wehub-resource-sync
1483 lines
57 KiB
Python
1483 lines
57 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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import math
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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_V2_PRETRAINED_INIT_CONFIGURATION,
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DEBERTA_V2_PRETRAINED_RESOURCE_FILES_MAP,
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DebertaV2Config,
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)
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__all__ = [
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"DebertaV2Model",
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"DebertaV2ForSequenceClassification",
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"DebertaV2ForQuestionAnswering",
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"DebertaV2ForTokenClassification",
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"DebertaV2PreTrainedModel",
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"DebertaV2ForMultipleChoice",
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]
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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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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 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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class GELUActivation(nn.Layer):
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"""
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Original Implementation of the GELU activation function in Google BERT repo when initially created. For
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information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
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torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional
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Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
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"""
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def __init__(self, use_gelu_python: bool = False):
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super().__init__()
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self.act = nn.functional.gelu
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def _gelu_python(self, input):
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return input * 0.5 * (1.0 + paddle.erf(input / math.sqrt(2.0)))
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def forward(self, input):
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return self.act(input)
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class DebertaV2Embeddings(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 = nn.LayerNorm(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.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 DebertaV2SelfOutput(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 = nn.LayerNorm(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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class DebertaV2Attention(nn.Layer):
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def __init__(self, config):
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super().__init__()
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self.self = DisentangledSelfAttention(config)
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self.output = DebertaV2SelfOutput(config)
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self.config = config
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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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self_output = self.self(
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hidden_states,
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attention_mask,
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output_attentions,
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query_states=query_states,
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relative_pos=relative_pos,
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rel_embeddings=rel_embeddings,
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)
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if output_attentions:
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self_output, att_matrix = self_output
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if query_states is None:
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query_states = hidden_states
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attention_output = self.output(self_output, query_states)
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if output_attentions:
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return (attention_output, att_matrix)
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else:
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return attention_output
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class DebertaV2Intermediate(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.intermediate_size)
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self.intermediate_act_fn = GELUActivation()
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class DebertaV2Output(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.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(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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class DebertaV2Layer(nn.Layer):
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def __init__(self, config):
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super().__init__()
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self.attention = DebertaV2Attention(config)
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self.intermediate = DebertaV2Intermediate(config)
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self.output = DebertaV2Output(config)
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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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query_states=None,
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relative_pos=None,
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rel_embeddings=None,
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output_attentions=False,
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):
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attention_output = self.attention(
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hidden_states,
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attention_mask,
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output_attentions=output_attentions,
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query_states=query_states,
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relative_pos=relative_pos,
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rel_embeddings=rel_embeddings,
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)
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if output_attentions:
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attention_output, att_matrix = attention_output
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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if output_attentions:
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return (layer_output, att_matrix)
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else:
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return layer_output
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class ConvLayer(nn.Layer):
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def __init__(self, config):
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super().__init__()
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kernel_size = getattr(config, "conv_kernel_size", 3)
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groups = getattr(config, "conv_groups", 1)
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self.conv_act = getattr(config, "conv_act", "tanh")
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self.conv = nn.Conv1D(
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in_channels=config.hidden_size,
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out_channels=config.hidden_size,
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kernel_size=kernel_size,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=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, hidden_states, residual_states, input_mask):
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out = self.conv(hidden_states.transpose([0, 2, 1]))
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out = out.transpose([0, 2, 1])
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rmask = (1 - input_mask).astype(bool)
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mask = rmask.unsqueeze(-1).tile([1, 1, out.shape[2]])
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out = paddle.where(mask, paddle.zeros_like(out), out)
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out = GELUActivation()(self.dropout(out))
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layer_norm_input = residual_states + out
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output = self.LayerNorm(layer_norm_input)
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if input_mask is None:
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output_states = output
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else:
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if input_mask.ndim != layer_norm_input.ndim:
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if input_mask.ndim == 4:
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input_mask = paddle.squeeze(input_mask, [1, 2])
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input_mask = input_mask.unsqueeze(2)
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input_mask = input_mask.astype(output.dtype)
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output_states = output * input_mask
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return output_states
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def make_log_bucket_position(relative_pos, bucket_size, max_position):
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relative_pos = relative_pos.astype("float32")
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sign = paddle.sign(relative_pos)
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mid = bucket_size // 2
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abs_pos = paddle.where(
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(relative_pos < mid) & (relative_pos > -mid),
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paddle.to_tensor(mid - 1).astype(relative_pos.dtype),
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paddle.abs(relative_pos),
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)
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log_pos = (
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paddle.ceil(paddle.log(abs_pos / mid) / paddle.log(paddle.to_tensor((max_position - 1) / mid)) * (mid - 1))
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+ mid
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)
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bucket_pos = paddle.where(abs_pos <= mid, relative_pos.astype(log_pos.dtype), log_pos * sign)
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return bucket_pos
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def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):
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"""
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Build relative position according to the query and key
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We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
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\\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q -
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P_k\\)
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Args:
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query_size (int): the length of query
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key_size (int): the length of key
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bucket_size (int): the size of position bucket
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max_position (int): the maximum allowed absolute position
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Return:
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`paddle.Tensor`: A tensor with shape [1, query_size, key_size]
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"""
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q_ids = paddle.arange(0, query_size, dtype="int64")
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k_ids = paddle.arange(0, key_size, dtype="int64")
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rel_pos_ids = q_ids.unsqueeze(1) - k_ids.unsqueeze(0)
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if bucket_size > 0 and max_position > 0:
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rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)
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rel_pos_ids = rel_pos_ids.astype("int64")
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rel_pos_ids = rel_pos_ids[:query_size, :]
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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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"""
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Disentangled self-attention module
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Parameters:
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config (`DebertaV2Config`):
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A model config class instance with the configuration to build a new model. The schema is similar to
|
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*BertConfig*, for more details, please refer [`DebertaV2Config`]
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"""
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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:
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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_attention_head_size = config.hidden_size // config.num_attention_heads
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self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=True)
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self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=True)
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self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=True)
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self.share_att_key = getattr(config, "share_att_key", False)
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self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
|
|
self.relative_attention = getattr(config, "relative_attention", False)
|
|
|
|
if self.relative_attention:
|
|
self.position_buckets = getattr(config, "position_buckets", -1)
|
|
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.pos_ebd_size = self.max_relative_positions
|
|
if self.position_buckets > 0:
|
|
self.pos_ebd_size = self.position_buckets
|
|
|
|
self.pos_dropout = StableDropout(config.hidden_dropout_prob)
|
|
|
|
if not self.share_att_key:
|
|
if "c2p" in self.pos_att_type:
|
|
self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=True)
|
|
if "p2c" in self.pos_att_type:
|
|
self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = StableDropout(config.attention_probs_dropout_prob)
|
|
|
|
def transpose_for_scores(self, x, attention_heads):
|
|
new_x_shape = x.shape[:-1] + [attention_heads, -1]
|
|
x = x.reshape(new_x_shape)
|
|
return x.transpose(perm=[0, 2, 1, 3]).reshape([-1, x.shape[1], x.shape[-1]])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions=False,
|
|
query_states=None,
|
|
relative_pos=None,
|
|
rel_embeddings=None,
|
|
):
|
|
if query_states is None:
|
|
query_states = hidden_states
|
|
query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads)
|
|
key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads)
|
|
value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads)
|
|
|
|
rel_att = None
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
scale_factor = 1
|
|
if "c2p" in self.pos_att_type:
|
|
scale_factor += 1
|
|
if "p2c" in self.pos_att_type:
|
|
scale_factor += 1
|
|
scale = paddle.sqrt(paddle.to_tensor(query_layer.shape[-1], dtype=paddle.float32) * scale_factor)
|
|
attention_scores = paddle.bmm(query_layer, key_layer.transpose([0, 2, 1])) / scale.astype(
|
|
dtype=query_layer.dtype
|
|
)
|
|
if self.relative_attention:
|
|
rel_embeddings = self.pos_dropout(rel_embeddings)
|
|
rel_att = self.disentangled_attention_bias(
|
|
query_layer, key_layer, relative_pos, rel_embeddings, scale_factor
|
|
)
|
|
|
|
if rel_att is not None:
|
|
attention_scores = attention_scores + rel_att
|
|
attention_scores = attention_scores
|
|
attention_scores = attention_scores.reshape(
|
|
[-1, self.num_attention_heads, attention_scores.shape[-2], attention_scores.shape[-1]]
|
|
)
|
|
|
|
# bsz x height x length x dimension
|
|
attention_probs = softmax_with_mask(attention_scores, attention_mask, -1)
|
|
attention_probs = self.dropout(attention_probs)
|
|
context_layer = paddle.bmm(
|
|
attention_probs.reshape([-1, attention_probs.shape[-2], attention_probs.shape[-1]]), value_layer
|
|
)
|
|
context_layer = context_layer.reshape(
|
|
[-1, self.num_attention_heads, context_layer.shape[-2], context_layer.shape[-1]]
|
|
).transpose([0, 2, 1, 3])
|
|
new_context_layer_shape = context_layer.shape[:-2] + [
|
|
-1,
|
|
]
|
|
context_layer = context_layer.reshape(new_context_layer_shape)
|
|
if output_attentions:
|
|
return (context_layer, attention_probs)
|
|
else:
|
|
return context_layer
|
|
|
|
def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
|
|
if relative_pos is None:
|
|
q = query_layer.shape[-2]
|
|
relative_pos = build_relative_position(
|
|
q,
|
|
key_layer.shape[-2],
|
|
bucket_size=self.position_buckets,
|
|
max_position=self.max_relative_positions,
|
|
)
|
|
if relative_pos.ndim == 2:
|
|
relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
|
|
elif relative_pos.ndim == 3:
|
|
relative_pos = relative_pos.unsqueeze(1)
|
|
# bsz x height x query x key
|
|
elif relative_pos.ndim != 4:
|
|
raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.ndim}")
|
|
|
|
att_span = self.pos_ebd_size
|
|
relative_pos = relative_pos.astype("int64")
|
|
|
|
rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0)
|
|
if self.share_att_key:
|
|
pos_query_layer = self.transpose_for_scores(
|
|
self.query_proj(rel_embeddings), self.num_attention_heads
|
|
).tile([query_layer.shape[0] // self.num_attention_heads, 1, 1])
|
|
pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).tile(
|
|
[query_layer.shape[0] // self.num_attention_heads, 1, 1]
|
|
)
|
|
else:
|
|
if "c2p" in self.pos_att_type:
|
|
pos_key_layer = self.transpose_for_scores(
|
|
self.pos_key_proj(rel_embeddings), self.num_attention_heads
|
|
).tile([query_layer.shape[0] // self.num_attention_heads, 1, 1])
|
|
if "p2c" in self.pos_att_type:
|
|
pos_query_layer = self.transpose_for_scores(
|
|
self.pos_query_proj(rel_embeddings), self.num_attention_heads
|
|
).tile([query_layer.shape[0] // self.num_attention_heads, 1, 1])
|
|
|
|
score = 0
|
|
# content->position
|
|
if "c2p" in self.pos_att_type:
|
|
scale = paddle.sqrt(paddle.to_tensor(pos_key_layer.shape[-1], dtype=paddle.float32) * scale_factor)
|
|
c2p_att = paddle.bmm(query_layer, pos_key_layer.transpose([0, 2, 1]))
|
|
c2p_pos = paddle.clip(relative_pos + att_span, 0, att_span * 2 - 1)
|
|
c2p_att = paddle.take_along_axis(
|
|
c2p_att,
|
|
axis=-1,
|
|
indices=c2p_pos.squeeze(0).expand(
|
|
[query_layer.shape[0], query_layer.shape[1], relative_pos.shape[-1]]
|
|
),
|
|
)
|
|
score += c2p_att / scale.astype(dtype=c2p_att.dtype)
|
|
# position->content
|
|
if "p2c" in self.pos_att_type:
|
|
scale = paddle.sqrt(paddle.to_tensor(pos_query_layer.shape[-1], dtype=paddle.float32) * scale_factor)
|
|
if key_layer.shape[-2] != query_layer.shape[-2]:
|
|
r_pos = build_relative_position(
|
|
key_layer.shape[-2],
|
|
key_layer.shape[-2],
|
|
bucket_size=self.position_buckets,
|
|
max_position=self.max_relative_positions,
|
|
)
|
|
r_pos = r_pos.unsqueeze(0)
|
|
else:
|
|
r_pos = relative_pos
|
|
|
|
p2c_pos = paddle.clip(-r_pos + att_span, 0, att_span * 2 - 1)
|
|
p2c_att = paddle.bmm(key_layer, pos_query_layer.transpose([0, 2, 1]))
|
|
p2c_att = paddle.take_along_axis(
|
|
p2c_att,
|
|
axis=-1,
|
|
indices=p2c_pos.squeeze(0).expand([query_layer.shape[0], key_layer.shape[-2], key_layer.shape[-2]]),
|
|
).transpose([0, 2, 1])
|
|
score += p2c_att / scale.astype(dtype=p2c_att.dtype)
|
|
|
|
return score
|
|
|
|
|
|
class DebertaV2Encoder(nn.Layer):
|
|
"""Modified BertEncoder with relative position bias support"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.layer = nn.LayerList([DebertaV2Layer(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.position_buckets = getattr(config, "position_buckets", -1)
|
|
pos_ebd_size = self.max_relative_positions * 2
|
|
|
|
if self.position_buckets > 0:
|
|
pos_ebd_size = self.position_buckets * 2
|
|
|
|
self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)
|
|
|
|
self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")]
|
|
|
|
if "layer_norm" in self.norm_rel_ebd:
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias_attr=True, weight_attr=True)
|
|
|
|
self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None
|
|
self.gradient_checkpointing = False
|
|
|
|
def get_rel_embedding(self):
|
|
rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
|
|
if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd):
|
|
rel_embeddings = self.LayerNorm(rel_embeddings)
|
|
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(paddle.int8)
|
|
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],
|
|
bucket_size=self.position_buckets,
|
|
max_position=self.max_relative_positions,
|
|
)
|
|
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,
|
|
):
|
|
|
|
if attention_mask.ndim <= 2:
|
|
input_mask = attention_mask
|
|
else:
|
|
input_mask = (attention_mask.sum(-2) > 0).astype(paddle.int8)
|
|
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()
|
|
output_states = next_kv
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (output_states,)
|
|
|
|
output_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:
|
|
output_states, att_m = output_states
|
|
|
|
if i == 0 and self.conv is not None:
|
|
output_states = self.conv(hidden_states, output_states, input_mask)
|
|
|
|
if query_states is not None:
|
|
query_states = output_states
|
|
if isinstance(hidden_states, Sequence):
|
|
next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
|
|
else:
|
|
next_kv = output_states
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (att_m,)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (output_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=output_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
)
|
|
|
|
|
|
class DebertaV2PreTrainedModel(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 = DebertaV2Config
|
|
resource_files_names = {"model_state": "model_state.pdparams"}
|
|
base_model_prefix = "deberta"
|
|
|
|
pretrained_init_configuration = DEBERTA_V2_PRETRAINED_INIT_CONFIGURATION
|
|
pretrained_resource_files_map = DEBERTA_V2_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"],
|
|
["encoder.rel_embeddings.weight", "encoder.rel_embeddings.weight"],
|
|
["encoder.LayerNorm.weight", "encoder.LayerNorm.weight"],
|
|
["encoder.LayerNorm.bias", "encoder.LayerNorm.bias"],
|
|
]
|
|
for layer_index in range(config.num_hidden_layers):
|
|
|
|
layer_mappings = [
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.query_proj.weight",
|
|
f"encoder.layer.{layer_index}.attention.self.query_proj.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.query_proj.bias",
|
|
f"encoder.layer.{layer_index}.attention.self.query_proj.bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.key_proj.weight",
|
|
f"encoder.layer.{layer_index}.attention.self.key_proj.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.key_proj.bias",
|
|
f"encoder.layer.{layer_index}.attention.self.key_proj.bias",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.value_proj.weight",
|
|
f"encoder.layer.{layer_index}.attention.self.value_proj.weight",
|
|
"transpose",
|
|
],
|
|
[
|
|
f"encoder.layer.{layer_index}.attention.self.value_proj.bias",
|
|
f"encoder.layer.{layer_index}.attention.self.value_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-tiny-model-private/tiny-random-DebertaV2Model
|
|
if config.architectures is not None and "DebertaV2Model" in config.architectures:
|
|
pass
|
|
else:
|
|
for mapping in model_mappings:
|
|
mapping[0] = "deberta." + mapping[0]
|
|
mapping[1] = "deberta." + mapping[1]
|
|
if config.architectures is not None and "DebertaV2ForQuestionAnswering" in config.architectures:
|
|
model_mappings.extend(
|
|
[["qa_outputs.weight", "qa_outputs.weight", "transpose"], ["qa_outputs.bias", "qa_outputs.bias"]]
|
|
)
|
|
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 DebertaV2Model(DebertaV2PreTrainedModel):
|
|
def __init__(self, config: DebertaV2Config):
|
|
super(DebertaV2Model, self).__init__(config)
|
|
self.config = config
|
|
self.embeddings = DebertaV2Embeddings(config)
|
|
self.encoder = DebertaV2Encoder(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 DebertaV2PredictionHeadTransform(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 DebertaV2LMPredictionHead(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = DebertaV2PredictionHeadTransform(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 DebertaV2OnlyMLMHead(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = DebertaV2LMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.deberta = DebertaV2Model(config)
|
|
self.cls = DebertaV2OnlyMLMHead(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 DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.deberta = DebertaV2Model(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 DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.deberta = DebertaV2Model(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 DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.deberta = DebertaV2Model(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 DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
|
|
|
|
"""
|
|
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: DebertaV2Config):
|
|
super(DebertaV2ForMultipleChoice, self).__init__(config)
|
|
self.deberta = DebertaV2Model(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,
|
|
)
|