1720 lines
79 KiB
Python
1720 lines
79 KiB
Python
# coding=utf-8
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# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
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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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"""PyTorch Flaubert model, based on XLM."""
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import itertools
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import math
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from dataclasses import dataclass
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from typing import Callable, Optional, Union
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import gelu, get_activation
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from ...cache_utils import Cache, EncoderDecoderCache
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from ...generation import GenerationMixin
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from ...modeling_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 ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import ModelOutput, auto_docstring, logging
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from .configuration_flaubert import FlaubertConfig
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logger = logging.get_logger(__name__)
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# Copied from transformers.models.xlm.modeling_xlm.create_sinusoidal_embeddings
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def create_sinusoidal_embeddings(n_pos, dim, out):
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position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
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out.requires_grad = False
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out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
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out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
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out.detach_()
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# Copied from transformers.models.xlm.modeling_xlm.get_masks
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def get_masks(slen, lengths, causal, padding_mask=None):
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"""
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Generate hidden states mask, and optionally an attention mask.
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"""
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alen = torch.arange(slen, dtype=torch.long, device=lengths.device)
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if padding_mask is not None:
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mask = padding_mask
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else:
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assert lengths.max().item() <= slen
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mask = alen < lengths[:, None]
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# attention mask is the same as mask, or triangular inferior attention (causal)
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bs = lengths.size(0)
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if causal:
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attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None]
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else:
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attn_mask = mask
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# sanity check
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assert mask.size() == (bs, slen)
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assert causal is False or attn_mask.size() == (bs, slen, slen)
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return mask, attn_mask
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# Copied from transformers.models.xlm.modeling_xlm.MultiHeadAttention
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class MultiHeadAttention(nn.Module):
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NEW_ID = itertools.count()
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def __init__(self, n_heads, dim, config):
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super().__init__()
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self.layer_id = next(MultiHeadAttention.NEW_ID)
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self.dim = dim
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self.n_heads = n_heads
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self.head_dim = dim // n_heads
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self.dropout = config.attention_dropout
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assert self.dim % self.n_heads == 0
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self.q_lin = nn.Linear(dim, dim)
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self.k_lin = nn.Linear(dim, dim)
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self.v_lin = nn.Linear(dim, dim)
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self.out_lin = nn.Linear(dim, dim)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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attention_head_size = self.dim // self.n_heads
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, attention_head_size, self.pruned_heads)
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# Prune linear layers
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self.q_lin = prune_linear_layer(self.q_lin, index)
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self.k_lin = prune_linear_layer(self.k_lin, index)
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self.v_lin = prune_linear_layer(self.v_lin, index)
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self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
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# Update hyper params
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self.n_heads = self.n_heads - len(heads)
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self.dim = attention_head_size * self.n_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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input,
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mask,
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kv=None,
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cache=None,
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head_mask=None,
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output_attentions=False,
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cache_position=None,
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):
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"""
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Self-attention (if kv is None) or attention over source sentence (provided by kv).
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"""
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# Input is (bs, qlen, dim)
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# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
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bs, qlen, dim = input.size()
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is_cross_attention = kv is not None
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mask_reshape = (bs, 1, qlen, -1) if mask.dim() == 3 else (bs, 1, 1, -1)
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q = self.q_lin(input).view(bs, -1, self.n_heads, self.head_dim).transpose(1, 2)
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if cache is not None:
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if isinstance(cache, EncoderDecoderCache):
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is_updated = cache.is_updated.get(self.layer_id)
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if is_cross_attention:
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# after the first generated id, we can subsequently re-use all key/value_states from cache
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curr_past_key_value = cache.cross_attention_cache
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else:
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curr_past_key_value = cache.self_attention_cache
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else:
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curr_past_key_value = cache
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current_states = kv if is_cross_attention else input
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if is_cross_attention and cache is not None and is_updated:
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# reuse k,v, cross_attentions
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k = curr_past_key_value.key_cache[self.layer_id]
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v = curr_past_key_value.value_cache[self.layer_id]
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else:
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k = self.k_lin(current_states)
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v = self.v_lin(current_states)
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k = k.view(bs, -1, self.n_heads, self.head_dim).transpose(1, 2)
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v = v.view(bs, -1, self.n_heads, self.head_dim).transpose(1, 2)
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if cache is not None:
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# save all key/value_states to cache to be re-used for fast auto-regressive generation
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cache_position = cache_position if not is_cross_attention else None
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k, v = curr_past_key_value.update(k, v, self.layer_id, {"cache_position": cache_position})
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# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
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if is_cross_attention:
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cache.is_updated[self.layer_id] = True
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q = q / math.sqrt(self.head_dim) # (bs, n_heads, qlen, head_dim)
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scores = torch.matmul(q, k.transpose(2, 3)) # (bs, n_heads, qlen, klen)
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mask = (mask == 0).view(mask_reshape).expand_as(scores) # (bs, n_heads, qlen, klen)
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scores.masked_fill_(mask, torch.finfo(scores.dtype).min) # (bs, n_heads, qlen, klen)
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weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen)
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weights = nn.functional.dropout(weights, p=self.dropout, training=self.training) # (bs, n_heads, qlen, klen)
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# Mask heads if we want to
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if head_mask is not None:
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weights = weights * head_mask
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context = torch.matmul(weights, v) # (bs, n_heads, qlen, head_dim)
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context = context.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.head_dim)
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outputs = (self.out_lin(context),)
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if output_attentions:
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outputs = outputs + (weights,)
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return outputs
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# Copied from transformers.models.xlm.modeling_xlm.TransformerFFN
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class TransformerFFN(nn.Module):
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def __init__(self, in_dim, dim_hidden, out_dim, config):
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super().__init__()
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self.dropout = config.dropout
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self.lin1 = nn.Linear(in_dim, dim_hidden)
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self.lin2 = nn.Linear(dim_hidden, out_dim)
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self.act = gelu if config.gelu_activation else nn.functional.relu
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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def forward(self, input):
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return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)
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def ff_chunk(self, input):
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x = self.lin1(input)
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x = self.act(x)
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x = self.lin2(x)
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x = nn.functional.dropout(x, p=self.dropout, training=self.training)
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return x
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@auto_docstring(
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custom_intro="""
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The bare Flaubert Model transformer outputting raw hidden-states without any specific head on top.
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"""
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)
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# Copied from transformers.models.xlm.modeling_xlm.XLMPredLayer with XLM->Flaubert
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class FlaubertPredLayer(nn.Module):
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"""
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Prediction layer (cross_entropy or adaptive_softmax).
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"""
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def __init__(self, config):
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super().__init__()
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self.asm = config.asm
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self.n_words = config.n_words
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self.pad_index = config.pad_index
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dim = config.emb_dim
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if config.asm is False:
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self.proj = nn.Linear(dim, config.n_words, bias=True)
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else:
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self.proj = nn.AdaptiveLogSoftmaxWithLoss(
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in_features=dim,
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n_classes=config.n_words,
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cutoffs=config.asm_cutoffs,
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div_value=config.asm_div_value,
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head_bias=True, # default is False
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)
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def forward(self, x, y=None):
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"""Compute the loss, and optionally the scores."""
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outputs = ()
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if self.asm is False:
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scores = self.proj(x)
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outputs = (scores,) + outputs
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if y is not None:
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loss = nn.functional.cross_entropy(scores.view(-1, self.n_words), y.view(-1), reduction="mean")
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outputs = (loss,) + outputs
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else:
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scores = self.proj.log_prob(x)
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outputs = (scores,) + outputs
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if y is not None:
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_, loss = self.proj(x, y)
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outputs = (loss,) + outputs
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return outputs
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for outputs of question answering models using a [`~modeling_utils.FlaubertSQuADHead`].
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"""
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)
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# Copied from transformers.models.xlm.modeling_xlm.XLMSquadHeadOutput with XLM->Flaubert
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class FlaubertSquadHeadOutput(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
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Classification loss as the sum of start token, end token (and is_impossible if provided) classification
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losses.
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start_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
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Log probabilities for the top config.start_n_top start token possibilities (beam-search).
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start_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
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Indices for the top config.start_n_top start token possibilities (beam-search).
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end_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
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Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
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(beam-search).
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end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
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Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search).
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cls_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
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Log probabilities for the `is_impossible` label of the answers.
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"""
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loss: Optional[torch.FloatTensor] = None
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start_top_log_probs: Optional[torch.FloatTensor] = None
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start_top_index: Optional[torch.LongTensor] = None
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end_top_log_probs: Optional[torch.FloatTensor] = None
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end_top_index: Optional[torch.LongTensor] = None
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cls_logits: Optional[torch.FloatTensor] = None
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# Copied from transformers.models.xlm.modeling_xlm.XLMPoolerStartLogits with XLM->Flaubert
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class FlaubertPoolerStartLogits(nn.Module):
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"""
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Compute SQuAD start logits from sequence hidden states.
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Args:
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config ([`FlaubertConfig`]):
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The config used by the model, will be used to grab the `hidden_size` of the model.
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"""
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def __init__(self, config: FlaubertConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, 1)
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def forward(
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self, hidden_states: torch.FloatTensor, p_mask: Optional[torch.FloatTensor] = None
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) -> torch.FloatTensor:
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"""
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Args:
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hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
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The final hidden states of the model.
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p_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *optional*):
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
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should be masked.
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Returns:
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`torch.FloatTensor`: The start logits for SQuAD.
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"""
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x = self.dense(hidden_states).squeeze(-1)
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if p_mask is not None:
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if p_mask.dtype == torch.float16:
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x = x * (1 - p_mask) - 65500 * p_mask
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else:
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x = x * (1 - p_mask) - 1e30 * p_mask
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return x
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# Copied from transformers.models.xlm.modeling_xlm.XLMPoolerEndLogits with XLM->Flaubert
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class FlaubertPoolerEndLogits(nn.Module):
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"""
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Compute SQuAD end logits from sequence hidden states.
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Args:
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config ([`FlaubertConfig`]):
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The config used by the model, will be used to grab the `hidden_size` of the model and the `layer_norm_eps`
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to use.
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"""
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def __init__(self, config: FlaubertConfig):
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super().__init__()
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self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
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self.activation = nn.Tanh()
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dense_1 = nn.Linear(config.hidden_size, 1)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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start_states: Optional[torch.FloatTensor] = None,
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start_positions: Optional[torch.LongTensor] = None,
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p_mask: Optional[torch.FloatTensor] = None,
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) -> torch.FloatTensor:
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"""
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Args:
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hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
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The final hidden states of the model.
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start_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
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The hidden states of the first tokens for the labeled span.
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start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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The position of the first token for the labeled span.
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p_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *optional*):
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
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should be masked.
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<Tip>
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One of `start_states` or `start_positions` should be not `None`. If both are set, `start_positions` overrides
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`start_states`.
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</Tip>
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Returns:
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`torch.FloatTensor`: The end logits for SQuAD.
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"""
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assert start_states is not None or start_positions is not None, (
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"One of start_states, start_positions should be not None"
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)
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if start_positions is not None:
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slen, hsz = hidden_states.shape[-2:]
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start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
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start_states = hidden_states.gather(-2, start_positions) # shape (bsz, 1, hsz)
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start_states = start_states.expand(-1, slen, -1) # shape (bsz, slen, hsz)
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x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
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x = self.activation(x)
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x = self.LayerNorm(x)
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x = self.dense_1(x).squeeze(-1)
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if p_mask is not None:
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if p_mask.dtype == torch.float16:
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x = x * (1 - p_mask) - 65500 * p_mask
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else:
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x = x * (1 - p_mask) - 1e30 * p_mask
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return x
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# Copied from transformers.models.xlm.modeling_xlm.XLMPoolerAnswerClass with XLM->Flaubert
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class FlaubertPoolerAnswerClass(nn.Module):
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"""
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Compute SQuAD 2.0 answer class from classification and start tokens hidden states.
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Args:
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config ([`FlaubertConfig`]):
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The config used by the model, will be used to grab the `hidden_size` of the model.
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"""
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def __init__(self, config: FlaubertConfig):
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super().__init__()
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self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
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self.activation = nn.Tanh()
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self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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start_states: Optional[torch.FloatTensor] = None,
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start_positions: Optional[torch.LongTensor] = None,
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cls_index: Optional[torch.LongTensor] = None,
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) -> torch.FloatTensor:
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"""
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Args:
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hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
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The final hidden states of the model.
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start_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
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The hidden states of the first tokens for the labeled span.
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start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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The position of the first token for the labeled span.
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cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Position of the CLS token for each sentence in the batch. If `None`, takes the last token.
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<Tip>
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|
One of `start_states` or `start_positions` should be not `None`. If both are set, `start_positions` overrides
|
|
`start_states`.
|
|
|
|
</Tip>
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: The SQuAD 2.0 answer class.
|
|
"""
|
|
# No dependency on end_feature so that we can obtain one single `cls_logits` for each sample.
|
|
hsz = hidden_states.shape[-1]
|
|
assert start_states is not None or start_positions is not None, (
|
|
"One of start_states, start_positions should be not None"
|
|
)
|
|
if start_positions is not None:
|
|
start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
|
|
start_states = hidden_states.gather(-2, start_positions).squeeze(-2) # shape (bsz, hsz)
|
|
|
|
if cls_index is not None:
|
|
cls_index = cls_index[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
|
|
cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, hsz)
|
|
else:
|
|
cls_token_state = hidden_states[:, -1, :] # shape (bsz, hsz)
|
|
|
|
x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))
|
|
x = self.activation(x)
|
|
x = self.dense_1(x).squeeze(-1)
|
|
|
|
return x
|
|
|
|
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMSQuADHead with XLM->Flaubert
|
|
class FlaubertSQuADHead(nn.Module):
|
|
r"""
|
|
A SQuAD head inspired by XLNet.
|
|
|
|
Args:
|
|
config ([`FlaubertConfig`]):
|
|
The config used by the model, will be used to grab the `hidden_size` of the model and the `layer_norm_eps`
|
|
to use.
|
|
"""
|
|
|
|
def __init__(self, config: FlaubertConfig):
|
|
super().__init__()
|
|
self.start_n_top = config.start_n_top
|
|
self.end_n_top = config.end_n_top
|
|
|
|
self.start_logits = FlaubertPoolerStartLogits(config)
|
|
self.end_logits = FlaubertPoolerEndLogits(config)
|
|
self.answer_class = FlaubertPoolerAnswerClass(config)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
cls_index: Optional[torch.LongTensor] = None,
|
|
is_impossible: Optional[torch.LongTensor] = None,
|
|
p_mask: Optional[torch.FloatTensor] = None,
|
|
return_dict: bool = False,
|
|
) -> Union[FlaubertSquadHeadOutput, tuple[torch.FloatTensor]]:
|
|
r"""
|
|
hidden_states (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`):
|
|
Final hidden states of the model on the sequence tokens.
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Positions of the first token for the labeled span.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Positions of the last token for the labeled span.
|
|
cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Position of the CLS token for each sentence in the batch. If `None`, takes the last token.
|
|
is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Whether the question has a possible answer in the paragraph or not.
|
|
p_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *optional*):
|
|
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
|
|
should be masked.
|
|
"""
|
|
start_logits = self.start_logits(hidden_states, p_mask=p_mask)
|
|
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, let's remove the dimension added by batch splitting
|
|
for x in (start_positions, end_positions, cls_index, is_impossible):
|
|
if x is not None and x.dim() > 1:
|
|
x.squeeze_(-1)
|
|
|
|
# during training, compute the end logits based on the ground truth of the start position
|
|
end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if cls_index is not None and is_impossible is not None:
|
|
# Predict answerability from the representation of CLS and START
|
|
cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
|
|
loss_fct_cls = nn.BCEWithLogitsLoss()
|
|
cls_loss = loss_fct_cls(cls_logits, is_impossible)
|
|
|
|
# note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
|
|
total_loss += cls_loss * 0.5
|
|
|
|
return FlaubertSquadHeadOutput(loss=total_loss) if return_dict else (total_loss,)
|
|
|
|
else:
|
|
# during inference, compute the end logits based on beam search
|
|
bsz, slen, hsz = hidden_states.size()
|
|
start_log_probs = nn.functional.softmax(start_logits, dim=-1) # shape (bsz, slen)
|
|
|
|
start_top_log_probs, start_top_index = torch.topk(
|
|
start_log_probs, self.start_n_top, dim=-1
|
|
) # shape (bsz, start_n_top)
|
|
start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
|
|
start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
|
|
start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
|
|
|
|
hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(
|
|
start_states
|
|
) # shape (bsz, slen, start_n_top, hsz)
|
|
p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
|
|
end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
|
|
end_log_probs = nn.functional.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)
|
|
|
|
end_top_log_probs, end_top_index = torch.topk(
|
|
end_log_probs, self.end_n_top, dim=1
|
|
) # shape (bsz, end_n_top, start_n_top)
|
|
end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
|
|
end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
|
|
|
|
start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
|
|
cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
|
|
|
|
if not return_dict:
|
|
return (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits)
|
|
else:
|
|
return FlaubertSquadHeadOutput(
|
|
start_top_log_probs=start_top_log_probs,
|
|
start_top_index=start_top_index,
|
|
end_top_log_probs=end_top_log_probs,
|
|
end_top_index=end_top_index,
|
|
cls_logits=cls_logits,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->Flaubert
|
|
class FlaubertSequenceSummary(nn.Module):
|
|
r"""
|
|
Compute a single vector summary of a sequence hidden states.
|
|
|
|
Args:
|
|
config ([`FlaubertConfig`]):
|
|
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
|
|
config class of your model for the default values it uses):
|
|
|
|
- **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:
|
|
|
|
- `"last"` -- Take the last token hidden state (like XLNet)
|
|
- `"first"` -- Take the first token hidden state (like Bert)
|
|
- `"mean"` -- Take the mean of all tokens hidden states
|
|
- `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
|
|
- `"attn"` -- Not implemented now, use multi-head attention
|
|
|
|
- **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
|
|
- **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
|
|
(otherwise to `config.hidden_size`).
|
|
- **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
|
|
another string or `None` will add no activation.
|
|
- **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
|
|
- **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
|
|
"""
|
|
|
|
def __init__(self, config: FlaubertConfig):
|
|
super().__init__()
|
|
|
|
self.summary_type = getattr(config, "summary_type", "last")
|
|
if self.summary_type == "attn":
|
|
# We should use a standard multi-head attention module with absolute positional embedding for that.
|
|
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
|
|
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
|
|
raise NotImplementedError
|
|
|
|
self.summary = nn.Identity()
|
|
if hasattr(config, "summary_use_proj") and config.summary_use_proj:
|
|
if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
|
|
num_classes = config.num_labels
|
|
else:
|
|
num_classes = config.hidden_size
|
|
self.summary = nn.Linear(config.hidden_size, num_classes)
|
|
|
|
activation_string = getattr(config, "summary_activation", None)
|
|
self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity()
|
|
|
|
self.first_dropout = nn.Identity()
|
|
if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
|
|
self.first_dropout = nn.Dropout(config.summary_first_dropout)
|
|
|
|
self.last_dropout = nn.Identity()
|
|
if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
|
|
self.last_dropout = nn.Dropout(config.summary_last_dropout)
|
|
|
|
def forward(
|
|
self, hidden_states: torch.FloatTensor, cls_index: Optional[torch.LongTensor] = None
|
|
) -> torch.FloatTensor:
|
|
"""
|
|
Compute a single vector summary of a sequence hidden states.
|
|
|
|
Args:
|
|
hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
|
|
The hidden states of the last layer.
|
|
cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
|
|
Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: The summary of the sequence hidden states.
|
|
"""
|
|
if self.summary_type == "last":
|
|
output = hidden_states[:, -1]
|
|
elif self.summary_type == "first":
|
|
output = hidden_states[:, 0]
|
|
elif self.summary_type == "mean":
|
|
output = hidden_states.mean(dim=1)
|
|
elif self.summary_type == "cls_index":
|
|
if cls_index is None:
|
|
cls_index = torch.full_like(
|
|
hidden_states[..., :1, :],
|
|
hidden_states.shape[-2] - 1,
|
|
dtype=torch.long,
|
|
)
|
|
else:
|
|
cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
|
|
cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
|
|
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
|
|
output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
|
|
elif self.summary_type == "attn":
|
|
raise NotImplementedError
|
|
|
|
output = self.first_dropout(output)
|
|
output = self.summary(output)
|
|
output = self.activation(output)
|
|
output = self.last_dropout(output)
|
|
|
|
return output
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMPreTrainedModel with XLM->Flaubert
|
|
class FlaubertPreTrainedModel(PreTrainedModel):
|
|
config: FlaubertConfig
|
|
load_tf_weights = None
|
|
base_model_prefix = "transformer"
|
|
|
|
def __init__(self, *inputs, **kwargs):
|
|
super().__init__(*inputs, **kwargs)
|
|
|
|
@property
|
|
def dummy_inputs(self):
|
|
inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
|
|
attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
|
|
if self.config.use_lang_emb and self.config.n_langs > 1:
|
|
langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
|
|
else:
|
|
langs_list = None
|
|
return {"input_ids": inputs_list, "attention_mask": attns_list, "langs": langs_list}
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights."""
|
|
if isinstance(module, nn.Embedding):
|
|
if self.config is not None and self.config.embed_init_std is not None:
|
|
nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
if isinstance(module, nn.Linear):
|
|
if self.config is not None and self.config.init_std is not None:
|
|
nn.init.normal_(module.weight, mean=0, std=self.config.init_std)
|
|
if module.bias is not None:
|
|
nn.init.constant_(module.bias, 0.0)
|
|
if isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, FlaubertModel) and self.config.sinusoidal_embeddings:
|
|
create_sinusoidal_embeddings(
|
|
self.config.max_position_embeddings, self.config.emb_dim, out=module.position_embeddings.weight
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class FlaubertModel(FlaubertPreTrainedModel):
|
|
def __init__(self, config): # , dico, is_encoder, with_output):
|
|
super().__init__(config)
|
|
|
|
# encoder / decoder, output layer
|
|
self.is_encoder = config.is_encoder
|
|
self.is_decoder = not config.is_encoder
|
|
if self.is_decoder:
|
|
raise NotImplementedError("Currently Flaubert can only be used as an encoder")
|
|
# self.with_output = with_output
|
|
self.causal = config.causal
|
|
|
|
# dictionary / languages
|
|
self.n_langs = config.n_langs
|
|
self.use_lang_emb = config.use_lang_emb
|
|
self.n_words = config.n_words
|
|
self.eos_index = config.eos_index
|
|
self.pad_index = config.pad_index
|
|
# self.dico = dico
|
|
# self.id2lang = config.id2lang
|
|
# self.lang2id = config.lang2id
|
|
# assert len(self.dico) == self.n_words
|
|
# assert len(self.id2lang) == len(self.lang2id) == self.n_langs
|
|
|
|
# model parameters
|
|
self.dim = config.emb_dim # 512 by default
|
|
self.hidden_dim = self.dim * 4 # 2048 by default
|
|
self.n_heads = config.n_heads # 8 by default
|
|
self.n_layers = config.n_layers
|
|
self.dropout = config.dropout
|
|
self.attention_dropout = config.attention_dropout
|
|
assert self.dim % self.n_heads == 0, "transformer dim must be a multiple of n_heads"
|
|
|
|
# embeddings
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
|
|
if config.n_langs > 1 and config.use_lang_emb:
|
|
self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
|
|
self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
|
|
self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)
|
|
|
|
# transformer layers
|
|
self.attentions = nn.ModuleList()
|
|
self.layer_norm1 = nn.ModuleList()
|
|
self.ffns = nn.ModuleList()
|
|
self.layer_norm2 = nn.ModuleList()
|
|
# if self.is_decoder:
|
|
# self.layer_norm15 = nn.ModuleList()
|
|
# self.encoder_attn = nn.ModuleList()
|
|
|
|
for _ in range(self.n_layers):
|
|
self.attentions.append(MultiHeadAttention(self.n_heads, self.dim, config=config))
|
|
self.layer_norm1.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
|
|
# if self.is_decoder:
|
|
# self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
|
|
# self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
|
|
self.ffns.append(TransformerFFN(self.dim, self.hidden_dim, self.dim, config=config))
|
|
self.layer_norm2.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
|
|
|
|
if hasattr(config, "pruned_heads"):
|
|
pruned_heads = config.pruned_heads.copy().items()
|
|
config.pruned_heads = {}
|
|
for layer, heads in pruned_heads:
|
|
if self.attentions[int(layer)].n_heads == config.n_heads:
|
|
self.prune_heads({int(layer): list(map(int, heads))})
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
self.layerdrop = getattr(config, "layerdrop", 0.0)
|
|
self.pre_norm = getattr(config, "pre_norm", False)
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
|
)
|
|
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMModel.get_input_embeddings
|
|
def get_input_embeddings(self):
|
|
return self.embeddings
|
|
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMModel.set_input_embeddings
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.embeddings = new_embeddings
|
|
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMModel._prune_heads
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.attentions[layer].prune_heads(heads)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
lengths: Optional[torch.LongTensor] = None,
|
|
cache: Optional[dict[str, torch.FloatTensor]] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
r"""
|
|
langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use `attention_mask` for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`:
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary strings to `torch.FloatTensor` that contains precomputed hidden-states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding. The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
"""
|
|
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
|
|
|
|
# removed: src_enc=None, src_len=None
|
|
if input_ids is not None:
|
|
bs, slen = input_ids.size()
|
|
else:
|
|
bs, slen = inputs_embeds.size()[:-1]
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if not isinstance(cache, Cache):
|
|
cache = EncoderDecoderCache.from_legacy_cache(cache)
|
|
|
|
if lengths is None:
|
|
if input_ids is not None:
|
|
lengths = (input_ids != self.pad_index).sum(dim=1).long()
|
|
else:
|
|
lengths = torch.tensor([slen] * bs, device=device)
|
|
# mask = input_ids != self.pad_index
|
|
|
|
# check inputs
|
|
assert lengths.size(0) == bs
|
|
assert lengths.max().item() <= slen
|
|
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
|
|
# assert (src_enc is None) == (src_len is None)
|
|
# if src_enc is not None:
|
|
# assert self.is_decoder
|
|
# assert src_enc.size(0) == bs
|
|
|
|
# generate masks
|
|
mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
|
|
# if self.is_decoder and src_enc is not None:
|
|
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
|
|
|
|
# Setting the position-ids to the registered buffer in constructor, it helps
|
|
# when tracing the model without passing position-ids, solves
|
|
# issues similar to issue #5664
|
|
if position_ids is None:
|
|
if hasattr(self, "position_ids"):
|
|
position_ids = self.position_ids[:, :slen]
|
|
position_ids = position_ids.expand((bs, slen))
|
|
else:
|
|
position_ids = torch.arange(slen, dtype=torch.long, device=device)
|
|
position_ids = position_ids.unsqueeze(0).expand((bs, slen))
|
|
else:
|
|
assert position_ids.size() == (bs, slen) # (slen, bs)
|
|
# position_ids = position_ids.transpose(0, 1)
|
|
|
|
# langs
|
|
if langs is not None:
|
|
assert langs.size() == (bs, slen) # (slen, bs)
|
|
# langs = langs.transpose(0, 1)
|
|
|
|
# Prepare head mask if needed
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layers)
|
|
|
|
# do not recompute cached elements
|
|
if cache is not None and input_ids is not None:
|
|
_slen = slen - cache.get_seq_length()
|
|
input_ids = input_ids[:, -_slen:]
|
|
position_ids = position_ids[:, -_slen:]
|
|
if langs is not None:
|
|
langs = langs[:, -_slen:]
|
|
mask = mask[:, -_slen:]
|
|
attn_mask = attn_mask[:, -_slen:]
|
|
|
|
# embeddings
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embeddings(input_ids)
|
|
|
|
tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
|
|
if langs is not None and self.use_lang_emb and self.config.n_langs > 1:
|
|
tensor = tensor + self.lang_embeddings(langs)
|
|
if token_type_ids is not None:
|
|
tensor = tensor + self.embeddings(token_type_ids)
|
|
tensor = self.layer_norm_emb(tensor)
|
|
tensor = nn.functional.dropout(tensor, p=self.dropout, training=self.training)
|
|
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
|
|
|
|
# transformer layers
|
|
hidden_states = () if output_hidden_states else None
|
|
attentions = () if output_attentions else None
|
|
for i in range(self.n_layers):
|
|
# LayerDrop
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if dropout_probability < self.layerdrop:
|
|
continue
|
|
|
|
if output_hidden_states:
|
|
hidden_states = hidden_states + (tensor,)
|
|
|
|
# self attention
|
|
if not self.pre_norm:
|
|
attn_outputs = self.attentions[i](
|
|
tensor,
|
|
attn_mask,
|
|
cache=cache,
|
|
head_mask=head_mask[i],
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
attn = attn_outputs[0]
|
|
if output_attentions:
|
|
attentions = attentions + (attn_outputs[1],)
|
|
attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
|
|
tensor = tensor + attn
|
|
tensor = self.layer_norm1[i](tensor)
|
|
else:
|
|
tensor_normalized = self.layer_norm1[i](tensor)
|
|
attn_outputs = self.attentions[i](tensor_normalized, attn_mask, cache=cache, head_mask=head_mask[i])
|
|
attn = attn_outputs[0]
|
|
if output_attentions:
|
|
attentions = attentions + (attn_outputs[1],)
|
|
attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
|
|
tensor = tensor + attn
|
|
|
|
# FFN
|
|
if not self.pre_norm:
|
|
tensor = tensor + self.ffns[i](tensor)
|
|
tensor = self.layer_norm2[i](tensor)
|
|
else:
|
|
tensor_normalized = self.layer_norm2[i](tensor)
|
|
tensor = tensor + self.ffns[i](tensor_normalized)
|
|
|
|
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
|
|
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
hidden_states = hidden_states + (tensor,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [tensor, hidden_states, attentions] if v is not None)
|
|
|
|
return BaseModelOutput(last_hidden_state=tensor, hidden_states=hidden_states, attentions=attentions)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Flaubert Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
"""
|
|
)
|
|
class FlaubertWithLMHeadModel(FlaubertPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["pred_layer.proj.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = FlaubertModel(config)
|
|
self.pred_layer = FlaubertPredLayer(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.pred_layer.proj
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.pred_layer.proj = new_embeddings
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, **kwargs):
|
|
# Overwritten -- uses a language id
|
|
|
|
mask_token_id = self.config.mask_token_id
|
|
lang_id = self.config.lang_id
|
|
|
|
effective_batch_size = input_ids.shape[0]
|
|
mask_token = torch.full((effective_batch_size, 1), mask_token_id, dtype=torch.long, device=input_ids.device)
|
|
input_ids = torch.cat([input_ids, mask_token], dim=1)
|
|
if lang_id is not None:
|
|
langs = torch.full_like(input_ids, lang_id)
|
|
else:
|
|
langs = None
|
|
return {"input_ids": input_ids, "langs": langs}
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
lengths: Optional[torch.Tensor] = None,
|
|
cache: Optional[dict[str, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, MaskedLMOutput]:
|
|
r"""
|
|
langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use `attention_mask` for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`:
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary strings to `torch.FloatTensor` that contains precomputed hidden-states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding. The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
langs=langs,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
lengths=lengths,
|
|
cache=cache,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
output = transformer_outputs[0]
|
|
outputs = self.pred_layer(output, labels) # (loss, logits) or (logits,) depending on if labels are provided.
|
|
|
|
if not return_dict:
|
|
return outputs + transformer_outputs[1:]
|
|
|
|
return MaskedLMOutput(
|
|
loss=outputs[0] if labels is not None else None,
|
|
logits=outputs[0] if labels is None else outputs[1],
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Flaubert Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
|
|
e.g. for GLUE tasks.
|
|
"""
|
|
)
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMForSequenceClassification with XLM_INPUTS->FLAUBERT_INPUTS,XLM->Flaubert
|
|
class FlaubertForSequenceClassification(FlaubertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.transformer = FlaubertModel(config)
|
|
self.sequence_summary = FlaubertSequenceSummary(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
lengths: Optional[torch.Tensor] = None,
|
|
cache: Optional[dict[str, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, SequenceClassifierOutput]:
|
|
r"""
|
|
langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`.
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding.
|
|
|
|
The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
langs=langs,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
lengths=lengths,
|
|
cache=cache,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
output = transformer_outputs[0]
|
|
logits = self.sequence_summary(output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMForTokenClassification with XLM_INPUTS->FLAUBERT_INPUTS,XLM->Flaubert
|
|
class FlaubertForTokenClassification(FlaubertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = FlaubertModel(config)
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
lengths: Optional[torch.Tensor] = None,
|
|
cache: Optional[dict[str, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, TokenClassifierOutput]:
|
|
r"""
|
|
langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`.
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding.
|
|
|
|
The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
langs=langs,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
lengths=lengths,
|
|
cache=cache,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
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 = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Flaubert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
|
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
"""
|
|
)
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringSimple with XLM_INPUTS->FLAUBERT_INPUTS,XLM->Flaubert
|
|
class FlaubertForQuestionAnsweringSimple(FlaubertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.transformer = FlaubertModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
lengths: Optional[torch.Tensor] = None,
|
|
cache: Optional[dict[str, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`.
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding.
|
|
|
|
The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
langs=langs,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
lengths=lengths,
|
|
cache=cache,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = transformer_outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
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 len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 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.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = 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) + transformer_outputs[1:]
|
|
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=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Base class for outputs of question answering models using a `SquadHead`.
|
|
"""
|
|
)
|
|
# Copied from transformer.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput with XLM->Flaubert
|
|
class FlaubertForQuestionAnsweringOutput(ModelOutput):
|
|
r"""
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
|
|
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
|
|
losses.
|
|
start_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
|
|
Log probabilities for the top config.start_n_top start token possibilities (beam-search).
|
|
start_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
|
|
Indices for the top config.start_n_top start token possibilities (beam-search).
|
|
end_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
|
|
Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
|
|
(beam-search).
|
|
end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
|
|
Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search).
|
|
cls_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
|
|
Log probabilities for the `is_impossible` label of the answers.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
start_top_log_probs: Optional[torch.FloatTensor] = None
|
|
start_top_index: Optional[torch.LongTensor] = None
|
|
end_top_log_probs: Optional[torch.FloatTensor] = None
|
|
end_top_index: Optional[torch.LongTensor] = None
|
|
cls_logits: Optional[torch.FloatTensor] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMForQuestionAnswering with XLM_INPUTS->FLAUBERT_INPUTS,XLM->Flaubert
|
|
class FlaubertForQuestionAnswering(FlaubertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.transformer = FlaubertModel(config)
|
|
self.qa_outputs = FlaubertSQuADHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
lengths: Optional[torch.Tensor] = None,
|
|
cache: Optional[dict[str, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
is_impossible: Optional[torch.Tensor] = None,
|
|
cls_index: Optional[torch.Tensor] = None,
|
|
p_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, FlaubertForQuestionAnsweringOutput]:
|
|
r"""
|
|
langs (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`.
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding.
|
|
|
|
The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels whether a question has an answer or no answer (SQuAD 2.0)
|
|
cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the classification token to use as input for computing plausibility of the
|
|
answer.
|
|
p_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...). 1.0 means token should be
|
|
masked. 0.0 mean token is not masked.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, FlaubertForQuestionAnswering
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-mlm-en-2048")
|
|
>>> model = FlaubertForQuestionAnswering.from_pretrained("FacebookAI/xlm-mlm-en-2048")
|
|
|
|
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
|
|
... 0
|
|
... ) # Batch size 1
|
|
>>> start_positions = torch.tensor([1])
|
|
>>> end_positions = torch.tensor([3])
|
|
|
|
>>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
|
|
>>> loss = outputs.loss
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
langs=langs,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
lengths=lengths,
|
|
cache=cache,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
output = transformer_outputs[0]
|
|
|
|
outputs = self.qa_outputs(
|
|
output,
|
|
start_positions=start_positions,
|
|
end_positions=end_positions,
|
|
cls_index=cls_index,
|
|
is_impossible=is_impossible,
|
|
p_mask=p_mask,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if not return_dict:
|
|
return outputs + transformer_outputs[1:]
|
|
|
|
return FlaubertForQuestionAnsweringOutput(
|
|
loss=outputs.loss,
|
|
start_top_log_probs=outputs.start_top_log_probs,
|
|
start_top_index=outputs.start_top_index,
|
|
end_top_log_probs=outputs.end_top_log_probs,
|
|
end_top_index=outputs.end_top_index,
|
|
cls_logits=outputs.cls_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMForMultipleChoice with XLM_INPUTS->FLAUBERT_INPUTS,XLM->Flaubert
|
|
class FlaubertForMultipleChoice(FlaubertPreTrainedModel):
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super().__init__(config, *inputs, **kwargs)
|
|
|
|
self.transformer = FlaubertModel(config)
|
|
self.sequence_summary = FlaubertSequenceSummary(config)
|
|
self.logits_proj = nn.Linear(config.num_labels, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
langs: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
lengths: Optional[torch.Tensor] = None,
|
|
cache: Optional[dict[str, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, MultipleChoiceModelOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
langs (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
|
|
languages ids which can be obtained from the language names by using two conversion mappings provided in
|
|
the configuration of the model (only provided for multilingual models). More precisely, the *language name
|
|
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
|
|
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
|
|
|
|
See usage examples detailed in the [multilingual documentation](../multilingual).
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
|
|
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
|
|
`[0, ..., input_ids.size(-1)]`.
|
|
cache (`dict[str, torch.FloatTensor]`, *optional*):
|
|
Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
|
|
attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
|
|
decoding.
|
|
|
|
The dictionary object will be modified in-place during the forward pass to add newly computed
|
|
hidden-states.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
labels (`torch.LongTensor` 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)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
langs = langs.view(-1, langs.size(-1)) if langs is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
if lengths is not None:
|
|
logger.warning(
|
|
"The `lengths` parameter cannot be used with the Flaubert multiple choice models. Please use the "
|
|
"attention mask instead."
|
|
)
|
|
lengths = None
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
langs=langs,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
lengths=lengths,
|
|
cache=cache,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
output = transformer_outputs[0]
|
|
logits = self.sequence_summary(output)
|
|
logits = self.logits_proj(logits)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"FlaubertForMultipleChoice",
|
|
"FlaubertForQuestionAnswering",
|
|
"FlaubertForQuestionAnsweringSimple",
|
|
"FlaubertForSequenceClassification",
|
|
"FlaubertForTokenClassification",
|
|
"FlaubertModel",
|
|
"FlaubertWithLMHeadModel",
|
|
"FlaubertPreTrainedModel",
|
|
]
|