Source code for fairseq.modules.transformer_layer
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.modules import LayerNorm, MultiheadAttention
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
from torch import Tensor
[docs]class TransformerEncoderLayer(nn.Module):
"""Encoder layer block.
In the original paper each operation (multi-head attention or FFN) is
postprocessed with: `dropout -> add residual -> layernorm`. In the
tensor2tensor code they suggest that learning is more robust when
preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.encoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
"""
def __init__(self, args):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.quant_noise = getattr(args, "quant_noise_pq", 0)
self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8)
self.self_attn = self.build_self_attention(self.embed_dim, args)
self.self_attn_layer_norm = LayerNorm(self.embed_dim)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, "activation_fn", "relu")
)
activation_dropout_p = getattr(args, "activation_dropout", 0)
if activation_dropout_p == 0:
# for backwards compatibility with models that use args.relu_dropout
activation_dropout_p = getattr(args, "relu_dropout", 0)
self.activation_dropout_module = FairseqDropout(
float(activation_dropout_p), module_name=self.__class__.__name__
)
self.normalize_before = args.encoder_normalize_before
self.fc1 = self.build_fc1(
self.embed_dim,
args.encoder_ffn_embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.fc2 = self.build_fc2(
args.encoder_ffn_embed_dim,
self.embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.final_layer_norm = LayerNorm(self.embed_dim)
[docs] def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
return quant_noise(
nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size
)
[docs] def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
return quant_noise(
nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size
)
[docs] def build_self_attention(self, embed_dim, args):
return MultiheadAttention(
embed_dim,
args.encoder_attention_heads,
dropout=args.attention_dropout,
self_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
)
[docs] def upgrade_state_dict_named(self, state_dict, name):
"""
Rename layer norm states from `...layer_norms.0.weight` to
`...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
`...final_layer_norm.weight`
"""
layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"}
for old, new in layer_norm_map.items():
for m in ("weight", "bias"):
k = "{}.layer_norms.{}.{}".format(name, old, m)
if k in state_dict:
state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k]
del state_dict[k]
[docs] def forward(self, x, encoder_padding_mask, attn_mask: Optional[Tensor] = None):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, seq_len)` where padding elements are indicated by ``1``.
attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`,
where `tgt_len` is the length of output and `src_len` is the
length of input, though here both are equal to `seq_len`.
`attn_mask[tgt_i, src_j] = 1` means that when calculating the
embedding for `tgt_i`, we exclude (mask out) `src_j`. This is
useful for strided self-attention.
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
# anything in original attn_mask = 1, becomes -1e8
# anything in original attn_mask = 0, becomes 0
# Note that we cannot use -inf here, because at some edge cases,
# the attention weight (before softmax) for some padded element in query
# will become -inf, which results in NaN in model parameters
if attn_mask is not None:
attn_mask = attn_mask.masked_fill(attn_mask.to(torch.bool), -1e8)
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
x, _ = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
attn_mask=attn_mask,
)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.final_layer_norm(x)
return x
[docs]class TransformerDecoderLayer(nn.Module):
"""Decoder layer block.
In the original paper each operation (multi-head attention, encoder
attention or FFN) is postprocessed with: `dropout -> add residual ->
layernorm`. In the tensor2tensor code they suggest that learning is more
robust when preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.decoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(
self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False
):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.quant_noise = getattr(args, "quant_noise_pq", 0)
self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8)
self.cross_self_attention = getattr(args, "cross_self_attention", False)
self.self_attn = self.build_self_attention(
self.embed_dim,
args,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
)
self.activation_fn = utils.get_activation_fn(
activation=str(args.activation_fn)
if getattr(args, "activation_fn", None) is not None
else "relu"
)
activation_dropout_p = getattr(args, "activation_dropout", 0)
if activation_dropout_p == 0:
# for backwards compatibility with models that use args.relu_dropout
activation_dropout_p = getattr(args, "relu_dropout", 0)
self.activation_dropout_module = FairseqDropout(
float(activation_dropout_p), module_name=self.__class__.__name__
)
self.normalize_before = args.decoder_normalize_before
# use layerNorm rather than FusedLayerNorm for exporting.
# char_inputs can be used to determint this.
# TODO remove this once we update apex with the fix
export = getattr(args, "char_inputs", False)
self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = self.build_encoder_attention(self.embed_dim, args)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
self.fc1 = self.build_fc1(
self.embed_dim,
args.decoder_ffn_embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.fc2 = self.build_fc2(
args.decoder_ffn_embed_dim,
self.embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
self.need_attn = True
self.onnx_trace = False
[docs] def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)
[docs] def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)
[docs] def build_self_attention(
self, embed_dim, args, add_bias_kv=False, add_zero_attn=False
):
return MultiheadAttention(
embed_dim,
args.decoder_attention_heads,
dropout=args.attention_dropout,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
self_attention=not getattr(args, "cross_self_attention", False),
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
)
[docs] def build_encoder_attention(self, embed_dim, args):
return MultiheadAttention(
embed_dim,
args.decoder_attention_heads,
kdim=getattr(args, "encoder_embed_dim", None),
vdim=getattr(args, "encoder_embed_dim", None),
dropout=args.attention_dropout,
encoder_decoder_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
)
[docs] def forward(
self,
x,
encoder_out: Optional[torch.Tensor] = None,
encoder_padding_mask: Optional[torch.Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
prev_self_attn_state: Optional[List[torch.Tensor]] = None,
prev_attn_state: Optional[List[torch.Tensor]] = None,
self_attn_mask: Optional[torch.Tensor] = None,
self_attn_padding_mask: Optional[torch.Tensor] = None,
need_attn: bool = False,
need_head_weights: bool = False,
):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor, optional): binary
ByteTensor of shape `(batch, src_len)` where padding
elements are indicated by ``1``.
need_attn (bool, optional): return attention weights
need_head_weights (bool, optional): return attention weights
for each head (default: return average over heads).
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
if need_head_weights:
need_attn = True
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
if prev_self_attn_state is not None:
prev_key, prev_value = prev_self_attn_state[:2]
saved_state: Dict[str, Optional[Tensor]] = {
"prev_key": prev_key,
"prev_value": prev_value,
}
if len(prev_self_attn_state) >= 3:
saved_state["prev_key_padding_mask"] = prev_self_attn_state[2]
assert incremental_state is not None
self.self_attn._set_input_buffer(incremental_state, saved_state)
_self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state)
if self.cross_self_attention and not (
incremental_state is not None
and _self_attn_input_buffer is not None
and "prev_key" in _self_attn_input_buffer
):
if self_attn_mask is not None:
assert encoder_out is not None
self_attn_mask = torch.cat(
(x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1
)
if self_attn_padding_mask is not None:
if encoder_padding_mask is None:
assert encoder_out is not None
encoder_padding_mask = self_attn_padding_mask.new_zeros(
encoder_out.size(1), encoder_out.size(0)
)
self_attn_padding_mask = torch.cat(
(encoder_padding_mask, self_attn_padding_mask), dim=1
)
assert encoder_out is not None
y = torch.cat((encoder_out, x), dim=0)
else:
y = x
x, attn = self.self_attn(
query=x,
key=y,
value=y,
key_padding_mask=self_attn_padding_mask,
incremental_state=incremental_state,
need_weights=False,
attn_mask=self_attn_mask,
)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
if self.encoder_attn is not None and encoder_out is not None:
residual = x
if self.normalize_before:
x = self.encoder_attn_layer_norm(x)
if prev_attn_state is not None:
prev_key, prev_value = prev_attn_state[:2]
saved_state: Dict[str, Optional[Tensor]] = {
"prev_key": prev_key,
"prev_value": prev_value,
}
if len(prev_attn_state) >= 3:
saved_state["prev_key_padding_mask"] = prev_attn_state[2]
assert incremental_state is not None
self.encoder_attn._set_input_buffer(incremental_state, saved_state)
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
need_weights=need_attn or (not self.training and self.need_attn),
need_head_weights=need_head_weights,
)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.encoder_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.final_layer_norm(x)
if self.onnx_trace and incremental_state is not None:
saved_state = self.self_attn._get_input_buffer(incremental_state)
assert saved_state is not None
if self_attn_padding_mask is not None:
self_attn_state = [
saved_state["prev_key"],
saved_state["prev_value"],
saved_state["prev_key_padding_mask"],
]
else:
self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]]
return x, attn, self_attn_state
return x, attn, None
[docs] def make_generation_fast_(self, need_attn: bool = False, **kwargs):
self.need_attn = need_attn
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m