modeling.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import collections
import math

import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.fluid import layers
from paddle.nn.layer.transformer import _convert_param_attr_to_list

from .. import PretrainedModel, register_base_model

__all__ = [
    'GPT2Model',
    "GPT2PretrainedModel",
    'GPT2ForPretraining',
    'GPT2PretrainingCriterion',
]


class MultiHeadAttention(nn.Layer):
    """
    Attention mapps queries and a set of key-value pairs to outputs, and
    Multi-Head Attention performs multiple parallel attention to jointly attending
    to information from different representation subspaces.

    """

    Cache = collections.namedtuple("Cache", ["k", "v"])
    StaticCache = collections.namedtuple("StaticCache", ["k", "v"])

    def __init__(self,
                 embed_dim,
                 num_heads,
                 dropout=0.,
                 kdim=None,
                 vdim=None,
                 need_weights=False,
                 weight_attr=None,
                 bias_attr=None):
        super(MultiHeadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.need_weights = need_weights

        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
        self.q_proj = nn.Linear(
            embed_dim, embed_dim, weight_attr, bias_attr=bias_attr)
        self.k_proj = nn.Linear(
            self.kdim, embed_dim, weight_attr, bias_attr=bias_attr)
        self.v_proj = nn.Linear(
            self.vdim, embed_dim, weight_attr, bias_attr=bias_attr)
        self.out_proj = nn.Linear(
            embed_dim, embed_dim, weight_attr, bias_attr=bias_attr)

    def _prepare_qkv(self, query, key, value, use_cache=False, cache=None):
        r"""
        Prapares linear projected queries, keys and values for usage of subsequnt
        multiple parallel attention. If `cache` is not None, using cached results
        to reduce redundant calculations.

        """

        q = self.q_proj(query)
        q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
        q = tensor.transpose(x=q, perm=[0, 2, 1, 3])

        if isinstance(cache, self.StaticCache):
            # for encoder-decoder attention in inference and has cached
            k, v = cache.k, cache.v
        else:
            k, v = self.compute_kv(key, value)

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

        return (q, k, v) if use_cache is False else (q, k, v, cache)

    def compute_kv(self, key, value):
        r"""
        Applies linear projection on input keys and values, then splits heads
        (reshape and transpose) to get keys and values from different representation
        subspaces. The results are used as key-values pairs for subsequent multiple
        parallel attention.

        It is part of calculations in multi-head attention, and is provided as
        a method to pre-compute and prefetch these results, thus we can use them
        to construct cache for inference.

        """
        k = self.k_proj(key)
        v = self.v_proj(value)
        k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
        k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
        v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
        v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
        return k, v

    def gen_cache(self, key, value=None, type=Cache):
        """
        Generates cache for `forward` usage in inference accroding to arguments.
        The generated cache is an instance of `MultiHeadAttention.Cache` or an
        instance of `MultiHeadAttention.StaticCache`.
        """
        if type == MultiHeadAttention.StaticCache:  # static_kv
            k, v = self.compute_kv(key, value)
            return self.StaticCache(k, v)
        elif value is None:  # incremental_state
            k = layers.fill_constant_batch_size_like(
                input=key,
                shape=[-1, self.num_heads, 0, self.head_dim],
                dtype=key.dtype,
                value=0)
            v = layers.fill_constant_batch_size_like(
                input=key,
                shape=[-1, self.num_heads, 0, self.head_dim],
                dtype=key.dtype,
                value=0)
            return self.Cache(k, v)
        else:
            # incremental_state with initial value, mainly for usage like UniLM
            return self.Cache(key, value)

    def forward(self,
                query,
                key,
                value,
                attn_mask=None,
                use_cache=False,
                cache=None):
        r"""
        Applies multi-head attention to map queries and a set of key-value pairs
        to outputs.
        """
        key = query if key is None else key
        value = query if value is None else value
        # compute q ,k ,v
        if use_cache is False:
            q, k, v = self._prepare_qkv(query, key, value, use_cache, cache)
        else:
            q, k, v, cache = self._prepare_qkv(query, key, value, use_cache,
                                               cache)
        # scale dot product attention
        product = layers.matmul(
            x=q, y=k, transpose_y=True, alpha=self.head_dim**-0.5)
        if attn_mask is not None:
            product = product + attn_mask
        weights = F.softmax(product)
        if self.dropout:
            weights = F.dropout(
                weights,
                self.dropout,
                training=self.training,
                mode="upscale_in_train")

        out = tensor.matmul(weights, v)

        # combine heads
        out = tensor.transpose(out, perm=[0, 2, 1, 3])
        out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])

        # project to output
        out = self.out_proj(out)

        outs = [out]
        if self.need_weights:
            outs.append(weights)
        if use_cache:
            outs.append(cache)
        return out if len(outs) == 1 else tuple(outs)


class TransformerDecoder(nn.Layer):
    """
    TransformerDecoder is a stack of N decoder layers.
    """

    def __init__(self, decoder_layer, num_layers, norm=None):
        super(TransformerDecoder, self).__init__()
        self.layers = nn.LayerList([(
            decoder_layer
            if i == 0 else type(decoder_layer)(**decoder_layer._config))
                                    for i in range(num_layers)])
        self.num_layers = num_layers
        self.norm = norm
        self.checkpoints = []

    def forward(self,
                tgt,
                memory,
                tgt_mask=None,
                memory_mask=None,
                use_cache=False,
                cache=None):
        r"""
        Applies a stack of N Transformer decoder layers on inputs. If `norm` is
        provided, also applies layer normalization on the output of last decoder
        layer.
        """
        output = tgt
        new_caches = []
        self.checkpoints = []
        for i, mod in enumerate(self.layers):
            if cache is None:
                if use_cache:
                    output, new_cache = mod(output,
                                            memory,
                                            tgt_mask=tgt_mask,
                                            use_cache=use_cache,
                                            cache=cache)
                    new_caches.append(new_cache)
                else:
                    output = mod(output,
                                 memory,
                                 tgt_mask=tgt_mask,
                                 use_cache=use_cache,
                                 cache=cache)

            else:
                output, new_cache = mod(output,
                                        memory,
                                        tgt_mask=tgt_mask,
                                        use_cache=use_cache,
                                        cache=cache[i])
                new_caches.append(new_cache)
            self.checkpoints.append(output.name)

        if self.norm is not None:
            output = self.norm(output)
        return output if use_cache is False else (output, new_caches)

    def gen_cache(self, memory, do_zip=False):
        r"""
        Generates cache for `forward` usage. The generated cache is a list, and
        each element in it is a tuple( :code:`(incremental_cache, static_cache)` )
        produced by `TransformerDecoderLayer.gen_cache`. See `TransformerDecoderLayer.gen_cache`
        for more details. If `do_zip` is True, apply `zip` on these tuples to get
        a list with two elements.
       """
        cache = [layer.gen_cache(memory) for layer in self.layers]
        if do_zip:
            cache = list(zip(*cache))
        return cache


class TransformerDecoderLayer(nn.Layer):
    """
    The transformer decoder layer.

    It contains multiheadattention and some linear layers.
    """

    def __init__(self,
                 d_model,
                 nhead,
                 dim_feedforward,
                 dropout=0.1,
                 activation="gelu",
                 attn_dropout=None,
                 act_dropout=None,
                 normalize_before=True,
                 weight_attr=None,
                 bias_attr=None):
        self._config = locals()
        self._config.pop("self")
        self._config.pop("__class__", None)  # py3

        super(TransformerDecoderLayer, self).__init__()
        attn_dropout = dropout if attn_dropout is None else attn_dropout
        act_dropout = dropout if act_dropout is None else act_dropout
        self.normalize_before = normalize_before

        weight_attrs = _convert_param_attr_to_list(weight_attr, 3)
        bias_attrs = _convert_param_attr_to_list(bias_attr, 3)

        self.self_attn = MultiHeadAttention(
            d_model,
            nhead,
            dropout=attn_dropout,
            weight_attr=weight_attrs[0],
            bias_attr=bias_attrs[0])
        self.linear1 = nn.Linear(
            d_model, dim_feedforward, weight_attrs[2], bias_attr=bias_attrs[2])
        #self.dropout1 = nn.Dropout(act_dropout, mode="upscale_in_train")
        self.linear2 = nn.Linear(
            dim_feedforward, d_model, weight_attrs[2], bias_attr=bias_attrs[2])
        self.norm1 = nn.LayerNorm(d_model, epsilon=1e-5)
        self.norm2 = nn.LayerNorm(d_model, epsilon=1e-5)
        self.dropout1 = nn.Dropout(dropout, mode="upscale_in_train")
        self.dropout2 = nn.Dropout(act_dropout, mode="upscale_in_train")
        self.activation = getattr(F, activation)

    def forward(self, tgt, memory, tgt_mask=None, use_cache=False, cache=None):
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)

        if use_cache is False:
            tgt = self.self_attn(tgt, tgt, tgt, tgt_mask, use_cache, cache)
        else:
            tgt, incremental_cache = self.self_attn(tgt, tgt, tgt, tgt_mask,
                                                    use_cache, cache)
        tgt = residual + self.dropout1(tgt)
        if not self.normalize_before:
            tgt = self.norm1(tgt)

        residual = tgt
        if self.normalize_before:
            tgt = self.norm2(tgt)
        tgt = self.dropout2(
            self.linear2(F.gelu(
                self.linear1(tgt), approximate=True)))
        tgt = residual + tgt

        if not self.normalize_before:
            tgt = self.norm2(tgt)

        return tgt if use_cache is False else (tgt, incremental_cache)

    def gen_cache(self, memory):
        incremental_cache = self.self_attn.gen_cache(
            memory, type=self.self_attn.Cache)
        return incremental_cache


class GPT2Embeddings(nn.Layer):
    """
    Include embeddings from word, position and token_type embeddings
    """

    def __init__(self,
                 vocab_size,
                 hidden_size=768,
                 hidden_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=16,
                 initializer_range=0.02):
        super(GPT2Embeddings, self).__init__()
        self.word_embeddings = nn.Embedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(
                mean=0.0, std=initializer_range)))
        self.position_embeddings = nn.Embedding(
            max_position_embeddings,
            hidden_size,
            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(
                mean=0.0, std=initializer_range)))
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, input_ids, position_ids=None):
        if position_ids is None:
            ones = paddle.ones_like(input_ids, dtype="int64")
            seq_length = paddle.cumsum(ones, axis=1)
            position_ids = seq_length - ones
        input_embedings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)

        embeddings = input_embedings + position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings


class GPT2PretrainedModel(PretrainedModel):
    """
    An abstract class for pretrained GPT2 models. It provides GPT2 related
    `model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
    `pretrained_init_configuration`, `base_model_prefix` for downloading and
    loading pretrained models. See `PretrainedModel` for more details.
    """

    model_config_file = "model_config.json"
    pretrained_init_configuration = {
        "gpt2-base-cn": {
            "vocab_size": 30000,
            "hidden_size": 2560,
            "num_hidden_layers": 32,
            "num_attention_heads": 32,
            "intermediate_size": 10240,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "pad_token_id": 0,
        },
        "gpt2-large-en": {
            "vocab_size": 50304,
            "hidden_size": 4096,
            "num_hidden_layers": 50,
            "num_attention_heads": 32,
            "intermediate_size": 16384,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
        },
        "gpt2-medium-en": {
            "vocab_size": 50304,
            "hidden_size": 1024,
            "num_hidden_layers": 24,
            "num_attention_heads": 16,
            "intermediate_size": 4096,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
        },
        "gpt2-small-en": {
            "vocab_size": 50304,
            "hidden_size": 1024,
            "num_hidden_layers": 4,
            "num_attention_heads": 4,
            "intermediate_size": 4096,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
        },
    }
    resource_files_names = {"model_state": "model_state.pdparams"}
    pretrained_resource_files_map = {
        "model_state": {
            "gpt2-base-cn":
            "https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-base-cn.pdparams",
            "gpt2-medium-en":
            "https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-medium-en.pdparams",
        }
    }
    base_model_prefix = "gpt2"

    def init_weights(self, layer):
        """ Initialization hook """
        if isinstance(layer, (nn.Linear, nn.Embedding)):
            # In the dygraph mode, use the `set_value` to reset the parameter directly,
            # and reset the `state_dict` to update parameter in static mode.
            if isinstance(layer.weight, paddle.Tensor):
                layer.weight.set_value(
                    paddle.tensor.normal(
                        mean=0.0,
                        std=self.initializer_range
                        if hasattr(self, "initializer_range") else
                        self.gpt2.config["initializer_range"],
                        shape=layer.weight.shape))


@register_base_model
class GPT2Model(GPT2PretrainedModel):
    """
    The base model of gpt2.
    """

    def __init__(self,
                 vocab_size,
                 hidden_size=768,
                 num_hidden_layers=12,
                 num_attention_heads=12,
                 intermediate_size=3072,
                 hidden_act="gelu",
                 hidden_dropout_prob=0.1,
                 attention_probs_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=16,
                 initializer_range=0.02,
                 pad_token_id=0):
        super(GPT2Model, self).__init__()
        self.pad_token_id = pad_token_id
        self.initializer_range = initializer_range
        self.embeddings = GPT2Embeddings(
            vocab_size, hidden_size, hidden_dropout_prob,
            max_position_embeddings, type_vocab_size, self.initializer_range)
        decoder_layer = TransformerDecoderLayer(
            d_model=hidden_size,
            nhead=num_attention_heads,
            dim_feedforward=intermediate_size,
            dropout=hidden_dropout_prob,
            activation=hidden_act,
            attn_dropout=attention_probs_dropout_prob,
            act_dropout=0,
            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(
                mean=0.0, std=self.initializer_range)),
            bias_attr=None)
        self.decoder = TransformerDecoder(
            decoder_layer, num_hidden_layers, norm=nn.LayerNorm(hidden_size))
        self.apply(self.init_weights)
        self.checkpoints = []

    def forward(self,
                input_ids,
                position_ids=None,
                attention_mask=None,
                use_cache=False,
                cache=None):
        self.checkpoints = []
        if attention_mask is None:
            length = input_ids.shape[1]
            attention_mask = paddle.tensor.triu(
                (paddle.ones(
                    (length, length),
                    dtype=self.embeddings.word_embeddings.weight.dtype) * -1e9),
                1)
        if position_ids is None:
            past_length = 0
            if cache is not None:
                past_length = cache[0].k.shape[-2]
            position_ids = paddle.arange(
                past_length, input_ids.shape[-1] + past_length, dtype='int64')
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)

        embedding_output = self.embeddings(
            input_ids=input_ids, position_ids=position_ids)
        encoder_outputs = self.decoder(
            embedding_output,
            memory=None,
            tgt_mask=attention_mask,
            use_cache=use_cache,
            cache=cache)
        self.checkpoints.extend(self.decoder.checkpoints)
        return encoder_outputs


class GPT2ForPretraining(GPT2PretrainedModel):
    """
    The pretraining model of GPT2.

    It returns some logits and cached_kvs.
    """

    def __init__(self, gpt2):
        super(GPT2ForPretraining, self).__init__()
        self.gpt2 = gpt2
        self.apply(self.init_weights)

    def forward(self,
                input_ids,
                position_ids=None,
                attention_mask=None,
                masked_positions=None,
                use_cache=False,
                cache=None):
        outputs = self.gpt2(
            input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            use_cache=use_cache,
            cache=cache)
        if use_cache:
            encoder_outputs, cached_kvs = outputs[:2]
        else:
            encoder_outputs = outputs
        logits = paddle.matmul(
            encoder_outputs,
            self.gpt2.embeddings.word_embeddings.weight,
            transpose_y=True)

        if use_cache:
            return logits, cached_kvs
        else:
            return logits


class GPT2PretrainingCriterion(paddle.nn.Layer):
    """
    Criterion for GPT2.

    It calculates the final loss.
    """

    def __init__(self):
        super(GPT2PretrainingCriterion, self).__init__()
        self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none")

    def forward(self, prediction_scores, masked_lm_labels, loss_mask):
        masked_lm_loss = self.loss_func(prediction_scores,
                                        masked_lm_labels.unsqueeze(2))
        loss_mask = loss_mask.reshape([-1])
        masked_lm_loss = paddle.sum(masked_lm_loss.reshape([-1]) * loss_mask)
        loss = masked_lm_loss / loss_mask.sum()
        return loss