wehub-resource-sync
1007 lines
42 KiB
Python
1007 lines
42 KiB
Python
# Copyright (c) 2022 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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import math
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import paddle
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import paddle.nn as nn
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from paddle import einsum
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from ...utils.env import CONFIG_NAME
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from .. import PretrainedModel, register_base_model
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from ..activations import get_activation
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from .configuration import (
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MegatronBert_PRETRAINED_INIT_CONFIGURATION,
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MegatronBert_PRETRAINED_RESOURCE_FILES_MAP,
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MegatronBertConfig,
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)
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__all__ = [
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"MegatronBertModel",
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"MegatronBertPretrainedModel",
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"MegatronBertForQuestionAnswering",
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"MegatronBertForSequenceClassification",
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"MegatronBertForNextSentencePrediction",
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"MegatronBertForCausalLM",
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"MegatronBertForPreTraining",
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"MegatronBertForMaskedLM",
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"MegatronBertForMultipleChoice",
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"MegatronBertForTokenClassification",
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]
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layer_norm_eps = 1e-12
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class MegatronBertPretrainedModel(PretrainedModel):
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r"""
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An abstract class for pretrained MegatronBert models. It provides RoBerta 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.
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See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
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"""
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model_config_file = CONFIG_NAME
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resource_files_names = {"model_state": "model_state.pdparams"}
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pretrained_init_configuration = MegatronBert_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = MegatronBert_PRETRAINED_RESOURCE_FILES_MAP
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base_model_prefix = "megatronbert"
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config_class = MegatronBertConfig
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def _init_weights(self, layer):
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"""Initialization hook"""
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if isinstance(layer, (nn.Linear, nn.Embedding)):
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# only support dygraph, use truncated_normal and make it inplace
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# and configurable later
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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.initializer_range
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if hasattr(self, "initializer_range")
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else self.megatronbert.config["initializer_range"],
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shape=layer.weight.shape,
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)
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)
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elif isinstance(layer, nn.LayerNorm):
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layer._epsilon = layer_norm_eps
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class MegatronBertEmbeddings(nn.Layer):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertEmbeddings, 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.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.register_buffer("position_ids", paddle.arange(end=config.max_position_embeddings).expand((1, -1)))
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self.position_embedding_type = config.position_embedding_type
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def forward(
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self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
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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[:, past_key_values_length : seq_length + past_key_values_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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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class MegatronBertSelfAttention(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertSelfAttention, self).__init__()
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = config.position_embedding_type
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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def transpose_for_scores(self, x):
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new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size]
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x = x.reshape(new_x_shape)
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return x.transpose((0, 2, 1, 3))
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def forward(self, hidden_states, attention_mask=None):
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mixed_query_layer = self.query(hidden_states)
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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attention_scores = paddle.matmul(query_layer, key_layer.transpose((0, 1, 3, 2)))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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seq_length = hidden_states.shape[1]
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position_ids_l = paddle.arange(end=seq_length, dtype="int64").reshape((-1, 1))
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position_ids_r = paddle.arange(end=seq_length, dtype="int64").reshape((1, -1))
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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if self.position_embedding_type == "relative_key":
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relative_position_scores = einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in MegatronBertModel forward() function)
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attention_scores = attention_scores + attention_mask.astype(attention_scores.dtype)
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, axis=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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context_layer = paddle.matmul(attention_probs, value_layer)
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context_layer = context_layer.transpose((0, 2, 1, 3))
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new_context_layer_shape = context_layer.shape[:-2] + [self.all_head_size]
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context_layer = context_layer.reshape(new_context_layer_shape)
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return context_layer, attention_probs
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class MegatronBertSelfOutput(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertSelfOutput, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, residual):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return residual + hidden_states
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class MegatronBertAttention(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertAttention, self).__init__()
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.self = MegatronBertSelfAttention(config)
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self.output = MegatronBertSelfOutput(config)
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self.pruned_heads = set()
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def forward(self, hidden_states, attention_mask=None):
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ln_outputs = self.layer_norm(hidden_states)
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self_outputs = self.self(ln_outputs, attention_mask)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class MegatronBertIntermediate(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertIntermediate, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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self.intermediate_act_fn = get_activation(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 MegatronBertOutput(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertOutput, self).__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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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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return input_tensor + hidden_states
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class MegatronBertLayer(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertLayer, self).__init__()
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self.seq_len_dim = 1
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self.attention = MegatronBertAttention(config)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.intermediate = MegatronBertIntermediate(config)
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self.output = MegatronBertOutput(config)
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def forward(self, hidden_states, attention_mask=None):
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self_attention_outputs = self.attention(hidden_states, attention_mask)
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attention_output = self_attention_outputs[0]
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outputs = self_attention_outputs[1:]
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layer_output = self.feed_forward_chunk(attention_output)
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outputs = (layer_output,) + outputs
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return outputs
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def feed_forward_chunk(self, attention_output):
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ln_output = self.layer_norm(attention_output)
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intermediate_output = self.intermediate(ln_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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class MegatronBertEncoder(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertEncoder, self).__init__()
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self.layer = nn.LayerList([MegatronBertLayer(config) for _ in range(config.num_hidden_layers)])
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# The final layer norm. We removed the 1st LN, moved LN to each hidden layer and this one
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# is simply the final LN (Transformer's BERT has it attached to each hidden layer).
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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, attention_mask=None):
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for i, layer_module in enumerate(self.layer):
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layer_outputs = layer_module(hidden_states, attention_mask)
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hidden_states = layer_outputs[0]
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# Finalize the 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 MegatronBertPooler(nn.Layer):
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertPooler, self).__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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@register_base_model
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class MegatronBertModel(MegatronBertPretrainedModel):
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"""
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The bare MegatronBert Model transformer outputting raw hidden-states.
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This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
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Refer to the superclass documentation for the generic methods.
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This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
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/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
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and refer to the Paddle documentation for all matter related to general usage and behavior.
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Args:
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Args:
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config (:class:`MegatronBertConfig`):
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An instance of MegatronBertConfig used to construct MBartModel.
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"""
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertModel, self).__init__(config)
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self.num_hidden_layers = config.num_hidden_layers
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self.pad_token_id = config.pad_token_id
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self.initializer_range = config.initializer_range
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self.embeddings = MegatronBertEmbeddings(config)
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self.encoder = MegatronBertEncoder(config)
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self.pooler = MegatronBertPooler(config)
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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(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
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r"""
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The MegatronBertModel forward method, overrides the `__call__()` special 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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attention_mask (Tensor, optional):
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Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
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usually the paddings or the subsequent positions.
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Its data type can be int, float and bool.
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If its data type is int, the values should be either 0 or 1.
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- **1** for tokens that **not masked**,
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- **0** for tokens that **masked**.
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It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
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Defaults to `None`, which means nothing needed to be prevented attention to.
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Returns:
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tuple: Returns tuple (`sequence_output`, `pooled_output`).
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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 its shape is [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 its shape is [batch_size, 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 import MegatronBertModel, MegatronBertTokenizer
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tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
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model = MegatronBertModel.from_pretrained('megatronbert-uncased')
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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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input_shape = input_ids.shape
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if attention_mask is None:
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attention_mask = paddle.unsqueeze(
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(input_ids == self.pad_token_id).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
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)
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else:
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if attention_mask.ndim == 2:
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# attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length]
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attention_mask = attention_mask.unsqueeze(axis=[1, 2])
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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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embedding_output = self.embeddings(
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input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids
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)
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encoder_outputs = self.encoder(embedding_output, attention_mask=attention_mask)
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sequence_output = encoder_outputs
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pooled_output = self.pooler(sequence_output)
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return sequence_output, pooled_output
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class MegatronBertForQuestionAnswering(MegatronBertPretrainedModel):
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"""
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MegatronBert Model with question answering tasks.
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Args:
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megatronbert (:class:`MegatronBertModel`):
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An instance of :class:`MegatronBertModel`.
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"""
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def __init__(self, config: MegatronBertConfig):
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super(MegatronBertForQuestionAnswering, self).__init__(config)
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self.megatronbert = MegatronBertModel(config)
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self.qa_outputs = nn.Linear(config.hidden_size, 2)
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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,
|
|
attention_mask=None,
|
|
):
|
|
r"""
|
|
The MegatronBertForQuestionAnswering forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
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 MegatronBertForQuestionAnswering, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForQuestionAnswering.from_pretrained('megatronbert-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
outputs = model(**inputs)
|
|
|
|
start_logits = outputs[0]
|
|
end_logits = outputs[1]
|
|
"""
|
|
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(2, axis=-1)
|
|
start_logits = start_logits.squeeze(-1)
|
|
end_logits = end_logits.squeeze(-1)
|
|
|
|
output = (start_logits, end_logits)
|
|
return output
|
|
|
|
|
|
class MegatronBertForSequenceClassification(MegatronBertPretrainedModel):
|
|
"""
|
|
MegatronBert Model with sequence classification tasks.
|
|
|
|
Args:
|
|
megatronbert (:class:`MegatronBertModel`):
|
|
An instance of :class:`MegatronBertModel`.
|
|
num_labels (int):
|
|
The number of labels.
|
|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertForSequenceClassification, self).__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.megatronbert = MegatronBertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MegatronBertForSequenceClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
Returns:
|
|
Tensor: Returns tensor `logits`, a tensor of the sequence classification logits.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MegatronBertForSequenceClassification, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForSequenceClassification.from_pretrained('megatronbert-uncased', num_labels=2)
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
logits = model(**inputs)
|
|
"""
|
|
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
return logits
|
|
|
|
|
|
class MegatronBertPredictionHeadTransform(nn.Layer):
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertPredictionHeadTransform, self).__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.transform_act_fn = get_activation(config.hidden_act)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MegatronBertLMPredictionHead(nn.Layer):
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertLMPredictionHead, self).__init__()
|
|
self.transform = MegatronBertPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
|
|
self.decoder_weight = self.create_parameter(
|
|
shape=[config.vocab_size, config.hidden_size], dtype=self.transform.dense.weight.dtype, is_bias=False
|
|
)
|
|
self.decoder_bias = self.create_parameter(
|
|
shape=[config.vocab_size], dtype=self.decoder_weight.dtype, is_bias=True
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = paddle.tensor.matmul(hidden_states, self.decoder_weight, transpose_y=True) + self.decoder_bias
|
|
return hidden_states
|
|
|
|
|
|
class MegatronBertOnlyMLMHead(nn.Layer):
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertOnlyMLMHead, self).__init__()
|
|
self.predictions = MegatronBertLMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class MegatronBertOnlyNSPHead(nn.Layer):
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertOnlyNSPHead, self).__init__()
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, pooled_output):
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return seq_relationship_score
|
|
|
|
|
|
class MegatronBertPreTrainingHeads(nn.Layer):
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertPreTrainingHeads, self).__init__()
|
|
self.predictions = MegatronBertLMPredictionHead(config)
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, sequence_output, pooled_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
class MegatronBertForPreTraining(MegatronBertPretrainedModel):
|
|
"""
|
|
Megatronbert Model with pretraining tasks on top.
|
|
|
|
Args:
|
|
megatronbert (:class:`MegatronBertModel`):
|
|
An instance of :class:`MegatronBertModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertForPreTraining, self).__init__(config)
|
|
|
|
self.megatronbert = MegatronBertModel(config)
|
|
self.cls = MegatronBertPreTrainingHeads(config)
|
|
|
|
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MegatronBertForPreTraining forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
Returns:
|
|
tuple: Returns tuple (`prediction_scores`, `seq_relationship_score`).
|
|
|
|
With the fields:
|
|
|
|
- `prediction_scores` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size].
|
|
Otherwise, its shape is [batch_size, mask_token_num, vocab_size].
|
|
|
|
- `seq_relationship_score` (Tensor):
|
|
The scores of next sentence prediction.
|
|
Its data type should be float32 and its shape is [batch_size, 2].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MegatronBertForPreTraining, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForPreTraining.from_pretrained('megatronbert-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
prediction_scores, seq_relationship_score = model(**inputs)
|
|
"""
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
sequence_output, pooled_output = outputs[:2]
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
|
|
|
output = (prediction_scores, seq_relationship_score)
|
|
return output
|
|
|
|
|
|
class MegatronBertForCausalLM(MegatronBertPretrainedModel):
|
|
"""
|
|
MegatronBert Model with a `causal masked language modeling` head on top.
|
|
|
|
Args:
|
|
megatronbert (:class:`MegatronBertModel`):
|
|
An instance of :class:`MegatronBertModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertForCausalLM, self).__init__(config)
|
|
|
|
self.megatronbert = MegatronBertModel(config)
|
|
self.cls = MegatronBertOnlyMLMHead(config)
|
|
|
|
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MegatronBertForCausalLM forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
Returns:
|
|
Tensor: Returns Tensor `prediction_scores`. The scores of masked token prediction.
|
|
Its data type should be float32. If `masked_positions` is None, its shape is
|
|
[batch_size, sequence_length, vocab_size]. Otherwise, its shape is
|
|
[batch_size, mask_token_num, vocab_size].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MegatronBertForCausalLM, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForCausalLM.from_pretrained('megatronbert-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
prediction_scores = model(**inputs)
|
|
"""
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class MegatronBertForMaskedLM(MegatronBertPretrainedModel):
|
|
"""
|
|
MegatronBert Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
megatronbert (:class:`MegatronBertModel`):
|
|
An instance of :class:`MegatronBertModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertForMaskedLM, self).__init__(config)
|
|
|
|
self.megatronbert = MegatronBertModel(config)
|
|
self.cls = MegatronBertOnlyMLMHead(config)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
):
|
|
r"""
|
|
The MegatronBertForMaskedLM forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
Returns:
|
|
Tensor: Returns Tensor `prediction_scores`. The scores of masked token prediction.
|
|
Its data type should be float32. If `masked_positions` is None, its shape is
|
|
[batch_size, sequence_length, vocab_size]. Otherwise, its shape is
|
|
[batch_size, mask_token_num, vocab_size].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MegatronBertForMaskedLM, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForMaskedLM.from_pretrained('megatronbert-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
prediction_scores = model(**inputs)
|
|
"""
|
|
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
return prediction_scores
|
|
|
|
|
|
class MegatronBertForNextSentencePrediction(MegatronBertPretrainedModel):
|
|
"""
|
|
MegatronBert Model with a `next sentence prediction (classification)` head on top.
|
|
|
|
Args:
|
|
megatronbert (:class:`MegatronBertModel`):
|
|
An instance of :class:`MegatronBertModel`.
|
|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertForNextSentencePrediction, self).__init__(config)
|
|
|
|
self.megatronbert = MegatronBertModel(config)
|
|
self.cls = MegatronBertOnlyNSPHead(config)
|
|
|
|
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MegatronBertForNextSentencePrediction forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
Returns:
|
|
Tensor: Returns Tensor `seq_relationship_scores`. The scores of next sentence prediction.
|
|
Its data type should be float32 and its shape is [batch_size, 2].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MegatronBertForNextSentencePrediction, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForNextSentencePrediction.from_pretrained('megatronbert-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
seq_relationship_scores = model(**inputs)
|
|
"""
|
|
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
pooled_output = outputs[1]
|
|
|
|
seq_relationship_scores = self.cls(pooled_output)
|
|
|
|
return seq_relationship_scores
|
|
|
|
|
|
class MegatronBertForMultipleChoice(MegatronBertPretrainedModel):
|
|
"""
|
|
MegatronBert Model with a multiple choice classification head on top.
|
|
|
|
Args:
|
|
megatronbert (:class:`MegatronBertModel`):
|
|
An instance of :class:`MegatronBertModel`.
|
|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
|
|
super(MegatronBertForMultipleChoice, self).__init__(config)
|
|
|
|
self.megatronbert = MegatronBertModel(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, attention_mask=None):
|
|
r"""
|
|
The MegatronBertForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
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 MegatronBertForMultipleChoice, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForNextSentencePrediction.from_pretrained('megatronbert-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
reshaped_logits = model(**inputs)
|
|
"""
|
|
num_choices = input_ids.shape[1]
|
|
|
|
input_ids = input_ids.reshape((-1, input_ids.shape[-1])) if input_ids is not None else None
|
|
attention_mask = attention_mask.reshape((-1, attention_mask.shape[-1])) if attention_mask 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
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position_ids = position_ids.reshape((-1, position_ids.shape[-1])) if position_ids is not None else None
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|
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outputs = self.megatronbert(
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input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
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)
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|
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pooled_output = outputs[1]
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|
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pooled_output = self.dropout(pooled_output)
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logits = self.classifier(pooled_output)
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reshaped_logits = logits.reshape((-1, num_choices))
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|
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return reshaped_logits
|
|
|
|
|
|
class MegatronBertForTokenClassification(MegatronBertPretrainedModel):
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|
"""
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|
MegatronBert Model with a token classification head on top.
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|
|
|
Args:
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|
megatronbert (:class:`MegatronBertModel`):
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|
An instance of :class:`MegatronBertModel`.
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|
|
|
num_labels (int):
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|
The number of labels.
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|
"""
|
|
|
|
def __init__(self, config: MegatronBertConfig):
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|
super(MegatronBertForTokenClassification, self).__init__(config)
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|
self.num_labels = config.num_labels
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|
self.megatronbert = MegatronBertModel(config)
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|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
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|
self.classifier = nn.Linear(config.hidden_size, self.num_labels)
|
|
|
|
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None):
|
|
r"""
|
|
The MegatronBertForTokenClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MegatronBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MegatronBertModel`.
|
|
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 MegatronBertForTokenClassification, MegatronBertTokenizer
|
|
|
|
tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased')
|
|
model = MegatronBertForTokenClassification.from_pretrained('megatronbert-uncased', num_labels=2)
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
reshaped_logits = model(**inputs)
|
|
"""
|
|
|
|
outputs = self.megatronbert(
|
|
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
return logits
|