wehub-resource-sync
937 lines
35 KiB
Python
937 lines
35 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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"""Modeling classes for FNet model."""
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import paddle
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import paddle.nn as nn
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from paddle.nn import Layer
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from .. import PretrainedModel, register_base_model
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from ..activations import ACT2FN
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from .configuration import (
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FNET_PRETRAINED_INIT_CONFIGURATION,
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FNET_PRETRAINED_RESOURCE_FILES_MAP,
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FNetConfig,
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)
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__all__ = [
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"FNetPretrainedModel",
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"FNetModel",
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"FNetForSequenceClassification",
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"FNetForPreTraining",
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"FNetForMaskedLM",
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"FNetForNextSentencePrediction",
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"FNetForMultipleChoice",
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"FNetForTokenClassification",
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"FNetForQuestionAnswering",
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]
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class FNetBasicOutput(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.layer_norm(input_tensor + hidden_states)
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return hidden_states
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class FNetOutput(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.layer_norm(input_tensor + hidden_states)
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return hidden_states
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class FNetIntermediate(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class FNetLayer(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.fourier = FNetFourierTransform(config)
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self.intermediate = FNetIntermediate(config)
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self.output = FNetOutput(config)
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def forward(self, hidden_states):
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self_fourier_outputs = self.fourier(hidden_states)
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fourier_output = self_fourier_outputs[0]
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intermediate_output = self.intermediate(fourier_output)
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layer_output = self.output(intermediate_output, fourier_output)
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return (layer_output,)
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class FNetEncoder(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.layers = nn.LayerList([FNetLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(self, hidden_states, output_hidden_states=False, return_dict=True):
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all_hidden_states = () if output_hidden_states else None
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for i, layer_module in enumerate(self.layers):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_outputs = layer_module(hidden_states)
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hidden_states = layer_outputs[0]
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if return_dict:
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return {"last_hidden_state": hidden_states, "all_hidden_states": all_hidden_states}
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return (hidden_states,)
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class FNetPooler(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class FNetEmbeddings(Layer):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config: FNetConfig):
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super(FNetEmbeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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# NOTE: This is the project layer and will be needed. The original code allows for different embedding and different model dimensions.
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self.projection = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids", paddle.arange(config.max_position_embeddings, dtype="int64").expand((1, -1))
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)
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def forward(
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self,
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input_ids,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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):
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if input_ids is not None:
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input_shape = input_ids.shape
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else:
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input_shape = inputs_embeds.shape[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if token_type_ids is None:
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token_type_ids = paddle.zeros(input_shape, dtype="int64")
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.layer_norm(embeddings)
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embeddings = self.projection(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class FNetBasicFourierTransform(Layer):
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def __init__(self):
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super().__init__()
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self.fourier_transform = paddle.fft.fftn
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def forward(self, hidden_states):
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outputs = self.fourier_transform(hidden_states).real()
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return (outputs,)
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class FNetFourierTransform(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.fourier_transform = FNetBasicFourierTransform()
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self.output = FNetBasicOutput(config)
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def forward(self, hidden_states):
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self_outputs = self.fourier_transform(hidden_states)
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fourier_output = self.output(self_outputs[0], hidden_states)
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return (fourier_output,)
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class FNetPredictionHeadTransform(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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if isinstance(config.hidden_act, str):
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self.transform_act_fn = ACT2FN[config.hidden_act]
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else:
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self.transform_act_fn = config.hidden_act
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.transform_act_fn(hidden_states)
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hidden_states = self.layer_norm(hidden_states)
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return hidden_states
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class FNetLMPredictionHead(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.transform = FNetPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = nn.Linear(config.vocab_size, config.hidden_size)
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self.bias = self.create_parameter(
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[config.vocab_size], is_bias=True, default_initializer=nn.initializer.Constant(value=0)
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)
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self.decoder.bias = self.bias
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def forward(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = paddle.matmul(hidden_states, self.decoder.weight, transpose_y=True) + self.bias
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return hidden_states
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class FNetOnlyMLMHead(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.predictions = FNetLMPredictionHead(config)
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def forward(self, sequence_output):
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prediction_scores = self.predictions(sequence_output)
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return prediction_scores
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class FNetOnlyNSPHead(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.seq_relationship = nn.Linear(config.hidden_size, 2)
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def forward(self, pooled_output):
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seq_relationship_score = self.seq_relationship(pooled_output)
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return seq_relationship_score
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class FNetPreTrainingHeads(Layer):
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def __init__(self, config: FNetConfig):
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super().__init__()
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self.predictions = FNetLMPredictionHead(config)
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self.seq_relationship = nn.Linear(config.hidden_size, 2)
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def forward(self, sequence_output, pooled_output):
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prediction_scores = self.predictions(sequence_output)
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seq_relationship_score = self.seq_relationship(pooled_output)
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return prediction_scores, seq_relationship_score
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class FNetPretrainedModel(PretrainedModel):
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"""
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An abstract class for pretrained FNet models. It provides FNet related
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`model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
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`pretrained_resource_files_map`, `base_model_prefix` for downloading and
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loading pretrained models. See `PretrainedModel` for more details.
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"""
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pretrained_init_configuration = FNET_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = FNET_PRETRAINED_RESOURCE_FILES_MAP
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base_model_prefix = "fnet"
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config_class = FNetConfig
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def _init_weights(self, layer):
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# Initialize the weights.
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if isinstance(layer, nn.Linear):
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layer.weight.set_value(
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paddle.tensor.normal(
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mean=0.0,
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std=self.config.initializer_range,
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shape=layer.weight.shape,
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)
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)
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if layer.bias is not None:
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layer.bias.set_value(paddle.zeros_like(layer.bias))
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elif isinstance(layer, nn.Embedding):
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layer.weight.set_value(
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paddle.tensor.normal(
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mean=0.0,
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std=self.config.initializer_range,
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shape=layer.weight.shape,
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)
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)
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if layer._padding_idx is not None:
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layer.weight[layer._padding_idx].set_value(paddle.zeros_like(layer.weight[layer._padding_idx]))
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elif isinstance(layer, nn.LayerNorm):
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layer.bias.set_value(paddle.zeros_like(layer.bias))
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layer.weight.set_value(paddle.ones_like(layer.weight))
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@register_base_model
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class FNetModel(FNetPretrainedModel):
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"""
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The model can behave as an encoder, following the architecture described in `FNet: Mixing Tokens with Fourier
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Transforms <https://arxiv.org/abs/2105.03824>`__ by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago
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Ontanon.
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"""
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def __init__(self, config: FNetConfig):
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super(FNetModel, self).__init__(config)
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self.initializer_range = config.initializer_range
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self.num_hidden_layers = config.num_hidden_layers
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self.embeddings = FNetEmbeddings(config)
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self.encoder = FNetEncoder(config)
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self.pooler = FNetPooler(config) if config.add_pooling_layer else None
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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def forward(
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self,
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input_ids=None,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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output_hidden_states=None,
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return_dict=None,
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):
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r"""
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The FNetModel forward method.
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Args:
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input_ids (Tensor):
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Indices of input sequence tokens in the vocabulary. They are
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numerical representations of tokens that build the input sequence.
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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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token_type_ids (Tensor, optional):
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Segment token indices to indicate different portions of the inputs.
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Selected in the range ``[0, type_vocab_size - 1]``.
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If `type_vocab_size` is 2, which means the inputs have two portions.
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Indices can either be 0 or 1:
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- 0 corresponds to a *sentence A* token,
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- 1 corresponds to a *sentence B* token.
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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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Defaults to `None`, which means we don't add segment embeddings.
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position_ids(Tensor, optional):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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max_position_embeddings - 1]``.
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Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
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inputs_embeds (Tensor, optional):
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If you want to control how to convert `inputs_ids` indices into associated vectors, you can
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pass an embedded representation directly instead of passing `inputs_ids`.
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output_hidden_states (bool, optional):
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Whether or not to return all hidden states. Default to `None`.
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return_dict (bool, optional):
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Whether or not to return a dict instead of a plain tuple. Default to `None`.
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Returns:
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tuple or Dict: Returns tuple (`sequence_output`, `pooled_output`, `encoder_outputs[1:]`)
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or a dict with last_hidden_state`, `pooled_output`, `all_hidden_states`, fields.
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With the fields:
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- `sequence_output` (Tensor):
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Sequence of hidden-states at the last layer of the model.
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It's data type should be float32 and has a shape of [`batch_size, sequence_length, hidden_size`].
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- `pooled_output` (Tensor):
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The output of first token (`[CLS]`) in sequence.
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We "pool" the model by simply taking the hidden state corresponding to the first token.
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Its data type should be float32 and
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has a shape of [batch_size, hidden_size].
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- `last_hidden_state` (Tensor):
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The output of the last encoder layer, it is also the `sequence_output`.
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It's data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].
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- `all_hidden_states` (Tensor):
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Hidden_states of all layers in the Transformer encoder. The length of `all_hidden_states` is `num_hidden_layers + 1`.
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For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
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Example:
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.. code-block::
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import paddle
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from paddlenlp.transformers.fnet.modeling import FNetModel
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from paddlenlp.transformers.fnet.tokenizer import FNetTokenizer
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tokenizer = FNetTokenizer.from_pretrained('fnet-base')
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model = FNetModel.from_pretrained('fnet-base')
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inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
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inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
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output = model(**inputs)
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"""
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.shape
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.shape[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if token_type_ids is None:
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token_type_ids = paddle.zeros(shape=input_shape, dtype="int64")
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embedding_output = self.embeddings(
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input_ids=input_ids,
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position_ids=position_ids,
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token_type_ids=token_type_ids,
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inputs_embeds=inputs_embeds,
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)
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encoder_outputs = self.encoder(
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embedding_output,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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sequence_output = encoder_outputs["last_hidden_state"] if return_dict else encoder_outputs[0]
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pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
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if return_dict:
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return {
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"last_hidden_state": sequence_output,
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"pooler_output": pooler_output,
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"all_hidden_states": encoder_outputs["all_hidden_states"],
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}
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return (sequence_output, pooler_output) + encoder_outputs[1:]
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class FNetForSequenceClassification(FNetPretrainedModel):
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"""
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FNet Model with a linear layer on top of the output layer,
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designed for sequence classification/regression tasks like GLUE tasks.
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Args:
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fnet (:class:`FNetModel`):
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An instance of FNetModel.
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num_classes (int, optional):
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The number of classes. Defaults to `2`.
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"""
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def __init__(self, config: FNetConfig, num_classes=2):
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super(FNetForSequenceClassification, self).__init__(config)
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self.num_classes = num_classes
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self.fnet = FNetModel(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.classifier = nn.Linear(config.hidden_size, num_classes)
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def forward(
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self,
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input_ids=None,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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labels=None,
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output_hidden_states=None,
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return_dict=None,
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):
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r"""
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The FNetForSequenceClassification forward method.
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|
|
Args:
|
|
input_ids (Tensor):
|
|
Indices of input sequence tokens in the vocabulary. They are
|
|
numerical representations of tokens that build the input sequence.
|
|
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
|
|
token_type_ids (Tensor, optional):
|
|
Segment token indices to indicate different portions of the inputs.
|
|
Selected in the range ``[0, type_vocab_size - 1]``.
|
|
If `type_vocab_size` is 2, which means the inputs have two portions.
|
|
Indices can either be 0 or 1:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
|
|
Defaults to `None`, which means we don't add segment embeddings.
|
|
position_ids(Tensor, optional):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
|
|
max_position_embeddings - 1]``.
|
|
Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
|
|
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`.
|
|
output_hidden_states (bool, optional):
|
|
Whether or not to return all hidden states. Default to `None`.
|
|
return_dict (bool, optional):
|
|
Whether or not to return a dict instead of a plain tuple. Default to `None`.
|
|
|
|
|
|
Returns:
|
|
Tensor or Dict: Returns tensor `logits`, or a dict with `logits`, `hidden_states`, `attentions` fields.
|
|
|
|
With the fields:
|
|
|
|
- `logits` (Tensor):
|
|
A tensor of the input text classification logits.
|
|
Shape as `[batch_size, num_classes]` and dtype as float32.
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers.fnet.modeling import FNetForSequenceClassification
|
|
from paddlenlp.transformers.fnet.tokenizer import FNetTokenizer
|
|
|
|
tokenizer = FNetTokenizer.from_pretrained('fnet-base')
|
|
model = FNetModel.from_pretrained('fnet-base')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
output = model(**inputs)
|
|
"""
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = outputs["pooler_output"] if return_dict else outputs[1]
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
if return_dict:
|
|
return {
|
|
"logits": logits,
|
|
"hidden_states": outputs["all_hidden_states"],
|
|
}
|
|
return logits
|
|
|
|
|
|
class FNetForPreTraining(FNetPretrainedModel):
|
|
"""
|
|
FNet Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
|
|
sentence prediction (classification)` head.
|
|
"""
|
|
|
|
def __init__(self, config: FNetConfig):
|
|
super().__init__(config)
|
|
|
|
self.fnet = FNetModel(config)
|
|
self.cls = FNetPreTrainingHeads(config)
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
|
|
def get_input_embeddings(self):
|
|
return self.fnet.embeddings.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
next_sentence_label=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
The FNetForPretraining forward method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`FNetModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
labels (LongTensor of shape (batch_size, sequence_length), optional):
|
|
Labels for computing the masked language modeling loss.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
next_sentence_labels(Tensor):
|
|
The labels of the next sentence prediction task, the dimensionality of `next_sentence_labels`
|
|
is equal to `seq_relation_labels`. Its data type should be int64 and
|
|
its shape is [batch_size, 1]
|
|
output_hidden_states (bool, optional):
|
|
See :class:`FNetModel`.
|
|
return_dict (bool, optional):
|
|
See :class:`FNetModel`.
|
|
|
|
Returns:
|
|
tuple or Dict: Returns tuple (`prediction_scores`, `seq_relationship_score`) or a dict with
|
|
`prediction_logits`, `seq_relationship_logits`, `hidden_states` fields.
|
|
"""
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
|
|
pooled_output = outputs[1] if not return_dict else outputs["pooler_output"]
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
|
|
|
if return_dict:
|
|
return {
|
|
"prediction_logits": prediction_scores,
|
|
"seq_relationship_logits": seq_relationship_score,
|
|
"hidden_states": outputs["all_hidden_states"],
|
|
}
|
|
return prediction_scores, seq_relationship_score, outputs["all_hidden_states"]
|
|
|
|
|
|
class FNetForMaskedLM(FNetPretrainedModel):
|
|
"""
|
|
FNet Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
fnet (:class:`FNetModel`):
|
|
An instance of :class:`FNetModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: FNetConfig):
|
|
super().__init__(config)
|
|
|
|
self.fnet = FNetModel(config)
|
|
self.cls = FNetOnlyMLMHead(config)
|
|
self.tie_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
|
|
def get_input_embeddings(self):
|
|
return self.fnet.embeddings.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
next_sentence_label=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
The FNetForMaskedLM forward method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`FNetModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
labels(Tensor, optional):
|
|
See :class:`FNetForPreTraining`.
|
|
next_sentence_label(Tensor, optional):
|
|
See :class:`FNetForPreTraining`.
|
|
output_hidden_states(Tensor, optional):
|
|
See :class:`FNetModel`.
|
|
return_dict(bool, optional):
|
|
See :class:`FNetModel`.
|
|
|
|
Returns:
|
|
Tensor or Dict: Returns tensor `prediction_scores` or a dict with `prediction_logits`, `hidden_states` fields.
|
|
|
|
With the fields:
|
|
|
|
- `prediction_scores` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
and its shape is [batch_size, sequence_length, vocab_size].
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
"""
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
if return_dict:
|
|
return {"prediction_logits": prediction_scores, "hidden_states": outputs["all_hidden_states"]}
|
|
return prediction_scores, outputs["all_hidden_states"]
|
|
|
|
|
|
class FNetForNextSentencePrediction(FNetPretrainedModel):
|
|
"""
|
|
FNet Model with a `next sentence prediction` head on top.
|
|
|
|
Args:
|
|
fnet (:class:`FNetModel`):
|
|
An instance of :class:`FNetModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: FNetConfig):
|
|
super().__init__(config)
|
|
|
|
self.fnet = FNetModel(config)
|
|
self.cls = FNetOnlyNSPHead(config)
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
|
|
def get_input_embeddings(self):
|
|
return self.fnet.embeddings.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
next_sentence_label=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = outputs[1] if not return_dict else outputs["pooler_output"]
|
|
seq_relationship_score = self.cls(pooled_output)
|
|
|
|
if return_dict:
|
|
return {"seq_relationship_logits": seq_relationship_score, "hidden_states": outputs["all_hidden_states"]}
|
|
return seq_relationship_score, outputs["all_hidden_states"]
|
|
|
|
|
|
class FNetForMultipleChoice(FNetPretrainedModel):
|
|
"""
|
|
FNet Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like SWAG tasks .
|
|
|
|
Args:
|
|
fnet (:class:`FNetModel`):
|
|
An instance of FNetModel.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: FNetConfig):
|
|
super(FNetForMultipleChoice, self).__init__(config)
|
|
self.fnet = FNetModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
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
|
|
position_ids = position_ids.reshape([-1, position_ids.shape[-1]]) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.reshape([-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]])
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = outputs["pooler_output"] if return_dict else outputs[1]
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.reshape([-1, num_choices])
|
|
|
|
if return_dict:
|
|
return {
|
|
"logits": reshaped_logits,
|
|
"hidden_states": outputs["all_hidden_states"],
|
|
}
|
|
return reshaped_logits
|
|
|
|
|
|
class FNetForTokenClassification(FNetPretrainedModel):
|
|
"""
|
|
FNet Model with a linear layer on top of the hidden-states output layer,
|
|
designed for token classification tasks like NER tasks.
|
|
|
|
Args:
|
|
fnet (:class:`FNetModel`):
|
|
An instance of FNetModel.
|
|
num_classes (int, optional):
|
|
The number of classes. Defaults to `2`.
|
|
"""
|
|
|
|
def __init__(self, config: FNetConfig, num_classes=2):
|
|
super(FNetForTokenClassification, self).__init__(config)
|
|
self.fnet = FNetModel(config)
|
|
self.num_classes = num_classes
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, self.num_classes)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
if return_dict:
|
|
return {
|
|
"logits": logits,
|
|
"hidden_states": outputs["all_hidden_states"],
|
|
}
|
|
return logits
|
|
|
|
|
|
class FNetForQuestionAnswering(FNetPretrainedModel):
|
|
"""
|
|
FNet 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:
|
|
fnet (:class:`FNetModel`):
|
|
An instance of FNetModel.
|
|
num_labels (int):
|
|
The number of labels.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: FNetConfig, num_labels):
|
|
super(FNetForQuestionAnswering, self).__init__(config)
|
|
self.num_labels = num_labels
|
|
self.fnet = FNetModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, self.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
start_positions=None,
|
|
end_positions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
outputs = self.fnet(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = paddle.split(logits, num_or_sections=2, axis=-1)
|
|
start_logits = start_logits.squeeze(axis=-1)
|
|
end_logits = start_logits.squeeze(axis=-1)
|
|
if return_dict:
|
|
return {
|
|
"start_logits": start_logits,
|
|
"end_logits": end_logits,
|
|
"hidden_states": outputs["all_hidden_states"],
|
|
}
|
|
return start_logits, end_logits
|