paddlenlp.transformers.gau_alpha.modeling 源代码

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import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
from paddle.nn import Layer
from ..albert.modeling import ACT2FN

from .. import PretrainedModel, register_base_model

__all__ = [
    "GAUAlphaModel",
    "GAUAlphaForMaskedLM",
    "GAUAlphaPretrainedModel",
    "GAUAlphaForSequenceClassification",
    "GAUAlphaForTokenClassification",
    "GAUAlphaForQuestionAnswering",
    "GAUAlphaForMultipleChoice",
]

INF = 1e4


class Norm(Layer):
    def __init__(self, epsilon=1e-12):
        super().__init__()
        self._epsilon = epsilon

    def forward(self, x):
        variance = paddle.mean(paddle.square(x), axis=-1, keepdim=True)
        return x / paddle.sqrt(variance + self._epsilon)


def attention_normalize(a, mask=None, axis=-1, method="softmax"):
    if method == "softmax":
        return F.softmax(a, axis=axis)
    else:
        if mask is not None:
            l = mask.sum(-1, keepdim=True)
        else:
            l = paddle.ones_like(a) * paddle.shape(a)[-2]
        if method == "squared_relu":
            return F.relu(a) ** 2 / l
        elif method == "softmax_plus":
            scale = paddle.log(l) / np.log(512)
            # mask: 1 for not padding, 0 for padding
            # padding position's scale is 1
            if mask is not None:
                scale = scale * mask + 1 - mask
            return F.softmax(a * scale, axis=axis)
    return a


class ScaleOffset(Layer):
    def __init__(
        self,
        hidden_size=768,
        scale=True,
        offset=True,
    ):
        super().__init__()
        self.scale = scale
        self.offset = offset

        if self.scale:
            self.weight = self.create_parameter((hidden_size,), default_initializer=nn.initializer.Constant(1.0))
        if self.offset:
            self.bias = self.create_parameter((hidden_size,), is_bias=True)

    def forward(self, inputs):
        if self.scale:
            inputs = inputs * self.weight
        if self.offset:
            inputs = inputs + self.bias

        return inputs


class GatedAttentionUnit(Layer):
    """
    https://github.com/ZhuiyiTechnology/GAU-alpha/blob/ea15e08a85d35652775c360218090cbaed98da18/models.py#L6-L85
    """

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=1536,
        attention_key_size=128,
        activation="swish",
        use_bias=False,
        normalization="softmax_plus",
        attention_scale=True,
        attention_dropout=0.1,
        max_position_embeddings=512,
    ):
        super().__init__()
        self.activation = ACT2FN[activation]
        self.intermediate_size = intermediate_size
        self.attention_key_size = attention_key_size
        self.use_bias = use_bias
        self.normalization = normalization
        self.attention_scale = attention_scale
        self.attention_dropout = attention_dropout

        self.i_dense = nn.Linear(
            hidden_size,
            2 * intermediate_size + attention_key_size,
            bias_attr=self.use_bias,
        )
        self.o_dense = nn.Linear(intermediate_size, hidden_size, bias_attr=self.use_bias)

        self.q_scaleoffset = ScaleOffset(attention_key_size, offset=self.use_bias)
        self.k_scaleoffset = ScaleOffset(attention_key_size, offset=self.use_bias)
        self.rotary = RotaryPositionEmbedding(attention_key_size, max_position_embeddings)

    def forward(self, hidden_states, attention_mask=None):
        x = self.i_dense(hidden_states)
        u, v, qk = paddle.split(
            self.activation(x),
            [self.intermediate_size, self.intermediate_size, self.attention_key_size],
            axis=-1,
        )
        q, k = self.q_scaleoffset(qk), self.k_scaleoffset(qk)

        # apply_rotary
        q, k = self.rotary(q), self.rotary(k)

        # Attention
        a = paddle.matmul(q, k, transpose_y=True)

        if self.attention_scale:
            a = a / self.attention_key_size**0.5

        if attention_mask is not None:
            a = a * attention_mask + (attention_mask - 1) * INF

        A = attention_normalize(a, attention_mask, axis=-1, method=self.normalization)

        A = F.dropout(A, p=self.attention_dropout, training=self.training)

        o = self.o_dense(u * paddle.matmul(A, v))

        return o


class GAULayer(Layer):
    def __init__(
        self,
        hidden_size=768,
        intermediate_size=1536,
        attention_key_size=128,
        activation="swish",
        use_bias=False,
        normalization="softmax_plus",
        attention_scale=True,
        attention_dropout=0.1,
        hidden_dropout=0.1,
        norm_eps=1e-12,
        max_position_embeddings=512,
    ):
        super().__init__()
        self.gau = GatedAttentionUnit(
            hidden_size,
            intermediate_size,
            attention_key_size,
            activation,
            use_bias,
            normalization,
            attention_scale,
            attention_dropout,
            max_position_embeddings,
        )
        self.norm = Norm(norm_eps)
        self.hidden_dropout = hidden_dropout

    def forward(self, hidden_states, attention_mask=None):
        gau_output = self.gau(hidden_states, attention_mask=attention_mask)

        # dropout and residual
        o = F.dropout(gau_output[0], p=self.hidden_dropout, training=self.training)
        o = self.norm(hidden_states + o)

        return o


def initializer(tensor, num_hidden_layers=12, order=2, gain=1.0):
    """
    https://github.com/bojone/bert4keras/blob/5572ed481a14f5a62be7107e3846c88a5d6b617d/bert4keras/models.py#L1226-L1235
    """
    shape = paddle.shape(tensor)
    if shape[0] > 10000 or shape[0] < 10:
        hidden_size = shape[1]
    else:
        hidden_size = shape[0]
    gain *= num_hidden_layers ** (-1.0 / order)
    std = 1.13684723 / hidden_size**0.5 * gain

    return nn.initializer.TruncatedNormal(std=std)


class RotaryPositionEmbedding(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, seqlen, 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)


[文档]class GAUAlphaPretrainedModel(PretrainedModel): """ An abstract class for pretrained GAU-alpha models. It provides GAU-alpha 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 = { "chinese_GAU-alpha-char_L-24_H-768": { "vocab_size": 12000, "hidden_size": 768, "intermediate_size": 1536, "num_hidden_layers": 24, "max_position_embeddings": 512, "type_vocab_size": 2, "attention_key_size": 128, "norm_eps": 1e-12, "pad_token_id": 0, "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "hidden_act": "swish", "use_bias": False, "normalization": "softmax_plus", "attention_scale": True, }, } pretrained_resource_files_map = { "model_state": { "chinese_GAU-alpha-char_L-24_H-768": "https://bj.bcebos.com/paddlenlp/models/transformers/gau_alpha/chinese_GAU-alpha-char_L-24_H-768/model_state.pdparams", } } base_model_prefix = "gau_alpha"
[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): num_hidden_layers = ( self.num_hidden_layers if hasattr(self, "num_hidden_layers") else self.gau_alpha.config["num_hidden_layers"] ) initializer(layer.weight, num_hidden_layers, order=2, gain=1.0) if isinstance(layer, nn.Linear): use_bias = self.use_bias if hasattr(self, "use_bias") else self.gau_alpha.config["use_bias"] if layer.bias is not None and not use_bias: layer.bias = None
[文档]@register_base_model class GAUAlphaModel(GAUAlphaPretrainedModel): """ The bare GAUAlpha Model transformer 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.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 `GAUAlphaModel`. 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 `GAUAlphaModel`. hidden_size (int, optional): Dimensionality of the, encoder layers and pooler layer. Defaults to `768`. 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`. num_hidden_layers (int, optional): Number of hidden layers in the gau_alpha encoder. Defaults to `12`. 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`. attention_key_size (int, optional): The dimensionality of the key used in the gau layer. Defaults to `128`. norm_eps (float, optional): The epsilon value used in the normalization layer. Defaults to `1e-12`. pad_token_id (int, optional): The index of padding token in the token vocabulary. Defaults to `0`. 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 gau in all encoder layers to drop some attention target. Defaults to `0.1`. hidden_act (str, optional): The activation function used in gau layer. Defaults to `swish`. use_bias (bool, optional): Whether or not use bias. Defaults to `False`. normalization (str, optional): The normalization method used in gau layer. Defaults to `softmax_plus`. attention_scale (bool, optional): Whether or not to scale the attention scores. Defaults to `True`. """ def __init__( self, vocab_size=12000, hidden_size=768, intermediate_size=1536, num_hidden_layers=24, max_position_embeddings=512, type_vocab_size=2, attention_key_size=128, norm_eps=1e-12, pad_token_id=0, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, hidden_act="swish", use_bias=False, normalization="softmax_plus", attention_scale=True, ): super(GAUAlphaModel, self).__init__() self.pad_token_id = pad_token_id self.norm_eps = norm_eps self.num_hidden_layers = num_hidden_layers self.use_bias = use_bias self.embeddings = GAUAlphaEmbeddings( vocab_size, hidden_size, hidden_dropout_prob, type_vocab_size, norm_eps, ) self.encoder = GAUAlphaEncoder( num_hidden_layers, hidden_size, intermediate_size, attention_key_size, hidden_act, use_bias, normalization, attention_scale, attention_probs_dropout_prob, hidden_dropout_prob, norm_eps, max_position_embeddings, ) self.apply(self.init_weights)
[文档] def forward(self, input_ids=None, attention_mask=None, token_type_ids=None): r""" The GAUAlphaModel forward method, overrides the `__call__()` special method. 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]. attention_mask (Tensor, optional): Mask used in gau to avoid performing attention on 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. It is a tensor with shape broadcasted to `[batch_size, sequence_length, sequence_length]`. Defaults to `None`, which means nothing needed to be prevented attention to. 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. Returns: tuple: Returns `last_hidden_state` (Tensor) Sequence of hidden-states at the last layer of the model. It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import GAUAlphaModel, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaModel.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} last_hidden_state = model(**inputs) """ if attention_mask is None: attention_mask = input_ids != self.pad_token_id if attention_mask.ndim == 2: attention_mask = attention_mask.unsqueeze(1) # bs, 1, seqlen attention_mask = attention_mask.astype(paddle.get_default_dtype()) attention_mask.stop_gradient = True embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, ) last_hidden_state = self.encoder(embedding_output, attention_mask=attention_mask) return last_hidden_state
class GAUAlphaEmbeddings(Layer): """ Include embeddings from word and token_type embeddings """ def __init__( self, vocab_size, hidden_size=768, hidden_dropout_prob=0.1, type_vocab_size=2, norm_eps=1e-12, ): super(GAUAlphaEmbeddings, self).__init__() self.word_embeddings = nn.Embedding(vocab_size, hidden_size) self.token_type_embeddings = nn.Embedding(type_vocab_size, hidden_size) self.norm = Norm(norm_eps) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None): if token_type_ids is None: token_type_ids = paddle.zeros_like(input_ids, dtype="int64") input_embedings = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = input_embedings + token_type_embeddings embeddings = self.norm(embeddings) embeddings = self.dropout(embeddings) return embeddings class GAUAlphaEncoder(Layer): def __init__( self, num_hidden_layers, hidden_size, intermediate_size, attention_key_size, hidden_act, use_bias, normalization, attention_scale, attention_probs_dropout_prob, hidden_dropout_prob, norm_eps, max_position_embeddings, ): super().__init__() self.layer = nn.LayerList( [ GAULayer( hidden_size=hidden_size, intermediate_size=intermediate_size, attention_key_size=attention_key_size, activation=hidden_act, use_bias=use_bias, normalization=normalization, attention_scale=attention_scale, attention_dropout=attention_probs_dropout_prob, hidden_dropout=hidden_dropout_prob, norm_eps=norm_eps, max_position_embeddings=max_position_embeddings, ) for _ in range(num_hidden_layers) ] ) def forward(self, hidden_states, attention_mask=None): for layer_module in self.layer: hidden_states = layer_module( hidden_states, attention_mask, ) return hidden_states
[文档]class GAUAlphaForQuestionAnswering(GAUAlphaPretrainedModel): """ GAUAlpha 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: gau_alpha (:class:`GAUAlphaModel`): An instance of GAUAlphaModel. dropout (float, optional): The dropout probability for output of GAUAlpha. If None, use the same value as `hidden_dropout_prob` of `GAUAlphaModel` instance `gau_alpha`. Defaults to `None`. """ def __init__(self, gau_alpha, dropout=None): super(GAUAlphaForQuestionAnswering, self).__init__() self.gau_alpha = gau_alpha self.dropout = nn.Dropout(dropout if dropout is not None else self.gau_alpha.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.gau_alpha.config["hidden_size"], 2) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, attention_mask=None): r""" The GAUAlphaForQuestionAnswering forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`GAUAlphaModel`. token_type_ids (Tensor, optional): See :class:`GAUAlphaModel`. attention_mask (Tensor, optional): See :class:`GAUAlphaModel`. Returns: tuple: Returns tuple (`start_logits`, `end_logits`). With the fields: - `start_logits` (Tensor): A tensor of the input token classification logits, indicates the start position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length]. - `end_logits` (Tensor): A tensor of the input token classification logits, indicates the end position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length]. Example: .. code-block:: import paddle from paddlenlp.transformers import GAUAlphaForQuestionAnswering, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForQuestionAnswering.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} outputs = model(**inputs) start_logits = outputs[0] end_logits = outputs[1] """ sequence_output = self.gau_alpha(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) logits = self.classifier(sequence_output) start_logits, end_logits = paddle.unstack(logits, axis=-1) return start_logits, end_logits
[文档]class GAUAlphaForSequenceClassification(GAUAlphaPretrainedModel): """ GAUAlpha Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks. Args: gau_alpha (`GAUAlphaModel`): An instance of `paddlenlp.transformers.GAUAlphaModel`. num_classes (int, optional): The number of classes. Default to `2`. dropout (float, optional): The dropout probability for output of GAUAlpha. If None, use the same value as `hidden_dropout_prob` of `paddlenlp.transformers.GAUAlphaModel` instance. Defaults to `None`. """ def __init__(self, gau_alpha, num_classes=2, dropout=None): super(GAUAlphaForSequenceClassification, self).__init__() self.num_classes = num_classes self.gau_alpha = gau_alpha self.dropout = nn.Dropout(dropout if dropout is not None else self.gau_alpha.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.gau_alpha.config["hidden_size"], num_classes) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, attention_mask=None): r""" Args: input_ids (Tensor): See :class:`GAUAlphaModel`. token_type_ids (Tensor, optional): See :class:`GAUAlphaModel`. attention_mask (Tensor, optional): See :class:`GAUAlphaModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the input text classification logits. Shape as `[batch_size, num_classes]` and dtype as float32. Example: .. code-block:: import paddle from paddlenlp.transformers import GAUAlphaForSequenceClassification, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForSequenceClassification.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} logits = model(**inputs) """ sequence_output = self.gau_alpha(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) pooled_output = sequence_output[:, 0] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) return logits
[文档]class GAUAlphaForTokenClassification(GAUAlphaPretrainedModel): """ GAUAlpha Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks. Args: gau_alpha (`GAUAlphaModel`): An instance of `paddlenlp.transformers.GAUAlphaModel`. num_classes (int, optional): The number of classes. Default to `2`. dropout (float, optional): The dropout probability for output of GAUAlpha. If None, use the same value as `hidden_dropout_prob` of `paddlenlp.transformers.GAUAlphaModel` instance. Defaults to `None`. """ def __init__(self, gau_alpha, num_classes=2, dropout=None): super(GAUAlphaForTokenClassification, self).__init__() self.num_classes = num_classes self.gau_alpha = gau_alpha # allow gau_alpha to be config self.dropout = nn.Dropout(dropout if dropout is not None else self.gau_alpha.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.gau_alpha.config["hidden_size"], num_classes) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, attention_mask=None): r""" Args: input_ids (Tensor): See :class:`GAUAlphaModel`. token_type_ids (Tensor, optional): See :class:`GAUAlphaModel`. attention_mask (Tensor, optional): See :class:`GAUAlphaModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the input token classification logits. Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`. Example: .. code-block:: import paddle from paddlenlp.transformers import GAUAlphaForTokenClassification, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForTokenClassification.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} logits = model(**inputs) """ sequence_output = self.gau_alpha(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) return logits
[文档]class GAUAlphaForMultipleChoice(GAUAlphaPretrainedModel): """ GAUAlpha Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks. Args: gau_alpha (:class:`GAUAlphaModel`): An instance of GAUAlphaModel. num_choices (int, optional): The number of choices. Defaults to `2`. dropout (float, optional): The dropout probability for output of GAUAlpha. If None, use the same value as `hidden_dropout_prob` of `GAUAlphaModel` instance `gau_alpha`. Defaults to None. """ def __init__(self, gau_alpha, num_choices=2, dropout=None): super(GAUAlphaForMultipleChoice, self).__init__() self.num_choices = num_choices self.gau_alpha = gau_alpha self.dropout = nn.Dropout(dropout if dropout is not None else self.gau_alpha.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.gau_alpha.config["hidden_size"], 1) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, attention_mask=None): r""" The GAUAlphaForMultipleChoice forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`GAUAlphaModel` and shape as [batch_size, num_choice, sequence_length]. token_type_ids(Tensor, optional): See :class:`GAUAlphaModel` and shape as [batch_size, num_choice, sequence_length]. attention_mask (list, optional): See :class:`GAUAlphaModel` and shape as [batch_size, num_choice, sequence_length]. Returns: Tensor: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits. Shape as `[batch_size, num_choice]` and dtype as `float32`. Example: .. code-block:: import paddle from paddlenlp.transformers import GAUAlphaForMultipleChoice, GAUAlphaTokenizer from paddlenlp.data import Pad tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForMultipleChoice.from_pretrained('chinese_GAU-alpha-char_L-24_H-768', num_choices=2) 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"]) inputs = tokenizer(text, text_pair) input_ids = Pad(axis=0, pad_val=tokenizer.pad_token_id)(inputs["input_ids"]) token_type_ids = Pad(axis=0, pad_val=tokenizer.pad_token_type_id)(inputs["token_type_ids"]) reshaped_logits = model( input_ids=paddle.to_tensor(input_ids, dtype="int64"), token_type_ids=paddle.to_tensor(token_type_ids, dtype="int64"), ) print(reshaped_logits.shape) # [2, 2] """ # input_ids: [bs, num_choice, seq_l] input_ids = input_ids.reshape(shape=(-1, paddle.shape(input_ids)[-1])) # flat_input_ids: [bs*num_choice,seq_l] if token_type_ids is not None: token_type_ids = token_type_ids.reshape(shape=(-1, paddle.shape(token_type_ids)[-1])) if attention_mask is not None: attention_mask = attention_mask.reshape(shape=(-1, paddle.shape(attention_mask)[-1])) sequence_output = self.gau_alpha(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) pooled_output = sequence_output[:, 0] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) # logits: (bs*num_choice,1) reshaped_logits = logits.reshape(shape=(-1, self.num_choices)) # logits: (bs, num_choice) return reshaped_logits
class GAUAlphaLMPredictionHead(Layer): def __init__(self, hidden_size, vocab_size, embedding_weights=None, use_bias=False): super(GAUAlphaLMPredictionHead, self).__init__() self.use_bias = use_bias self.decoder_weight = ( self.create_parameter(shape=[vocab_size, hidden_size], dtype=self.transform.weight.dtype) if embedding_weights is None else embedding_weights ) if use_bias: self.decoder_bias = self.create_parameter( shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True ) def forward(self, hidden_states): hidden_states = paddle.matmul(hidden_states, self.decoder_weight, transpose_y=True) if self.use_bias: hidden_states = hidden_states + self.decoder_bias return hidden_states
[文档]class GAUAlphaForMaskedLM(GAUAlphaPretrainedModel): """ GAUAlpha Model with a `masked language modeling` head on top. Args: gau_alpha (:class:`GAUAlphaModel`): An instance of :class:`GAUAlphaModel`. """ def __init__(self, gau_alpha): super(GAUAlphaForMaskedLM, self).__init__() self.gau_alpha = gau_alpha self.cls = GAUAlphaLMPredictionHead( self.gau_alpha.config["hidden_size"], self.gau_alpha.config["vocab_size"], embedding_weights=self.gau_alpha.embeddings.word_embeddings.weight, use_bias=self.gau_alpha.config["use_bias"], ) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, attention_mask=None): r""" Args: input_ids (Tensor): See :class:`GAUAlphaModel`. token_type_ids (Tensor, optional): See :class:`GAUAlphaModel`. attention_mask (Tensor, optional): See :class:`GAUAlphaModel`. Returns: Tensor: Returns tensor `prediction_scores`, The scores of masked token prediction. Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import GAUAlphaForMaskedLM, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForMaskedLM.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} logits = model(**inputs) print(logits.shape) # [1, 11, 12000] """ sequence_output = self.gau_alpha(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) prediction_scores = self.cls(sequence_output) return prediction_scores