self.drop_prob
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def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length):
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"""
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
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are ignored. This is modified from fairseq's `utils.make_positions`.
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Args:
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x: paddle.Tensor x:
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Returns: paddle.Tensor
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"""
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if past_key_values_length is None:
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past_key_values_length = 0
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# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
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mask = (input_ids != padding_idx).cast("int64")
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incremental_indices = (paddle.cumsum(mask, axis=1) + past_key_values_length) * mask
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return incremental_indices + padding_idx
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def softmax_with_mask(x, mask, axis):
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rmask = paddle.logical_not(mask.astype("bool"))
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y = paddle.full(x.shape, -float("inf"), x.dtype)
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return F.softmax(paddle.where(rmask, y, x), axis=axis)
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class DebertaEmbeddings(nn.Layer):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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pad_token_id = getattr(config, "pad_token_id", 0)
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self.position_biased_input = getattr(config, "position_biased_input", True)
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self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
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if not self.position_biased_input:
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self.position_embeddings = None
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else:
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)
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self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)
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if config.type_vocab_size > 0:
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)
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if self.embedding_size != config.hidden_size:
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self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias_attr=False)
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self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
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self.dropout = StableDropout(config.hidden_dropout_prob)
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self.config = config
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
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if input_ids is not None:
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input_shape = input_ids.shape
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else:
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input_shape = inputs_embeds.shape[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = paddle.arange(seq_length, dtype="int64")
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position_ids = position_ids.unsqueeze(0).expand(input_shape)
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if token_type_ids is None:
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token_type_ids = paddle.zeros(input_shape, dtype="int64")
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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if self.position_embeddings is not None:
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position_embeds = self.position_embeddings(position_ids)
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else:
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position_embeds = paddle.zeros_like(inputs_embeds)
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embeddings = inputs_embeds
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if self.position_biased_input:
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embeddings = embeddings + position_embeds
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if self.config.type_vocab_size > 0:
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token_type_embeds = self.token_type_embeddings(token_type_ids)
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embeddings = embeddings + token_type_embeds
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if self.config.embedding_size != self.config.hidden_size:
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embeddings = self.embed_proj(embeddings)
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embeddings = self.LayerNorm(embeddings)
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if mask is not None:
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if mask.dim() != embeddings.dim():
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if mask.dim() == 4:
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mask = mask.squeeze(1).squeeze(1)
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mask = mask.unsqueeze(2)
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embeddings = embeddings * mask.astype(embeddings.dtype)
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embeddings = self.dropout(embeddings)
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return embeddings
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class DebertaLayerNorm(nn.Layer):
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"""LayerNorm module in the TF style (epsilon inside the square root)."""
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def __init__(self, size, eps=1e-12):
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super().__init__()
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self.weight = paddle.create_parameter(
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shape=[size], default_initializer=nn.initializer.Constant(1.0), dtype="float32"
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)
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self.add_parameter("weight", self.weight)
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self.bias = paddle.create_parameter(
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shape=[size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
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)
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self.add_parameter("bias", self.bias)
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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mean = hidden_states.mean(-1, keepdim=True)
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variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
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hidden_states = (hidden_states - mean) / paddle.sqrt(variance + self.variance_epsilon)
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y = self.weight * hidden_states + self.bias
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return y
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class DebertaSelfOutput(nn.Layer):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
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self.dropout = StableDropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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def build_relative_position(query_size, key_size):
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q_ids = paddle.arange(query_size, dtype="int64")
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k_ids = paddle.arange(key_size, dtype="int64")
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rel_pos_ids = q_ids[:, None] - paddle.tile(k_ids[None], [query_size, 1])
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rel_pos_ids = rel_pos_ids.unsqueeze(0)
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return rel_pos_ids
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def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
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return paddle.expand(
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c2p_pos, [query_layer.shape[0], query_layer.shape[1], query_layer.shape[2], relative_pos.shape[-1]]
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)
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def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
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return paddle.expand(
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c2p_pos, [query_layer.shape[0], query_layer.shape[1], key_layer.shape[-2], key_layer.shape[-2]]
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)
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def pos_dynamic_expand(pos_index, p2c_att, key_layer):
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return paddle.expand(pos_index, p2c_att.shape[:2] + (pos_index.shape[-2], key_layer.shape[-2]))
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class DisentangledSelfAttention(nn.Layer):
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def __init__(self, config):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads)
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.in_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias_attr=False)
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self.q_bias = paddle.create_parameter(
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shape=[self.all_head_size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
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)
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self.v_bias = paddle.create_parameter(
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shape=[self.all_head_size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
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)
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self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
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# To transform c2p|p2c" into ["c2p","p2c"]
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if isinstance(self.pos_att_type, str):
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self.pos_att_type = self.pos_att_type.split("|")
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self.relative_attention = getattr(config, "relative_attention", True)
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self.talking_head = getattr(config, "talking_head", False)
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if self.talking_head:
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self.head_logits_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias_attr=False)
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self.head_weights_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias_attr=False)
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if self.relative_attention:
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self.max_relative_positions = getattr(config, "max_relative_positions", -1)
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if self.max_relative_positions < 1:
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self.max_relative_positions = config.max_position_embeddings
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self.pos_dropout = nn.Dropout(config.hidden_dropout_prob)
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if "c2p" in self.pos_att_type:
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self.pos_proj = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=False)
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if "p2c" in self.pos_att_type:
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self.pos_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = StableDropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x):
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new_x_shape = x.shape[:-1] + [self.num_attention_heads, -1]
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x = paddle.reshape(x, new_x_shape)
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return paddle.transpose(x, perm=[0, 2, 1, 3])
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def forward(
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self,
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hidden_states,
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attention_mask,
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output_attentions=False,
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query_states=None,
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relative_pos=None,
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rel_embeddings=None,
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):
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if query_states is None:
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query_states = self.in_proj(hidden_states)
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query_states = self.transpose_for_scores(query_states)
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query_layer, key_layer, value_layer = paddle.chunk(query_states, 3, axis=-1)
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else:
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def linear(w, b, x):
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if b is not None:
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return paddle.matmul(x, w, transpose_y=True) + b
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else:
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return paddle.matmul(x, w, transpose_y=True)
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ws = paddle.chunk(self.in_proj.weight, self.num_attention_heads * 3, axis=0)
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qkvw = [paddle.concat([ws[i * 3 + k] for i in range(self.num_attention_heads)], axis=0) for k in range(3)]
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qkvb = [None] * 3
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q = linear(qkvw[0], qkvb[0], query_states.astype(qkvw[0].dtype))
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k, v = [linear(qkvw[i], qkvb[i], hidden_states.astype(qkvw[i].dtype)) for i in range(1, 3)]
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query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, v]]
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query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
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value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :])
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rel_att = None
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# Take the dot product between "query" and "key" to get the raw attention scores.
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scale_factor = 1 + len(self.pos_att_type)
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scale = paddle.sqrt(paddle.to_tensor(query_layer.shape[-1], dtype="float32") * scale_factor)
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query_layer = query_layer / scale
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attention_scores = paddle.matmul(query_layer, key_layer.transpose([0, 1, 3, 2]))
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if self.relative_attention:
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rel_embeddings = self.pos_dropout(rel_embeddings)
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rel_att = self.disentangled_att_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor)
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if rel_att is not None:
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attention_scores = attention_scores + rel_att
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# bxhxlxd
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if self.talking_head:
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attention_scores = self.head_logits_proj(paddle.transpose(attention_scores, [0, 2, 3, 1]))
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attention_scores = paddle.transpose(attention_scores, [0, 3, 1, 2])
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attention_probs = softmax_with_mask(attention_scores, attention_mask, -1)
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attention_probs = self.dropout(attention_probs)
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if self.talking_head:
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attention_probs = self.head_weights_proj(paddle.transpose(attention_probs, [0, 2, 3, 1]))
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attention_probs = paddle.transpose(attention_probs, [0, 3, 1, 2])
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context_layer = paddle.matmul(attention_probs, value_layer)
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context_layer = paddle.transpose(context_layer, [0, 2, 1, 3])
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context_layer = paddle.reshape(context_layer, context_layer.shape[:-2] + [-1])
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if output_attentions:
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return (context_layer, attention_probs)
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else:
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return context_layer
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def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
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if relative_pos is None:
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q = query_layer.shape[-2]
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relative_pos = build_relative_position(q, key_layer.shape[-2])
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if relative_pos.ndim == 2:
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relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
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elif relative_pos.ndim == 3:
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relative_pos = relative_pos.unsqueeze(1)
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# bxhxqxk
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elif relative_pos.ndim != 4:
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raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.ndim}")
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att_span = min(max(query_layer.shape[-2], key_layer.shape[-2]), self.max_relative_positions)
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relative_pos = relative_pos.astype("int64")
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rel_embeddings = rel_embeddings[
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self.max_relative_positions - att_span : self.max_relative_positions + att_span, :
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]
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rel_embeddings = paddle.unsqueeze(rel_embeddings, axis=0)
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score = 0
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if "c2p" in self.pos_att_type:
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pos_key_layer = self.pos_proj(rel_embeddings)
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pos_key_layer = self.transpose_for_scores(pos_key_layer)
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c2p_att = paddle.matmul(query_layer, pos_key_layer.transpose([0, 1, 3, 2]))
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c2p_pos = paddle.clip(relative_pos + att_span, 0, att_span * 2 - 1)
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c2p_att = paddle.take_along_axis(
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c2p_att, axis=-1, indices=c2p_dynamic_expand(c2p_pos, query_layer, relative_pos)
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)
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score += c2p_att
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if "p2c" in self.pos_att_type:
|
|
pos_query_layer = self.pos_q_proj(rel_embeddings)
|
|
pos_query_layer = self.transpose_for_scores(pos_query_layer)
|
|
pos_query_layer /= paddle.sqrt(paddle.to_tensor(pos_query_layer.shape[-1], dtype="float32") * scale_factor)
|
|
if query_layer.shape[-2] != key_layer.shape[-2]:
|
|
r_pos = build_relative_position(key_layer.shape[-2], key_layer.shape[-2])
|
|
else:
|
|
r_pos = relative_pos
|
|
p2c_pos = paddle.clip(-r_pos + att_span, 0, att_span * 2 - 1)
|
|
p2c_att = paddle.matmul(key_layer, pos_query_layer.transpose([0, 1, 3, 2]).astype(key_layer.dtype))
|
|
p2c_att = paddle.take_along_axis(
|
|
p2c_att, axis=-1, indices=p2c_dynamic_expand(p2c_pos, query_layer, key_layer)
|
|
).transpose([0, 1, 3, 2])
|
|
|
|
if query_layer.shape[-2] != key_layer.shape[-2]:
|
|
pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)
|
|
p2c_att = paddle.gather(p2c_att, axis=-2, index=pos_dynamic_expand(pos_index, p2c_att, key_layer))
|
|
score += p2c_att
|
|
|
|
return score
|
|
|
|
|
|
class DebertaAttention(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = DisentangledSelfAttention(config)
|
|
self.output = DebertaSelfOutput(config)
|
|
self.config = config
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions=False,
|
|
query_states=None,
|
|
relative_pos=None,
|
|
rel_embeddings=None,
|
|
):
|
|
self_output = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions,
|
|
query_states=query_states,
|
|
relative_pos=relative_pos,
|
|
rel_embeddings=rel_embeddings,
|
|
)
|
|
|
|
if output_attentions:
|
|
self_output, att_matrix = self_output
|
|
|
|
if query_states is None:
|
|
query_states = hidden_states
|
|
|
|
attention_output = self.output(self_output, query_states)
|
|
|
|
if output_attentions:
|
|
return (attention_output, att_matrix)
|
|
else:
|
|
return attention_output
|
|
|
|
|
|
class DebertaIntermediate(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class DebertaOutput(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
|
|
self.dropout = StableDropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states, input_tensor):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class DebertaLayer(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.attention = DebertaAttention(config)
|
|
self.intermediate = DebertaIntermediate(config)
|
|
self.output = DebertaOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask,
|
|
query_states=None,
|
|
relative_pos=None,
|
|
rel_embeddings=None,
|
|
output_attentions=False,
|
|
):
|
|
attention_output = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions=output_attentions,
|
|
query_states=query_states,
|
|
relative_pos=relative_pos,
|
|
rel_embeddings=rel_embeddings,
|
|
)
|
|
if output_attentions:
|
|
attention_output, att_matrix = attention_output
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
if output_attentions:
|
|
return (layer_output, att_matrix)
|
|
else:
|
|
return layer_output
|
|
|
|
|
|
class DebertaEncoder(paddle.nn.Layer):
|
|
"""Modified BertEncoder with relative position bias support"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.layer = paddle.nn.LayerList([DebertaLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.relative_attention = getattr(config, "relative_attention", False)
|
|
if self.relative_attention:
|
|
self.max_relative_positions = getattr(config, "max_relative_positions", -1)
|
|
if self.max_relative_positions < 1:
|
|
self.max_relative_positions = config.max_position_embeddings
|
|
self.rel_embeddings = nn.Embedding(self.max_relative_positions * 2, config.hidden_size)
|
|
self.gradient_checkpointing = False
|
|
|
|
def get_rel_embedding(self):
|
|
rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
|
|
return rel_embeddings
|
|
|
|
def get_attention_mask(self, attention_mask):
|
|
if attention_mask.dim() <= 2:
|
|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
|
|
attention_mask = attention_mask.astype("float32")
|
|
elif attention_mask.dim() == 3:
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
return attention_mask
|
|
|
|
def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
|
|
if self.relative_attention and relative_pos is None:
|
|
q = query_states.shape[-2] if query_states is not None else hidden_states.shape[-2]
|
|
relative_pos = build_relative_position(q, hidden_states.shape[-2])
|
|
return relative_pos
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask,
|
|
output_hidden_states=True,
|
|
output_attentions=False,
|
|
query_states=None,
|
|
relative_pos=None,
|
|
return_dict=None,
|
|
):
|
|
attention_mask = self.get_attention_mask(attention_mask)
|
|
relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
if isinstance(hidden_states, Sequence):
|
|
next_kv = hidden_states[0]
|
|
else:
|
|
next_kv = hidden_states
|
|
rel_embeddings = self.get_rel_embedding()
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
hidden_states = paddle.utils.checkpoint.checkpoint(
|
|
create_custom_forward(layer_module),
|
|
next_kv,
|
|
attention_mask,
|
|
query_states,
|
|
relative_pos,
|
|
rel_embeddings,
|
|
)
|
|
else:
|
|
hidden_states = layer_module(
|
|
next_kv,
|
|
attention_mask,
|
|
query_states=query_states,
|
|
relative_pos=relative_pos,
|
|
rel_embeddings=rel_embeddings,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
if output_attentions:
|
|
hidden_states, att_m = hidden_states
|
|
|
|
if query_states is not None:
|
|
query_states = hidden_states
|
|
if isinstance(hidden_states, Sequence):
|
|
next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
|
|
else:
|
|
next_kv = hidden_states
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (att_m,)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
)
|
|
|
|
|
|
class DebertaPreTrainedModel(PretrainedModel):
|
|
"""
|
|
An abstract class for pretrained BERT models. It provides BERT related
|
|
`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
|
|
`pretrained_init_configuration`, `base_model_prefix` for downloading and
|
|
loading pretrained models.
|
|
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
|
|
"""
|
|
|
|
model_config_file = CONFIG_NAME
|
|
config_class = DebertaConfig
|
|
resource_files_names = {"model_state": "model_state.pdparams"}
|
|
base_model_prefix = "deberta"
|
|
|
|
pretrained_init_configuration = DEBERTA_PRETRAINED_INIT_CONFIGURATION
|
|
pretrained_resource_files_map = DEBERTA_PRETRAINED_RESOURCE_FILES_MAP
|
|
|
|
@classmethod
|
|
def _get_name_mappings(cls, config):
|
|
mappings = []
|
|
model_mappings = [
|
|
["embeddings.word_embeddings.weight", "embeddings.word_embeddings.weight"],
|
|
["embeddings.LayerNorm.weight", "embeddings.LayerNorm.weight"],
|
|
["embeddings.LayerNorm.bias", "embeddings.LayerNorm.bias"],
|
|
["embeddings.position_embeddings.weight", "embeddings.position_embeddings.weight"],
|
|
["embeddings.token_type_embeddings.weight", "embeddings.token_type_embeddings.weight"],
|
|
["encoder.rel_embeddings.weight", "encoder.rel_embeddings.weight"],
|
|
]
|
|
for layer_index in range(config.num_hidden_layers):
|
|
|
|
layer_mappings = [
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.q_bias",
|
|
f"encoder.layer.{layer_index}.attention.self.q_bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.v_bias",
|
|
f"encoder.layer.{layer_index}.attention.self.v_bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.in_proj.weight",
|
|
f"encoder.layer.{layer_index}.attention.self.in_proj.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.pos_proj.weight",
|
|
f"encoder.layer.{layer_index}.attention.self.pos_proj.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.weight",
|
|
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.bias",
|
|
f"encoder.layer.{layer_index}.attention.self.pos_q_proj.bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.output.dense.weight",
|
|
f"encoder.layer.{layer_index}.attention.output.dense.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.output.dense.bias",
|
|
f"encoder.layer.{layer_index}.attention.output.dense.bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
|
|
f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
|
|
f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.intermediate.dense.weight",
|
|
f"encoder.layer.{layer_index}.intermediate.dense.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.intermediate.dense.bias",
|
|
f"encoder.layer.{layer_index}.intermediate.dense.bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.output.dense.weight",
|
|
f"encoder.layer.{layer_index}.output.dense.weight",
|
|
"transpose",
|
|
],
|
|
[f"encoder.layer.{layer_index}.output.dense.bias", f"encoder.layer.{layer_index}.output.dense.bias"],
|
|
[
|
|
f"encoder.layer.{layer_index}.output.LayerNorm.weight",
|
|
f"encoder.layer.{layer_index}.output.LayerNorm.weight",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.output.LayerNorm.bias",
|
|
f"encoder.layer.{layer_index}.output.LayerNorm.bias",
|
|
],
|
|
]
|
|
model_mappings.extend(layer_mappings)
|
|
# adapt for hf-internal-testing/tiny-random-DebertaModel
|
|
if config.architectures is not None and "DebertaModel" in config.architectures:
|
|
pass
|
|
else:
|
|
for mapping in model_mappings:
|
|
mapping[0] = "deberta." + mapping[0]
|
|
mapping[1] = "deberta." + mapping[1]
|
|
mappings = [StateDictNameMapping(*mapping, index=index) for index, mapping in enumerate(model_mappings)]
|
|
return mappings
|
|
|
|
def init_weights(self, layer):
|
|
"""Initialization hook"""
|
|
if isinstance(layer, (nn.Linear, nn.Embedding)):
|
|
# In the dygraph mode, use the `set_value` to reset the parameter directly,
|
|
# and reset the `state_dict` to update parameter in static mode.
|
|
if isinstance(layer.weight, paddle.Tensor):
|
|
layer.weight.set_value(
|
|
paddle.tensor.normal(
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
shape=layer.weight.shape,
|
|
)
|
|
)
|
|
|
|
elif isinstance(layer, nn.LayerNorm):
|
|
layer._epsilon = self.config.layer_norm_eps
|
|
|
|
|
|
@register_base_model
|
|
class DebertaModel(DebertaPreTrainedModel):
|
|
def __init__(self, config: DebertaConfig):
|
|
super(DebertaModel, self).__init__(config)
|
|
self.config = config
|
|
self.embeddings = DebertaEmbeddings(config)
|
|
self.encoder = DebertaEncoder(config)
|
|
self.z_steps = getattr(config, "z_steps", 0)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.shape[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
if attention_mask is None:
|
|
attention_mask = paddle.ones(input_shape, dtype="int64")
|
|
if token_type_ids is None:
|
|
token_type_ids = paddle.zeros(input_shape, dtype="int64")
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask,
|
|
output_hidden_states=True,
|
|
output_attentions=output_attentions,
|
|
return_dict=return_dict,
|
|
)
|
|
if not return_dict:
|
|
encoded_layers = encoder_outputs[1]
|
|
else:
|
|
encoded_layers = encoder_outputs.hidden_states
|
|
|
|
if self.z_steps > 1:
|
|
hidden_states = encoded_layers[-2]
|
|
layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
|
|
query_states = encoded_layers[-1]
|
|
rel_embeddings = self.encoder.get_rel_embedding()
|
|
attention_mask = self.encoder.get_attention_mask(attention_mask)
|
|
rel_pos = self.encoder.get_rel_pos(embedding_output)
|
|
for layer in layers[1:]:
|
|
query_states = layer(
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions=False,
|
|
query_states=query_states,
|
|
relative_pos=rel_pos,
|
|
rel_embeddings=rel_embeddings,
|
|
)
|
|
encoded_layers.append(query_states)
|
|
|
|
sequence_output = encoded_layers[-1]
|
|
|
|
if not return_dict:
|
|
return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class DebertaPredictionHeadTransform(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class DebertaLMPredictionHead(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = DebertaPredictionHeadTransform(config)
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias_attr=False)
|
|
self.bias = paddle.create_parameter(
|
|
shape=[config.vocab_size], default_initializer=nn.initializer.Constant(0.0), dtype="float32"
|
|
)
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class DebertaOnlyMLMHead(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = DebertaLMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class DebertaForMaskedLM(DebertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.deberta = DebertaModel(config)
|
|
self.cls = DebertaOnlyMLMHead(config)
|
|
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.deberta(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(prediction_scores.reshape(-1, self.config.vocab_size), labels.reshape(-1))
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class ContextPooler(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
hidden_size = config.pooler_hidden_size if config.pooler_hidden_size is not None else config.hidden_size
|
|
self.dense = nn.Linear(config.hidden_size, hidden_size)
|
|
self.dropout = StableDropout(config.pooler_dropout)
|
|
self.config = config
|
|
|
|
def forward(self, hidden_states):
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
context_token = hidden_states[:, 0, :]
|
|
context_token = self.dropout(context_token)
|
|
pooled_output = self.dense(context_token)
|
|
pooled_output = F.gelu(pooled_output)
|
|
return pooled_output
|
|
|
|
@property
|
|
def output_dim(self):
|
|
return self.config.hidden_size
|
|
|
|
|
|
class DebertaForSequenceClassification(DebertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.deberta = DebertaModel(config)
|
|
|
|
self.pooler = ContextPooler(config)
|
|
output_dim = self.pooler.output_dim if self.pooler is not None else config.hidden_size
|
|
self.classifier = nn.Linear(output_dim, config.num_labels)
|
|
|
|
drop_out = getattr(config, "cls_dropout", None)
|
|
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
|
|
|
|
self.dropout = StableDropout(drop_out)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.deberta.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
return self.deberta.set_input_embeddings(new_embeddings)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.deberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = self.pooler(outputs[0])
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.num_labels == 1:
|
|
loss_fct = paddle.nn.MSELoss()
|
|
loss = loss_fct(logits, labels)
|
|
elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
|
|
else:
|
|
loss_fct = paddle.nn.BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class DebertaForTokenClassification(DebertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.deberta = DebertaModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.deberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class DebertaForQuestionAnswering(DebertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.deberta = DebertaModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
start_positions=None,
|
|
end_positions=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.deberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
logits = paddle.transpose(logits, perm=[2, 0, 1])
|
|
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if start_positions.ndim > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if start_positions.ndim > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.shape[1]
|
|
start_positions = start_positions.clip(0, ignored_index)
|
|
end_positions = end_positions.clip(0, ignored_index)
|
|
|
|
loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class DebertaForMultipleChoice(DebertaPreTrainedModel):
|
|
|
|
"""
|
|
Deberta Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like RocStories/SWAG tasks.
|
|
|
|
Args:
|
|
bert (:class:`DebertaModel`):
|
|
An instance of DebertaModel.
|
|
num_choices (int, optional):
|
|
The number of choices. Defaults to `2`.
|
|
dropout (float, optional):
|
|
The dropout probability for output of Bert.
|
|
If None, use the same value as `hidden_dropout_prob` of `DebertaModel`
|
|
instance `bert`. Defaults to None.
|
|
"""
|
|
|
|
def __init__(self, config: DebertaConfig):
|
|
super(DebertaForMultipleChoice, self).__init__(config)
|
|
self.deberta = DebertaModel(config)
|
|
self.dropout = StableDropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.pooler = ContextPooler(config)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
self.apply(self.init_weights)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=None,
|
|
output_attentions=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
The DebertaForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`DebertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
token_type_ids(Tensor, optional):
|
|
See :class:`DebertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
position_ids(Tensor, optional):
|
|
See :class:`DebertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
attention_mask (list, optional):
|
|
See :class:`DebertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
inputs_embeds (list, optional):
|
|
See :class:`DebertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
labels (Tensor of shape `(batch_size, )`, optional):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import BertForMultipleChoice, BertTokenizer
|
|
from paddlenlp.data import Pad, Dict
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = BertForMultipleChoice.from_pretrained('bert-base-uncased', num_choices=2)
|
|
|
|
data = [
|
|
{
|
|
"question": "how do you turn on an ipad screen?",
|
|
"answer1": "press the volume button.",
|
|
"answer2": "press the lock button.",
|
|
"label": 1,
|
|
},
|
|
{
|
|
"question": "how do you indent something?",
|
|
"answer1": "leave a space before starting the writing",
|
|
"answer2": "press the spacebar",
|
|
"label": 0,
|
|
},
|
|
]
|
|
|
|
text = []
|
|
text_pair = []
|
|
for d in data:
|
|
text.append(d["question"])
|
|
text_pair.append(d["answer1"])
|
|
text.append(d["question"])
|
|
text_pair.append(d["answer2"])
|
|
|
|
inputs = tokenizer(text, text_pair)
|
|
batchify_fn = lambda samples, fn=Dict(
|
|
{
|
|
"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id), # input_ids
|
|
"token_type_ids": Pad(
|
|
axis=0, pad_val=tokenizer.pad_token_type_id
|
|
), # token_type_ids
|
|
}
|
|
): fn(samples)
|
|
inputs = batchify_fn(inputs)
|
|
|
|
reshaped_logits = model(
|
|
input_ids=paddle.to_tensor(inputs[0], dtype="int64"),
|
|
token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"),
|
|
)
|
|
print(reshaped_logits.shape)
|
|
# [2, 2]
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if input_ids is not None:
|
|
num_choices = input_ids.shape[1]
|
|
elif inputs_embeds is not None:
|
|
num_choices = inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.reshape((-1, input_ids.shape[-1])) if input_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.reshape((-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
position_ids = position_ids.reshape((-1, position_ids.shape[-1])) if position_ids is not None else None
|
|
token_type_ids = token_type_ids.reshape((-1, token_type_ids.shape[-1])) if token_type_ids is not None else None
|
|
attention_mask = attention_mask.reshape((-1, attention_mask.shape[-1])) if attention_mask is not None else None
|
|
|
|
outputs = self.deberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = self.pooler(outputs[0])
|
|
pooled_output = self.dropout(pooled_output)
|
|
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.reshape((-1, num_choices))
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|