# Copyright 2021-2022 The Alibaba DAMO Team Authors. # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch PoNet model. """ import math from distutils.version import LooseVersion import torch import torch.utils.checkpoint from packaging import version from torch import nn from transformers.activations import ACT2FN from transformers.modeling_utils import PreTrainedModel from modelscope.metainfo import Models from modelscope.models import Model, TorchModel from modelscope.models.builder import MODELS from modelscope.outputs import AttentionBackboneModelOutput from modelscope.utils.constant import Tasks from modelscope.utils.logger import get_logger from modelscope.utils.torch_utils import (apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer) from .configuration import PoNetConfig logger = get_logger() is_pytorch_12plus = LooseVersion(torch.__version__) >= LooseVersion('1.12.0') CLS_ID = 101 EOS_ID = 102 def segment_max(src, index, dim=1): if is_pytorch_12plus: out = torch.zeros_like(src).scatter_reduce( dim, index[:, :, None].expand_as(src), src, reduce='amax', include_self=False) else: dummy_scatter_index = index[:, :, None].expand_as(src) min_value = src.min() - 1 dummpy_scatter_shape = (*src.shape[:-1], index.max() + 1, src.shape[-1]) dummy_scatter_index_expand = dummy_scatter_index.unsqueeze(-2).expand( *dummpy_scatter_shape) index_reconstruct_expand = torch.arange( index.max() + 1, device=src.device)[None, None, :, None].expand(*dummpy_scatter_shape) src_expand = src.unsqueeze(-2).expand(*dummpy_scatter_shape) out, _ = src_expand.masked_scatter( dummy_scatter_index_expand != index_reconstruct_expand, torch.full_like(src_expand, min_value.item())).max(dim=1) dummy = index.unsqueeze(-1).expand(*index.shape[:2], out.size(-1)) return torch.gather(out, dim, dummy).to(dtype=src.dtype) def get_segment_index(input_ids, cls_id=CLS_ID, eos_id=EOS_ID): mask = (input_ids == cls_id).to( dtype=torch.long) + (input_ids == eos_id).to(dtype=torch.long) mask = mask + torch.cat([torch.zeros_like(mask[:, 0:1]), mask[:, :-1]], dim=1) return mask.cumsum(dim=1) - 1 def get_token_type_mask(input_ids, cls_id=CLS_ID, eos_id=EOS_ID): mask = (input_ids == cls_id) | (input_ids == eos_id) return mask def get_win_max(hidden_states, kernel_size=3): m = nn.MaxPool1d(kernel_size, stride=1, padding=kernel_size // 2) out = m(hidden_states.permute(0, 2, 1)).permute(0, 2, 1) return out class PoNetEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding( config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm( config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, 'position_embedding_type', 'absolute') self.register_buffer( 'position_ids', torch.arange(config.max_position_embeddings).expand((1, -1))) if version.parse(torch.__version__) > version.parse('1.6.0'): self.register_buffer( 'token_type_ids', torch.zeros( self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), persistent=False, ) def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length:seq_length + past_key_values_length] if token_type_ids is None: if hasattr(self, 'token_type_ids'): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand( input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros( input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == 'absolute': position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class PoNetSelfAttention(nn.Module): def __init__(self, config): super().__init__() self.dense_local = nn.Linear(config.hidden_size, config.hidden_size) self.dense_segment = nn.Linear(config.hidden_size, config.hidden_size) self.num_attention_heads = config.num_attention_heads self.clsgsepg = getattr(config, 'clsgsepg', True) self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.dense_q = nn.Linear(config.hidden_size, self.all_head_size) self.dense_k = nn.Linear(config.hidden_size, self.all_head_size) self.dense_o = nn.Linear(config.hidden_size, self.all_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) # bz, head, len, head_size def forward( self, hidden_states, segment_index, token_type_mask, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): context_layer_q = self.transpose_for_scores( self.dense_q(hidden_states)) context_layer_k = self.transpose_for_scores( self.dense_k(hidden_states)) context_layer_v = context_layer_k context_layer_o = self.transpose_for_scores( self.dense_o(hidden_states)) if attention_mask is not None: _attention_mask = (attention_mask.squeeze(1).unsqueeze(-1) < -1) if attention_mask is not None: context_layer_q.masked_fill_(_attention_mask, 0.0) q = context_layer_q.sum(dim=-2) / torch.ones_like( _attention_mask).to(dtype=context_layer_q.dtype).masked_fill( _attention_mask, 0.0).sum(dim=-2) else: q = context_layer_q.mean(dim=-2) att = torch.einsum('bdh,bdlh -> bdl', q, context_layer_k) / math.sqrt( context_layer_q.shape[-1]) if attention_mask is not None: att = att + attention_mask.squeeze(1) att_prob = att.softmax(dim=-1) v = torch.einsum('bdlh,bdl->bdh', context_layer_v, att_prob) context_layer_segment = self.dense_segment(hidden_states) context_layer_local = self.dense_local(hidden_states) if attention_mask is not None: context_layer_local.masked_fill_( _attention_mask.squeeze(1), -10000) context_layer_segment.masked_fill_( _attention_mask.squeeze(1), -10000) if self.clsgsepg: # XXX: a trick to make sure the segment and local information will not leak context_layer_local = get_win_max( context_layer_local.masked_fill( token_type_mask.unsqueeze(dim=-1), -10000)) context_layer_segment = segment_max( context_layer_segment, index=segment_index) context_layer_segment.masked_fill_( token_type_mask.unsqueeze(dim=-1), 0.0) context_layer_local.masked_fill_( token_type_mask.unsqueeze(dim=-1), 0.0) else: context_layer_local = get_win_max(context_layer_local) context_layer_segment = segment_max( context_layer_segment, index=segment_index) context_layer_local = self.transpose_for_scores(context_layer_local) context_layer_segment = self.transpose_for_scores( context_layer_segment) context_layer = (v.unsqueeze(dim=-2) + context_layer_segment ) * context_layer_o + context_layer_local context_layer = context_layer.permute(0, 2, 1, 3).reshape( *hidden_states.shape[:2], -1) if attention_mask is not None: context_layer.masked_fill_(_attention_mask.squeeze(1), 0.0) outputs = (context_layer, att_prob) if output_attentions else (context_layer, ) return outputs class PoNetSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm( config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class PoNetIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class PoNetOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm( config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class PoNetAttention(nn.Module): def __init__(self, config): super().__init__() self.self = PoNetSelfAttention(config) self.output = PoNetSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len( heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, segment_index, token_type_mask, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): self_outputs = self.self( hidden_states, segment_index, token_type_mask, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output, ) + self_outputs[1:] # add attentions if we output them return outputs class PoNetLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = PoNetAttention(config) config.is_decoder = False # XXX: Decoder is not yet impletemented. self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: assert self.is_decoder, f'{self} should be used as a decoder model if cross attention is added' self.crossattention = PoNetAttention(config) self.intermediate = PoNetIntermediate(config) self.output = PoNetOutput(config) def forward( self, hidden_states, segment_index, token_type_mask, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[: 2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, segment_index, token_type_mask, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[ 1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: assert hasattr( self, 'crossattention' ), f'If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`' # noqa * cross_attn_past_key_value = past_key_value[ -2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[ 1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output) outputs = (layer_output, ) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value, ) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class PoNetEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList( [PoNetLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states, segment_index, token_type_mask, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = ( ) if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states, ) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[ i] if past_key_values is not None else None if getattr(self.config, 'gradient_checkpointing', False) and self.training: if use_cache: logger.warning( '`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting ' '`use_cache=False`...') use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, segment_index, token_type_mask, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, segment_index, token_type_mask, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1], ) if output_attentions: all_self_attentions = all_self_attentions + ( layer_outputs[1], ) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + ( layer_outputs[2], ) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states, ) if not return_dict: return tuple(v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None) return AttentionBackboneModelOutput( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class PoNetPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class PoNetPreTrainedModel(TorchModel, PreTrainedModel): """ A base class to handle weights initialization and a simple interface for loading pretrained models. """ config_class = PoNetConfig base_model_prefix = 'ponet' _keys_to_ignore_on_load_missing = [r'position_ids'] def __init__(self, config, **kwargs): super().__init__(config.name_or_path, **kwargs) super(Model, self).__init__(config) def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_( mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_( mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) @classmethod def _instantiate(cls, **kwargs): model_dir = kwargs.pop('model_dir', None) if model_dir is None: ponet_config = PoNetConfig(**kwargs) model = cls(ponet_config) else: model = super( Model, cls).from_pretrained(pretrained_model_name_or_path=model_dir) return model @MODELS.register_module(Tasks.backbone, module_name=Models.ponet) class PoNetModel(PoNetPreTrainedModel): """The bare PoNet Model transformer outputting raw hidden-states without any specific head on top. This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch `torch.nn.Module `__ subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config (:class:`~modelscope.models.nlp.ponet.PoNetConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder` argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config, add_pooling_layer=True, **kwargs): super().__init__(config, **kwargs) self.config = config self.embeddings = PoNetEmbeddings(config) self.encoder = PoNetEncoder(config) self.pooler = PoNetPooler(config) if add_pooling_layer else None self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value 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.encoder.layer[layer].attention.prune_heads(heads) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, segment_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`~modelscope.models.nlp.ponet.PoNetTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, 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. position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, 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]``. head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj: `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). Returns: Returns `modelscope.outputs.AttentionBackboneModelOutput` Examples: >>> from modelscope.models import Model >>> from modelscope.preprocessors import Preprocessor >>> model = Model.from_pretrained('damo/nlp_ponet_fill-mask_chinese-base', task='backbone') >>> preprocessor = Preprocessor.from_pretrained('damo/nlp_ponet_fill-mask_chinese-base') >>> print(model(**preprocessor('这是个测试'))) """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError( 'You cannot specify both input_ids and inputs_embeds at the same time' ) elif input_ids is not None: input_shape = input_ids.size() batch_size, seq_length = input_shape elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape else: raise ValueError( 'You have to specify either input_ids or inputs_embeds') device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[ 2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones( ((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros( input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, input_shape, device) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size( ) encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones( encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask( encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) segment_index = get_segment_index( input_ids) if segment_ids is None else segment_ids token_type_mask = get_token_type_mask(input_ids) encoder_outputs = self.encoder( embedding_output, segment_index, token_type_mask, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler( sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return AttentionBackboneModelOutput( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, )