# 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
[docs]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"
[docs] 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))
[docs]@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 = []
[docs] 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
[docs]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)
[docs] 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
[docs]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")
[docs] 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