# 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 torch import Tensor
from fairseq import utils
from fairseq.models.transformer import TransformerConfig
from fairseq.modules import LayerNorm, MultiheadAttention
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
class TransformerEncoderLayerBase(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
*cfg.encoder.normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
"""
def __init__(self, cfg, return_fc=False):
super().__init__()
self.cfg = cfg
self.return_fc = return_fc
self.embed_dim = cfg.encoder.embed_dim
self.quant_noise = cfg.quant_noise.pq
self.quant_noise_block_size = cfg.quant_noise.pq_block_size
self.self_attn = self.build_self_attention(self.embed_dim, cfg)
self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export)
self.dropout_module = FairseqDropout(
cfg.dropout, module_name=self.__class__.__name__
)
self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn)
activation_dropout_p = cfg.activation_dropout
if activation_dropout_p == 0:
# for backwards compatibility with models that use cfg.relu_dropout
activation_dropout_p = cfg.relu_dropout or 0
self.activation_dropout_module = FairseqDropout(
float(activation_dropout_p), module_name=self.__class__.__name__
)
self.normalize_before = cfg.encoder.normalize_before
self.fc1 = self.build_fc1(
self.embed_dim,
cfg.encoder.ffn_embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.fc2 = self.build_fc2(
cfg.encoder.ffn_embed_dim,
self.embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export)
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
)
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
)
def _get_fc_rank(self, remove_num: int) -> List[int]:
f1_filter_param = []
for i in range(self.fc1.out_features):
f1_filter_param.append(
torch.sum(torch.abs(self.fc1.weight[i]))
+ torch.sum(torch.abs(self.fc2.weight[:, i]))
+ torch.abs(self.fc1.bias[i])
)
return sorted(
range(len(f1_filter_param)), key=lambda k: f1_filter_param[k], reverse=False
)[0:remove_num]
def _prune_fc_layer(self, remove_index: List[int]):
new_fc1_weight = []
new_fc1_bias = []
for i in range(self.fc1.out_features):
if i not in remove_index:
new_fc1_weight.append(self.fc1.weight[i])
new_fc1_bias.append(self.fc1.bias[i])
new_fc1_weight = torch.stack(new_fc1_weight).detach()
new_fc1_weight.requires_grad = True
new_fc1_bias = torch.stack(new_fc1_bias).detach()
new_fc1_bias.requires_grad = True
self.fc1 = quant_noise(
nn.Linear(self.fc1.in_features, self.fc1.out_features - len(remove_index)),
p=self.quant_noise,
block_size=self.quant_noise_block_size,
)
self.fc1.weight = torch.nn.Parameter(new_fc1_weight)
self.fc1.bias = torch.nn.Parameter(new_fc1_bias)
new_fc2_weight = []
new_fc2_bias = []
for i in range(self.fc2.in_features):
if i not in remove_index:
new_fc2_weight.append(self.fc2.weight[:, i])
new_fc2_bias = self.fc2.bias.detach()
new_fc2_weight = torch.stack(new_fc2_weight, dim=-1).detach()
new_fc2_weight.requires_grad = True
new_fc2_bias = self.fc2.bias.detach()
new_fc2_bias.requires_grad = True
self.fc2 = quant_noise(
nn.Linear(self.fc2.in_features - len(remove_index), self.fc2.out_features),
p=self.quant_noise,
block_size=self.quant_noise_block_size,
)
self.fc2.weight = torch.nn.Parameter(new_fc2_weight)
self.fc2.bias = torch.nn.Parameter(new_fc2_bias)
def build_self_attention(self, embed_dim, cfg):
return MultiheadAttention(
embed_dim,
cfg.encoder.attention_heads,
dropout=cfg.attention_dropout,
self_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
xformers_att_config=cfg.encoder.xformers_att_config,
)
def residual_connection(self, x, residual):
return residual + x
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]
def forward(
self,
x,
encoder_padding_mask: Optional[Tensor],
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 if x.dtype == torch.float32 else -1e4
)
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,
need_weights=False,
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)
fc_result = 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.return_fc and not torch.jit.is_scripting():
return x, fc_result
return x
# backward compatible with the legacy argparse format
class TransformerDecoderLayerBase(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
*cfg.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, cfg, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False
):
super().__init__()
self.embed_dim = cfg.decoder.embed_dim
self.dropout_module = FairseqDropout(
cfg.dropout, module_name=self.__class__.__name__
)
self.quant_noise = cfg.quant_noise.pq
self.quant_noise_block_size = cfg.quant_noise.pq_block_size
self.cross_self_attention = cfg.cross_self_attention
self.self_attn = self.build_self_attention(
self.embed_dim,
cfg,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
)
self.attn_ln = (
LayerNorm(self.embed_dim)
if utils.safe_getattr(cfg, "scale_attn", False)
else None
)
self.nh = self.self_attn.num_heads
self.head_dim = self.self_attn.head_dim
scale_heads = utils.safe_getattr(cfg, "scale_heads", False)
self.c_attn = (
nn.Parameter(torch.ones((self.nh,)), requires_grad=True)
if scale_heads
else None
)
self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn)
activation_dropout_p = cfg.activation_dropout
if activation_dropout_p == 0:
# for backwards compatibility with models that use cfg.relu_dropout
activation_dropout_p = cfg.relu_dropout or 0
self.activation_dropout_module = FairseqDropout(
float(activation_dropout_p), module_name=self.__class__.__name__
)
self.normalize_before = cfg.decoder.normalize_before
self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.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, cfg)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export)
self.ffn_layernorm = (
LayerNorm(cfg.decoder.ffn_embed_dim)
if utils.safe_getattr(cfg, "scale_fc", False)
else None
)
self.w_resid = (
nn.Parameter(
torch.ones(
self.embed_dim,
),
requires_grad=True,
)
if utils.safe_getattr(cfg, "scale_resids", False)
else None
)
self.fc1 = self.build_fc1(
self.embed_dim,
cfg.decoder.ffn_embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.fc2 = self.build_fc2(
cfg.decoder.ffn_embed_dim,
self.embed_dim,
self.quant_noise,
self.quant_noise_block_size,
)
self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export)
self.need_attn = True
self.onnx_trace = False
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)
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)
def build_self_attention(
self, embed_dim, cfg, add_bias_kv=False, add_zero_attn=False
):
return MultiheadAttention(
embed_dim,
cfg.decoder.attention_heads,
dropout=cfg.attention_dropout,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
self_attention=not cfg.cross_self_attention,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
xformers_att_config=cfg.decoder.xformers_att_config,
)
def build_encoder_attention(self, embed_dim, cfg):
return MultiheadAttention(
embed_dim,
cfg.decoder.attention_heads,
kdim=cfg.encoder.embed_dim,
vdim=cfg.encoder.embed_dim,
dropout=cfg.attention_dropout,
encoder_decoder_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
xformers_att_config=cfg.encoder.xformers_att_config,
)
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def residual_connection(self, x, residual):
return residual + x
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,
)
if self.c_attn is not None:
tgt_len, bsz = x.size(0), x.size(1)
x = x.view(tgt_len, bsz, self.nh, self.head_dim)
x = torch.einsum("tbhd,h->tbhd", x, self.c_attn)
x = x.reshape(tgt_len, bsz, self.embed_dim)
if self.attn_ln is not None:
x = self.attn_ln(x)
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)
if self.ffn_layernorm is not None:
x = self.ffn_layernorm(x)
x = self.fc2(x)
x = self.dropout_module(x)
if self.w_resid is not None:
residual = torch.mul(self.w_resid, residual)
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
def make_generation_fast_(self, need_attn: bool = False, **kwargs):
self.need_attn = need_attn
# backward compatible with the legacy argparse format