paddlenlp.transformers.roformer.modeling 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 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.

from typing import Optional, Tuple
from paddle import Tensor
import paddle
import paddle.nn as nn

from .. import PretrainedModel, register_base_model
from ..albert.modeling import get_activation
from ..model_outputs import (BaseModelOutputWithPoolingAndCrossAttentions,
                             SequenceClassifierOutput, TokenClassifierOutput,
                             QuestionAnsweringModelOutput,
                             MultipleChoiceModelOutput, MaskedLMOutput,
                             CausalLMOutputWithCrossAttentions, tuple_output)
from paddle.common_ops_import import convert_dtype

__all__ = [
    'RoFormerModel',
    'RoFormerPretrainedModel',
    'RoFormerForSequenceClassification',
    'RoFormerForTokenClassification',
    'RoFormerForQuestionAnswering',
    'RoFormerForMaskedLM',
    'RoFormerForMultipleChoice',
    'RoFormerForCausalLM',
]


class RoFormerEmbeddings(nn.Layer):
    """
    Include embeddings from word and token_type embeddings
    """

    def __init__(
        self,
        vocab_size,
        embedding_size=768,
        hidden_dropout_prob=0.1,
        type_vocab_size=2,
    ):
        super().__init__()
        self.word_embeddings = nn.Embedding(vocab_size, embedding_size)
        self.token_type_embeddings = nn.Embedding(type_vocab_size,
                                                  embedding_size)
        self.layer_norm = nn.LayerNorm(embedding_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None):

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if token_type_ids is None:
            token_type_ids_shape = paddle.shape(inputs_embeds)[:-1]
            token_type_ids = paddle.zeros(token_type_ids_shape, dtype="int64")

        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + token_type_embeddings
        embeddings = self.layer_norm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class RotaryPositionEmbedding(nn.Layer):

    def __init__(self, dim, max_position_embeddings=512):
        super().__init__()
        inv_freq = 1.0 / (10000**(
            paddle.arange(0, dim, 2, dtype=paddle.get_default_dtype()) / dim))
        t = paddle.arange(max_position_embeddings,
                          dtype=paddle.get_default_dtype())
        freqs = paddle.matmul(t.unsqueeze(1), inv_freq.unsqueeze(0))
        self.register_buffer("sin", freqs.sin(), persistable=False)
        self.register_buffer("cos", freqs.cos(), persistable=False)

    def forward(self, x, offset=0):
        # x shape [batch_size, num_heads, seqlen, head_dim]
        seqlen = paddle.shape(x)[-2]
        sin, cos = (
            self.sin[offset:offset + seqlen, :],
            self.cos[offset:offset + seqlen, :],
        )
        x1, x2 = x[..., 0::2], x[..., 1::2]
        # [cos_nθ, -sin_nθ] [x1]
        # [sin_nθ,  cos_nθ] [x2]
        # => [x1 * cos_nθ - x2 * sin_nθ, x1 * sin_nθ + x2 * cos_nθ]
        return paddle.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos],
                            axis=-1).flatten(-2, -1)


class MultiHeadAttentionWithRotary(nn.MultiHeadAttention):

    def __init__(self,
                 embed_dim,
                 num_heads,
                 dropout=0.,
                 kdim=None,
                 vdim=None,
                 need_weights=False,
                 rotary_value=False,
                 max_position_embeddings=512):
        super().__init__(embed_dim, num_heads, dropout, kdim, vdim,
                         need_weights)
        self.rotary_value = rotary_value
        self.rotary = RotaryPositionEmbedding(self.head_dim,
                                              max_position_embeddings)

    def _prepare_qkv(self, query, key, value, cache=None):
        q = self.q_proj(query)
        q = paddle.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
        q = paddle.transpose(x=q, perm=[0, 2, 1, 3])

        k, v = self.compute_kv(key, value)

        offset = 0 if cache is None else cache.k.shape[2]

        # rotary q,k,v
        q = self.rotary(q, offset=offset)
        k = self.rotary(k, offset=offset)
        if self.rotary_value:
            v = self.rotary(v, offset=offset)

        if isinstance(cache, self.Cache):
            # for decoder self-attention in inference
            k = paddle.concat([cache.k, k], axis=2)
            v = paddle.concat([cache.v, v], axis=2)
            cache = self.Cache(k, v)

        return (q, k, v) if cache is None else (q, k, v, cache)


class TransformerEncoderLayerWithRotary(nn.TransformerEncoderLayer):

    def __init__(self,
                 d_model,
                 nhead,
                 dim_feedforward,
                 dropout=0.1,
                 activation="relu",
                 attn_dropout=None,
                 act_dropout=None,
                 normalize_before=False,
                 rotary_value=False,
                 max_position_embeddings=512,
                 **kwargs):
        super().__init__(d_model,
                         nhead,
                         dim_feedforward,
                         dropout=dropout,
                         activation=activation,
                         attn_dropout=attn_dropout,
                         act_dropout=act_dropout,
                         normalize_before=normalize_before)
        self.self_attn = MultiHeadAttentionWithRotary(
            d_model,
            nhead,
            dropout=attn_dropout,
            rotary_value=rotary_value,
            max_position_embeddings=max_position_embeddings)
        self._config.update({
            "rotary_value": rotary_value,
            "max_position_embeddings": max_position_embeddings
        })


class RoFormerPooler(nn.Layer):

    def __init__(self, hidden_size, pool_act="tanh"):
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = get_activation(pool_act)

    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 RoFormerLMPredictionHead(nn.Layer):

    def __init__(self,
                 embedding_size,
                 hidden_size,
                 vocab_size,
                 activation,
                 embedding_weights=None):
        super().__init__()
        self.transform = nn.Linear(hidden_size, embedding_size)
        self.activation = get_activation(activation)
        self.layer_norm = nn.LayerNorm(embedding_size)
        self.decoder_weight = (self.create_parameter(
            shape=[vocab_size, embedding_size],
            dtype=self.transform.weight.dtype,
            is_bias=False,
        ) if embedding_weights is None else embedding_weights)
        self.decoder_bias = self.create_parameter(
            shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True)

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = (paddle.matmul(
            hidden_states, self.decoder_weight, transpose_y=True) +
                         self.decoder_bias)
        return hidden_states


class RoFormerOnlyMLMHead(nn.Layer):

    def __init__(self, embedding_size, hidden_size, vocab_size, activation,
                 embedding_weights):
        super().__init__()
        self.predictions = RoFormerLMPredictionHead(
            embedding_size,
            hidden_size=hidden_size,
            vocab_size=vocab_size,
            activation=activation,
            embedding_weights=embedding_weights)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


[文档]class RoFormerPretrainedModel(PretrainedModel): r""" An abstract class for pretrained RoFormer models. It provides RoFormer related `model_config_file`, `pretrained_init_configuration`, `resource_files_names`, `pretrained_resource_files_map`, `base_model_prefix` for downloading and loading pretrained models. See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details. """ pretrained_init_configuration = { "roformer-chinese-small": { "vocab_size": 50000, "embedding_size": 384, "hidden_size": 384, "num_hidden_layers": 6, "num_attention_heads": 6, "intermediate_size": 1536, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "rotary_value": False, }, "roformer-chinese-base": { "vocab_size": 50000, "embedding_size": 768, "hidden_size": 768, "num_hidden_layers": 12, "num_attention_heads": 12, "intermediate_size": 3072, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 1536, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "rotary_value": False, }, "roformer-chinese-char-small": { "vocab_size": 12000, "embedding_size": 384, "hidden_size": 384, "num_hidden_layers": 6, "num_attention_heads": 6, "intermediate_size": 1536, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "rotary_value": False, }, "roformer-chinese-char-base": { "vocab_size": 12000, "embedding_size": 768, "hidden_size": 768, "num_hidden_layers": 12, "num_attention_heads": 12, "intermediate_size": 3072, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "rotary_value": False, }, "roformer-chinese-sim-char-ft-small": { "vocab_size": 12000, "embedding_size": 384, "hidden_size": 384, "num_hidden_layers": 6, "num_attention_heads": 6, "intermediate_size": 1536, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "eos_token_id": 102, "rotary_value": False, "pool_act": "linear", }, "roformer-chinese-sim-char-ft-base": { "vocab_size": 12000, "embedding_size": 768, "hidden_size": 768, "num_hidden_layers": 12, "num_attention_heads": 12, "intermediate_size": 3072, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "eos_token_id": 102, "rotary_value": False, "pool_act": "linear", }, "roformer-chinese-sim-char-small": { "vocab_size": 12000, "embedding_size": 384, "hidden_size": 384, "num_hidden_layers": 6, "num_attention_heads": 6, "intermediate_size": 1536, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "eos_token_id": 102, "rotary_value": False, "pool_act": "linear", }, "roformer-chinese-sim-char-base": { "vocab_size": 12000, "embedding_size": 768, "hidden_size": 768, "num_hidden_layers": 12, "num_attention_heads": 12, "intermediate_size": 3072, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "eos_token_id": 102, "rotary_value": False, "pool_act": "linear", }, "roformer-english-small-discriminator": { "vocab_size": 30522, "embedding_size": 128, "hidden_size": 256, "num_hidden_layers": 12, "num_attention_heads": 4, "intermediate_size": 1024, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 128, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "rotary_value": True, }, "roformer-english-small-generator": { "vocab_size": 30522, "embedding_size": 128, "hidden_size": 64, "num_hidden_layers": 12, "num_attention_heads": 1, "intermediate_size": 256, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 128, "type_vocab_size": 2, "initializer_range": 0.02, "pad_token_id": 0, "rotary_value": True, }, } pretrained_resource_files_map = { "model_state": { "roformer-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-small/model_state.pdparams", "roformer-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-base/model_state.pdparams", "roformer-chinese-char-small": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-char-small/model_state.pdparams", "roformer-chinese-char-base": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-char-base/model_state.pdparams", "roformer-chinese-sim-char-ft-small": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-sim-char-ft-small/model_state.pdparams", "roformer-chinese-sim-char-ft-base": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-sim-char-ft-base/model_state.pdparams", "roformer-chinese-sim-char-small": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-sim-char-small/model_state.pdparams", "roformer-chinese-sim-char-base": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-chinese-sim-char-base/model_state.pdparams", "roformer-english-small-discriminator": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-english-small-discriminator/model_state.pdparams", "roformer-english-small-generator": "https://bj.bcebos.com/paddlenlp/models/transformers/roformer/roformer-english-small-generator/model_state.pdparams", } } base_model_prefix = "roformer"
[文档] def init_weights(self, layer): """Initialization hook""" if isinstance(layer, (nn.Linear, nn.Embedding)): # In the dygraph mode, use the `set_value` to reset the parameter directly, # and reset the `state_dict` to update parameter in static mode. if isinstance(layer.weight, paddle.Tensor): layer.weight.set_value( paddle.normal( mean=0.0, std=self.initializer_range if hasattr( self, "initializer_range") else self.roformer.config["initializer_range"], shape=layer.weight.shape, )) elif isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-12
[文档]@register_base_model class RoFormerModel(RoFormerPretrainedModel): """ The bare RoFormerModel outputting raw hidden-states. This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`. Refer to the superclass documentation for the generic methods. This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation /docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior. Args: vocab_size (int): Vocabulary size of `inputs_ids` in `RoFormerModel`. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `RoFormerModel`. embedding_size (int, optional): Dimensionality of the embedding layer. Defaults to `768`. hidden_size (int, optional): Dimensionality of the, encoder layers and pooler layer. Defaults to `768`. num_hidden_layers (int, optional): Number of hidden layers in the Transformer encoder. Defaults to `12`. num_attention_heads (int, optional): Number of attention heads for each attention layer in the Transformer encoder. Defaults to `12`. intermediate_size (int, optional): Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to ff layers are firstly projected from `hidden_size` to `intermediate_size`, and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`. Defaults to `3072`. hidden_act (str, optional): The non-linear activation function in the feed-forward layer. ``"gelu"``, ``"relu"`` and any other paddle supported activation functions are supported. Defaults to `"gelu"`. hidden_dropout_prob (float, optional): The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to `0.1`. attention_probs_dropout_prob (float, optional): The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to `0.1`. max_position_embeddings (int, optional): The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to `512`. type_vocab_size (int, optional): The vocabulary size of `token_type_ids`. Defaults to `2`. initializer_range (float, optional): The standard deviation of the normal initializer. Defaults to 0.02. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`RoFormerPretrainedModel.init_weights()` for how weights are initialized in `RoFormerModel`. pad_token_id (int, optional): The index of padding token in the token vocabulary. Defaults to `0`. eos_token_id (int, optional): The id of the `eos` token. Defaults to `102`. pool_act (str, optional): The non-linear activation function in the pooler. Defaults to `"tanh"`. rotary_value (`bool`, optional): Whether or not apply rotay position embeddings to value. Defaults to `False`. """ def __init__( self, vocab_size, embedding_size=768, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1536, type_vocab_size=2, initializer_range=0.02, pad_token_id=0, eos_token_id=102, pool_act="tanh", rotary_value=False, ): super().__init__() self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id self.initializer_range = initializer_range if embedding_size != hidden_size: self.embeddings_project = nn.Linear(embedding_size, hidden_size) self.embeddings = RoFormerEmbeddings( vocab_size, embedding_size, hidden_dropout_prob, type_vocab_size, ) encoder_layer = TransformerEncoderLayerWithRotary( hidden_size, num_attention_heads, intermediate_size, dropout=hidden_dropout_prob, activation=hidden_act, attn_dropout=attention_probs_dropout_prob, act_dropout=0, rotary_value=rotary_value, max_position_embeddings=max_position_embeddings) self.encoder = nn.TransformerEncoder(encoder_layer, num_hidden_layers) self.pooler = RoFormerPooler(hidden_size, pool_act) self.apply(self.init_weights) def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, past_key_values: Optional[Tuple[Tuple[Tensor]]] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerModel forward method, overrides the `__call__()` special method. Args: input_ids (Tensor, optional): Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. It's data type should be `int64` and has a shape of [batch_size, sequence_length]. token_type_ids (Tensor, optional): Segment token indices to indicate first and second portions of the inputs. Indices can be either 0 or 1: - 0 corresponds to a **sentence A** token, - 1 corresponds to a **sentence B** token. It's data type should be `int64` and has a shape of [batch_size, sequence_length]. Defaults to None, which means no segment embeddings is added to token embeddings. attention_mask (Tensor, optional): Mask used in multi-head attention to avoid performing attention to some unwanted positions, usually the paddings or the subsequent positions. Its data type can be int, float and bool. When the data type is bool, the `masked` tokens have `False` values and the others have `True` values. When the data type is int, the `masked` tokens have `0` values and the others have `1` values. When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values. It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`. For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length], [batch_size, num_attention_heads, sequence_length, sequence_length]. Defaults to `None`, which means nothing needed to be prevented attention to. inputs_embeds (Tensor, optional): If you want to control how to convert `inputs_ids` indices into associated vectors, you can pass an embedded representation directly instead of passing `inputs_ids`. past_key_values (tuple(tuple(Tensor)), optional): The length of tuple equals to the number of layers, and each inner tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) which contains precomputed key and value hidden states of the attention blocks. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, optional): If set to `True`, `past_key_values` key value states are returned. Defaults to `None`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerModel, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerModel.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") output = model(**tokenized_inputs) """ 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." ) # init the default bool value output_attentions = output_attentions if output_attentions is not None else False output_hidden_states = output_hidden_states if output_hidden_states is not None else False return_dict = return_dict if return_dict is not None else False use_cache = use_cache if use_cache is not None else False past_key_values_length = 0 if past_key_values is not None: past_key_values_length = past_key_values[0][0].shape[2] if attention_mask is None: attention_mask = paddle.unsqueeze( (input_ids == self.pad_token_id).astype( self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]) if past_key_values is not None: batch_size = past_key_values[0][0].shape[0] past_mask = paddle.zeros( [batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype) attention_mask = paddle.concat([past_mask, attention_mask], axis=-1) # For 2D attention_mask from tokenizer elif attention_mask.ndim == 2: attention_mask = paddle.unsqueeze( attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype()) attention_mask = (1.0 - attention_mask) * -1e4 embedding_output = self.embeddings(input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds) if hasattr(self, "embeddings_project"): embedding_output = self.embeddings_project(embedding_output) self.encoder._use_cache = use_cache # To be consistent with HF encoder_outputs = self.encoder( embedding_output, src_mask=attention_mask, cache=past_key_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) if isinstance(encoder_outputs, type(embedding_output)): sequence_output = encoder_outputs pooled_output = self.pooler(sequence_output) return (sequence_output, pooled_output) else: sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( 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)
def get_input_embeddings(self) -> nn.Embedding: return self.embeddings.word_embeddings def set_input_embeddings(self, embedding: nn.Embedding): self.embeddings.word_embeddings = embedding
[文档] def get_input_embeddings(self) -> nn.Embedding: return self.embeddings.word_embeddings
[文档] def set_input_embeddings(self, embedding: nn.Embedding): self.embeddings.word_embeddings = embedding
[文档]class RoFormerForQuestionAnswering(RoFormerPretrainedModel): r""" RoFormer Model with a linear layer on top of the hidden-states output to compute `span_start_logits` and `span_end_logits`, designed for question-answering tasks like SQuAD. Args: roformer (:class:`RoFormerModel`): An instance of RoFormerModel. dropout (float, optional): The dropout probability for output of RoFormer. If None, use the same value as `hidden_dropout_prob` of `RoFormerModel` instance `roformer`. Defaults to `None`. """ def __init__(self, roformer, dropout=None): super().__init__() self.roformer = roformer # allow roformer to be config self.dropout = nn.Dropout(dropout if dropout is not None else self. roformer.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.roformer.config["hidden_size"], 2) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, start_positions: Optional[Tensor] = None, end_positions: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerForQuestionAnswering forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`RoFormerModel`. token_type_ids (Tensor, optional): See :class:`RoFormerModel`. attention_mask (Tensor, optional): See :class:`RoFormerModel`. inputs_embeds(Tensor, optional): See :class:`RoFormerModel`. start_positions (Tensor of shape `(batch_size,)`, optional): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (Tensor of shape `(batch_size,)`, optional): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerForQuestionAnswering, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForQuestionAnswering.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") outputs = model(**tokenized_inputs) """ outputs = self.roformer(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) sequence_output = outputs[0] logits = self.classifier(sequence_output) start_logits, end_logits = paddle.unstack(x=logits, axis=-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if start_positions.ndim > 1: start_positions = start_positions.squeeze(-1) if start_positions.ndim > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = paddle.shape(start_logits)[1] start_positions = start_positions.clip(0, ignored_index) end_positions = end_positions.clip(0, ignored_index) loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return tuple_output(output, total_loss) return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class RoFormerForSequenceClassification(RoFormerPretrainedModel): r""" RoFormer Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks. Args: roformer (:class:`RoFormerModel`): An instance of `RoFormerModel`. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of RoFormer. If None, use the same value as `hidden_dropout_prob` of `RoFormerModel` instance `roformer`. Defaults to `None`. """ def __init__(self, roformer, num_classes=2, dropout=None): super().__init__() self.num_classes = num_classes self.roformer = roformer # allow roformer to be config self.dropout = nn.Dropout(dropout if dropout is not None else self. roformer.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.roformer.config["hidden_size"], num_classes) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, labels: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerForSequenceClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`RoFormerModel`. token_type_ids (Tensor, optional): See :class:`RoFormerModel`. attention_mask (Tensor, optional): See :class:`RoFormerModel`. inputs_embeds(Tensor, optional): See :class:`RoFormerModel`. labels (Tensor of shape `(batch_size,)`, optional): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., num_classes - 1]`. If `num_classes == 1` a regression loss is computed (Mean-Square loss), If `num_classes > 1` a classification loss is computed (Cross-Entropy). output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerForSequenceClassification, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForSequenceClassification.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs) """ outputs = self.roformer(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.num_classes == 1: loss_fct = paddle.nn.MSELoss() loss = loss_fct(logits, labels) elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32: loss_fct = paddle.nn.CrossEntropyLoss() loss = loss_fct(logits.reshape((-1, self.num_classes)), labels.reshape((-1, ))) else: loss_fct = paddle.nn.BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits, ) + outputs[2:] return tuple_output(output, loss) return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class RoFormerForTokenClassification(RoFormerPretrainedModel): r""" RoFormer Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks. Args: roformer (:class:`RoFormerModel`): An instance of `RoFormerModel`. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of RoFormer. If None, use the same value as `hidden_dropout_prob` of `RoFormerModel` instance `roformer`. Defaults to `None`. """ def __init__(self, roformer, num_classes=2, dropout=None): super().__init__() self.num_classes = num_classes self.roformer = roformer # allow roformer to be config self.dropout = nn.Dropout(dropout if dropout is not None else self. roformer.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.roformer.config["hidden_size"], num_classes) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, labels: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerForTokenClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`RoFormerModel`. token_type_ids (Tensor, optional): See :class:`RoFormerModel`. attention_mask (Tensor, optional): See :class:`RoFormerModel`. inputs_embeds(Tensor, optional): See :class:`RoFormerModel`. labels (Tensor of shape `(batch_size, sequence_length)`, optional): Labels for computing the token classification loss. Indices should be in `[0, ..., num_classes - 1]`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerForTokenClassification, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForTokenClassification.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs) """ outputs = self.roformer(input_ids, token_type_ids=token_type_ids, attention_mask=attention_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 = paddle.nn.CrossEntropyLoss() loss = loss_fct(logits.reshape((-1, self.num_classes)), labels.reshape((-1, ))) if not return_dict: output = (logits, ) + outputs[2:] return tuple_output(output, loss) return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class RoFormerForMultipleChoice(RoFormerPretrainedModel): """ RoFormerModel with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks. Args: roformer (:class:`RoFormerModel`): An instance of RoFormerModel. num_choices (int, optional): The number of choices. Defaults to `2`. dropout (float, optional): The dropout probability for output of RoFormer. If None, use the same value as `hidden_dropout_prob` of `RoFormerModel` instance `roformer`. Defaults to None. """ def __init__(self, roformer, num_choices=2, dropout=None): super().__init__() self.roformer = roformer self.num_choices = num_choices self.dropout = nn.Dropout(dropout if dropout is not None else self. roformer.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.roformer.config["hidden_size"], 1) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, labels: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerForMultipleChoice forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`RoFormerModel` and shape as [batch_size, num_choice, sequence_length]. token_type_ids (Tensor, optional): See :class:`RoFormerModel` and shape as [batch_size, num_choice, sequence_length]. attention_mask (Tensor, optional): See :class:`RoFormerModel` and shape as [batch_size, num_choice, sequence_length]. inputs_embeds(Tensor, optional): See :class:`RoFormerModel`. labels (Tensor of shape `(batch_size, )`, optional): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerForMultipleChoice, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForMultipleChoice.from_pretrained('roformer-chinese-char-base') data = [ { "question": "如何打开ipad屏幕?", "answer1": "按音量按钮。", "answer2": "按下锁定按钮。", "label": 1, }, { "question": "如何缩进一些文本?", "answer1": "在开始写之前留一些空格。", "answer2": "按空格键。", "label": 0, }, ] text = [] text_pair = [] for d in data: text.append(d["question"]) text_pair.append(d["answer1"]) text.append(d["question"]) text_pair.append(d["answer2"]) tokenized_inputs = tokenizer(text, text_pair, padding=True, return_tensors="pd") reshaped_logits = model(**tokenized_inputs) print(reshaped_logits.shape) # [2, 2] """ input_ids = input_ids.reshape( (-1, input_ids.shape[-1])) if input_ids is not None else None token_type_ids = token_type_ids.reshape( (-1, token_type_ids.shape[-1])) if token_type_ids is not None else None attention_mask = attention_mask.reshape( (-1, attention_mask.shape[-1])) if attention_mask is not None else None outputs = self.roformer(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape((-1, self.num_choices)) loss = None if labels is not None: loss_fct = paddle.nn.CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits, ) + outputs[2:] return tuple_output(output, loss) return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class RoFormerForMaskedLM(RoFormerPretrainedModel): """ RoFormer Model with a `masked language modeling` head on top. Args: roformer (:class:RoFormerModel`): An instance of :class:`RoFormerModel`. """ def __init__(self, roformer): super().__init__() self.roformer = roformer self.cls = RoFormerOnlyMLMHead( self.roformer.config["embedding_size"], self.roformer.config["hidden_size"], self.roformer.config["vocab_size"], self.roformer.config["hidden_act"], embedding_weights=self.roformer.embeddings.word_embeddings.weight, ) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, labels: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerForMaskedLM forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`RoFormerModel`. token_type_ids (Tensor, optional): See :class:`RoFormerModel`. attention_mask (Tensor, optional): See :class:`RoFormerModel`. inputs_embeds(Tensor, optional): See :class:`RoFormerModel`. labels (Tensor of shape `(batch_size, sequence_length)`, optional): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., vocab_size]` output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerForMaskedLM, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForMaskedLM.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs) """ outputs = self.roformer(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = paddle.nn.CrossEntropyLoss( ) # -100 index = padding token masked_lm_loss = loss_fct( prediction_scores.reshape((-1, prediction_scores.shape[-1])), labels.reshape((-1, ))) if not return_dict: output = (prediction_scores, ) + outputs[2:] return tuple_output(output, masked_lm_loss) return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class RoFormerForCausalLM(RoFormerPretrainedModel): """ RoFormer Model with a `Causal language modeling` head on top. Args: roformer (:class:RoFormerModel`): An instance of :class:`RoFormerModel`. """ def __init__(self, roformer): super().__init__() self.roformer = roformer self.cls = RoFormerOnlyMLMHead( self.roformer.config["embedding_size"], self.roformer.config["hidden_size"], self.roformer.config["vocab_size"], self.roformer.config["hidden_act"], embedding_weights=self.roformer.embeddings.word_embeddings.weight, ) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, labels: Optional[Tensor] = None, past_key_values: Optional[Tuple[Tuple[Tensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The RoFormerForCausalLM forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`RoFormerModel`. token_type_ids (Tensor, optional): See :class:`RoFormerModel`. attention_mask (Tensor, optional): See :class:`RoFormerModel`. inputs_embeds(Tensor, optional): See :class:`RoFormerModel`. labels (Tensor of shape `(batch_size, sequence_length)`, optional): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., vocab_size]`. past_key_values (tuple(tuple(Tensor)), optional): See :class:`RoFormerModel`. use_cache (Tensor, optional): See :class:`RoFormerModel`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions`. Example: .. code-block:: import paddle from paddlenlp.transformers import RoFormerForCausalLM, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-sim-char-ft-base') model = RoFormerForCausalLM.from_pretrained('roformer-chinese-sim-char-ft-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs) print(logits.shape) # [1, 11, 12000] """ outputs = self.roformer(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, 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 = outputs[0] prediction_scores = self.cls(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :] labels = labels[:, 1:] loss_fct = paddle.nn.CrossEntropyLoss() lm_loss = loss_fct( shifted_prediction_scores.reshape( (-1, prediction_scores.shape[-1])), labels.reshape((-1, ))) if not return_dict: output = (prediction_scores, ) + outputs[2:] return tuple_output(output, lm_loss) return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
def prepare_inputs_for_generation(self, input_ids, use_cache=False, cache=None, **kwargs): # only last token for inputs_ids if past is defined in kwargs token_type_ids = kwargs.get("token_type_ids", None) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: if "int" in convert_dtype(attention_mask.dtype): attention_mask = (1.0 - attention_mask) * -1e4 if cache is not None: input_ids = input_ids[:, -1].unsqueeze(-1) token_type_ids = token_type_ids[:, -1].unsqueeze(-1) if attention_mask.ndim == 4: attention_mask = attention_mask[:, -1, -1, :].unsqueeze([1, 2]) return { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "past_key_values": cache, "use_cache": use_cache, } @staticmethod def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False): # Update the model inputs during generation. # Note that If `token_type_ids` and `attention_mask` in `model_kwargs` # and they contain pad value, the result vectors updated by this method # may be different from expected. In this case, you need to rewrite the # method. # update cache if isinstance(outputs, tuple): model_kwargs["cache"] = outputs[1] # update token_type_ids with last value if "token_type_ids" in model_kwargs and model_kwargs[ "token_type_ids"] is not None: token_type_ids = model_kwargs["token_type_ids"] # token type id = 1 model_kwargs["token_type_ids"] = paddle.concat( [token_type_ids, paddle.ones_like(token_type_ids[:, -1:])], axis=-1) # update attention_mask if not is_encoder_decoder and "attention_mask" in model_kwargs: attention_mask = model_kwargs["attention_mask"] # nn.Pad2D don't support the data type `bool` if convert_dtype(attention_mask.dtype) == 'bool': attention_mask = paddle.cast(attention_mask, 'int64') if len(attention_mask.shape) == 4: attention_mask = attention_mask.expand( (-1, -1, attention_mask.shape[-1], -1)) attention_mask = nn.Pad2D([0, 0, 0, 1], mode='replicate')(attention_mask) attention_mask = nn.Pad2D([0, 1, 0, 0], value=-1e4)(attention_mask) dtype = convert_dtype(attention_mask.dtype) if 'int' in dtype: attention_mask[:, :, -1, -1] = 1 elif 'float' in dtype: attention_mask[:, :, -1, -1] = 0.0 else: raise ValueError( 'The data type of input `attention_mask` must ' 'be bool, int or float') else: # convert to 4D attention_mask attention_mask = paddle.concat([ attention_mask, paddle.ones([attention_mask.shape[0], 1], dtype="int64") ], axis=-1) if "int" in convert_dtype(attention_mask.dtype): attention_mask = (1.0 - attention_mask) * -1e4 attention_mask = attention_mask.unsqueeze([1, 2]).expand( (-1, -1, attention_mask.shape[-1], -1)) token_type_ids = model_kwargs["token_type_ids"] mask = token_type_ids[:, None, :] > token_type_ids[:, :, None] # we need expand attention_mask attention_mask = paddle.where(mask.unsqueeze(1), paddle.to_tensor(-1e4), attention_mask) model_kwargs["attention_mask"] = attention_mask return model_kwargs def __getattr__(self, name): try: return super().__getattr__(name) except AttributeError as e: try: return getattr(getattr(self, self.base_model_prefix), name) except AttributeError: try: return getattr(self, self.base_model_prefix).config[name] except KeyError: raise e