"""
Implementation of "Attention is All You Need" and of
subsequent transformer based architectures
"""
import torch
import torch.nn as nn
from onmt.decoders.decoder import DecoderBase
from onmt.modules import MultiHeadedAttention, AverageAttention
from onmt.modules.position_ffn import PositionwiseFeedForward
from onmt.modules.position_ffn import ActivationFunction
from onmt.modules.moe import MoE
from onmt.utils.misc import sequence_mask
from onmt.modules.rmsnorm import RMSNorm
class TransformerDecoderLayerBase(nn.Module):
def __init__(
self,
d_model,
heads,
d_ff,
dropout,
attention_dropout,
self_attn_type="scaled_dot",
max_relative_positions=0,
relative_positions_buckets=0,
aan_useffn=False,
full_context_alignment=False,
alignment_heads=0,
pos_ffn_activation_fn=ActivationFunction.relu,
add_qkvbias=False,
num_kv=0,
add_ffnbias=True,
parallel_residual=False,
shared_layer_norm=False,
layer_norm="standard",
norm_eps=1e-6,
use_ckpting=[],
parallel_gpu=1,
sliding_window=0,
rotary_interleave=True,
rotary_theta=1e4,
rotary_dim=0,
num_experts=0,
num_experts_per_tok=2,
):
"""
Args:
d_model (int): the dimension of keys/values/queries in
:class:`MultiHeadedAttention`, also the input size of
the first-layer of the :class:`PositionwiseFeedForward`.
heads (int): the number of heads for MultiHeadedAttention.
d_ff (int): the second-layer of the
:class:`PositionwiseFeedForward`.
dropout (float): dropout in residual, self-attn(dot) and
feed-forward
attention_dropout (float): dropout in context_attn (and
self-attn(avg))
self_attn_type (string): type of self-attention scaled-dot,
flash-scaled-dot, average
max_relative_positions (int):
Max distance between inputs in relative positions
representations
relative_positions_buckets (int):
relative position bias see
https://github.com/google-research/text-to-text-transfer-transformer
aan_useffn (bool): Turn on the FFN layer in the AAN decoder
full_context_alignment (bool):
whether enable an extra full context decoder forward for
alignment
alignment_heads (int):
N. of cross attention heads to use for alignment guiding
pos_ffn_activation_fn (ActivationFunction):
activation function choice for PositionwiseFeedForward layer
add_qkvbias (bool): whether to add bias to the Key/Value nn.Linear
num_kv (int): number of heads for KV when different vs Q (multiquery)
add_ffnbias (bool): whether to add bias to the FF nn.Linear
parallel_residual (bool): Use parallel residual connections in each layer block, as used
by the GPT-J and GPT-NeoX models
shared_layer_norm (bool): When using parallel residual, share the input and post
attention layer norms.
layer_norm (string): type of layer normalization standard/rms
norm_eps (float): layer norm epsilon
use_ckpting (List): layers for which we checkpoint for backward
parallel_gpu (int): Number of gpu for tensor parallelism
sliding_window (int): Width of the band mask and KV cache (cf Mistral Model)
rotary_interleave (bool): Interleave the head dimensions when rotary
embeddings are applied
rotary_theta (int): rotary base theta
rotary_dim (int): in some cases the rotary dim is lower than head dim
num_experts (int): Number of experts for MoE
num_experts_per_tok (int): Number of experts choice per token
"""
super(TransformerDecoderLayerBase, self).__init__()
if self_attn_type in ["scaled-dot", "scaled-dot-flash"]:
self.self_attn = MultiHeadedAttention(
heads,
d_model,
dropout=attention_dropout,
max_relative_positions=max_relative_positions,
relative_positions_buckets=relative_positions_buckets,
rotary_interleave=rotary_interleave,
rotary_theta=rotary_theta,
rotary_dim=rotary_dim,
attn_type="self",
self_attn_type=self_attn_type,
add_qkvbias=add_qkvbias,
num_kv=num_kv,
use_ckpting=use_ckpting,
parallel_gpu=parallel_gpu,
)
elif self_attn_type == "average":
self.self_attn = AverageAttention(
d_model, dropout=attention_dropout, aan_useffn=aan_useffn
)
if num_experts > 0:
self.feed_forward = MoE(
num_experts,
num_experts_per_tok,
d_model,
d_ff,
dropout,
pos_ffn_activation_fn,
add_ffnbias,
parallel_residual,
layer_norm,
norm_eps,
use_ckpting=use_ckpting,
parallel_gpu=parallel_gpu,
)
else:
self.feed_forward = PositionwiseFeedForward(
d_model,
d_ff,
dropout,
pos_ffn_activation_fn,
add_ffnbias,
parallel_residual,
layer_norm,
norm_eps,
use_ckpting=use_ckpting,
parallel_gpu=parallel_gpu,
)
self.parallel_residual = parallel_residual
self.shared_layer_norm = shared_layer_norm
if layer_norm == "standard":
self.layer_norm_1 = nn.LayerNorm(d_model, eps=norm_eps)
if parallel_residual and not shared_layer_norm:
self.layer_norm_res = nn.LayerNorm(d_model, eps=norm_eps)
elif layer_norm == "rms":
self.layer_norm_1 = RMSNorm(d_model, eps=norm_eps)
if parallel_residual and not shared_layer_norm:
self.layer_norm_res = RMSNorm(d_model, eps=norm_eps)
else:
raise ValueError(f"{layer_norm} layer norm type is not supported")
self.dropout = nn.Dropout(dropout)
self.dropout_p = dropout
self.full_context_alignment = full_context_alignment
self.alignment_heads = alignment_heads
self.sliding_window = sliding_window
self.self_attn_type = self_attn_type
def forward(self, *args, **kwargs):
"""Extend `_forward` for (possibly) multiple decoder pass:
Always a default (future masked) decoder forward pass,
Possibly a second future aware decoder pass for joint learn
full context alignement, :cite:`garg2019jointly`.
Args:
* All arguments of _forward, of which
with_align (bool): needed to compute attn_align
return_attn (bool): to force MHA to return attns
Returns:
(FloatTensor, FloatTensor, FloatTensor or None):
* layer_out ``(batch_size, T, model_dim)``
* top_attn ``(batch_size, T, src_len)``
* attn_align ``(batch_size, T, src_len)`` or None
"""
with_align = kwargs.pop("with_align", False)
layer_out, attns = self._forward(*args, **kwargs)
top_attn = None if attns is None else attns[:, 0, :, :].contiguous()
attn_align = None
if with_align:
if self.full_context_alignment:
# return _, (B, Q_len, K_len)
_, attns = self._forward(*args, **kwargs, future=True)
if self.alignment_heads > 0:
attns = attns[:, : self.alignment_heads, :, :].contiguous()
# layer average attention across heads, get ``(B, Q, K)``
# Case 1: no full_context, no align heads -> layer avg baseline
# Case 2: no full_context, 1 align heads -> guided align
# Case 3: full_context, 1 align heads -> full cte guided align
attn_align = attns.mean(dim=1)
return layer_out, top_attn, attn_align
def update_dropout(self, dropout, attention_dropout):
self.self_attn.update_dropout(attention_dropout)
self.feed_forward.update_dropout(dropout)
self.dropout.p = dropout
def _forward(self, *args, **kwargs):
raise NotImplementedError
def _compute_dec_mask(self, tgt_pad_mask, future):
tgt_len = tgt_pad_mask.size(-1)
if not future:
# Add triangular future_mask and pad_mask, result mask in (B, T, T).
future_mask = torch.ones(
[tgt_len, tgt_len],
device=tgt_pad_mask.device,
dtype=torch.uint8,
)
future_mask = future_mask.tril_(0)
if self.sliding_window > 0:
future_mask = future_mask.triu_(-self.sliding_window)
future_mask = future_mask.bool()
future_mask = ~future_mask.view(1, tgt_len, tgt_len)
# Patch for scaled dot product attention.
patch_mask = ~torch.all(
tgt_pad_mask + future_mask, dim=2, keepdim=True
).expand_as(tgt_pad_mask + future_mask)
dec_mask = torch.gt(tgt_pad_mask + future_mask, 0)
dec_mask = torch.logical_and(dec_mask, patch_mask)
else:
# Only mask padding, result mask in (B, 1, T).
dec_mask = tgt_pad_mask
return dec_mask
def _forward_self_attn(self, norm_layer_in, dec_mask, step, return_attn=False):
if self.self_attn_type in ["scaled-dot", "scaled-dot-flash"]:
return self.self_attn(
norm_layer_in,
norm_layer_in,
norm_layer_in,
mask=dec_mask,
sliding_window=self.sliding_window,
step=step,
return_attn=return_attn,
)
elif self.self_attn_type == "average":
return self.self_attn(norm_layer_in, mask=dec_mask, step=step)
else:
raise ValueError(f"self attention {type(self.self_attn)} not supported")
class TransformerDecoderLayer(TransformerDecoderLayerBase):
"""Transformer Decoder layer block in Pre-Norm style.
Pre-Norm style is an improvement w.r.t. Original paper's Post-Norm style,
providing better converge speed and performance. This is also the actual
implementation in tensor2tensor and also avalable in fairseq.
See https://tunz.kr/post/4 and :cite:`DeeperTransformer`.
"""
def __init__(
self,
d_model,
heads,
d_ff,
dropout,
attention_dropout,
self_attn_type="scaled-dot",
max_relative_positions=0,
relative_positions_buckets=0,
aan_useffn=False,
full_context_alignment=False,
alignment_heads=0,
pos_ffn_activation_fn=ActivationFunction.relu,
add_qkvbias=False,
num_kv=0,
add_ffnbias=True,
parallel_residual=False,
shared_layer_norm=False,
layer_norm="standard",
norm_eps=1e-6,
use_ckpting=[],
parallel_gpu=1,
sliding_window=0,
rotary_interleave=True,
rotary_theta=1e4,
rotary_dim=0,
num_experts=0,
num_experts_per_tok=2,
):
"""
Args:
See TransformerDecoderLayerBase
"""
super(TransformerDecoderLayer, self).__init__(
d_model,
heads,
d_ff,
dropout,
attention_dropout,
self_attn_type,
max_relative_positions,
relative_positions_buckets,
aan_useffn,
full_context_alignment,
alignment_heads,
pos_ffn_activation_fn=pos_ffn_activation_fn,
add_qkvbias=add_qkvbias,
num_kv=num_kv,
add_ffnbias=add_ffnbias,
parallel_residual=parallel_residual,
shared_layer_norm=shared_layer_norm,
layer_norm=layer_norm,
norm_eps=norm_eps,
use_ckpting=use_ckpting,
parallel_gpu=parallel_gpu,
sliding_window=sliding_window,
rotary_interleave=rotary_interleave,
rotary_theta=rotary_theta,
rotary_dim=rotary_dim,
num_experts=num_experts,
num_experts_per_tok=num_experts_per_tok,
)
self.context_attn = MultiHeadedAttention(
heads,
d_model,
dropout=attention_dropout,
attn_type="context",
self_attn_type=self.self_attn_type,
add_qkvbias=add_qkvbias,
num_kv=num_kv,
use_ckpting=use_ckpting,
parallel_gpu=parallel_gpu,
)
if layer_norm == "standard":
self.layer_norm_2 = nn.LayerNorm(d_model, eps=norm_eps)
elif layer_norm == "rms":
self.layer_norm_2 = RMSNorm(d_model, eps=norm_eps)
else:
raise ValueError(f"{layer_norm} layer norm type is not supported")
def update_dropout(self, dropout, attention_dropout):
super(TransformerDecoderLayer, self).update_dropout(dropout, attention_dropout)
self.context_attn.update_dropout(attention_dropout)
def _forward(
self,
layer_in,
enc_out,
src_pad_mask,
tgt_pad_mask,
step=None,
future=False,
return_attn=False,
):
"""A naive forward pass for transformer decoder.
# T: could be 1 in the case of stepwise decoding or tgt_len
Args:
layer_in (FloatTensor): ``(batch_size, T, model_dim)``
enc_out (FloatTensor): ``(batch_size, src_len, model_dim)``
src_pad_mask (bool): ``(batch_size, 1, src_len)``
tgt_pad_mask (bool): ``(batch_size, 1, T)``
step (int or None): stepwise decoding counter
future (bool): If set True, do not apply future_mask.
return_attn (bool) : if set True requires attns output
Returns:
(FloatTensor, FloatTensor):
* layer_out ``(batch_size, T, model_dim)``
* attns ``(batch_size, head, T, src_len)``
"""
dec_mask = None
src_pad_mask = src_pad_mask.unsqueeze(1) # [B,1,1,slen]
if layer_in.size(1) > 1:
# masking is necessary when sequence length is greater than one
dec_mask = self._compute_dec_mask(tgt_pad_mask, future)
dec_mask = dec_mask.unsqueeze(1)
dec_mask = dec_mask.expand(-1, -1, dec_mask.size(3), -1)
src_pad_mask = src_pad_mask.expand(-1, -1, dec_mask.size(3), -1)
# mask now are (batch x 1 x tlen x s or t len)
# 1 = heads to be expanded in MHA
norm_layer_in = self.layer_norm_1(layer_in)
self_attn, _ = self._forward_self_attn(
norm_layer_in, dec_mask, step, return_attn=return_attn
)
if self.dropout_p > 0:
self_attn = self.dropout(self_attn)
if self.parallel_residual:
ctx_attn, attns = self.context_attn(
enc_out,
enc_out,
norm_layer_in,
mask=src_pad_mask,
return_attn=return_attn,
)
# feed_forward applies residual, so we remove and apply residual with un-normed
layer_out = (
self.feed_forward(norm_layer_in)
- norm_layer_in
+ layer_in
+ self_attn
+ ctx_attn
)
else:
query = self_attn + layer_in
norm_query = self.layer_norm_2(query)
ctx_attn, attns = self.context_attn(
enc_out, enc_out, norm_query, mask=src_pad_mask, return_attn=return_attn
)
if self.dropout_p > 0:
ctx_attn = self.dropout(ctx_attn)
layer_out = self.feed_forward(ctx_attn + query)
return layer_out, attns
class TransformerDecoderBase(DecoderBase):
def __init__(
self, d_model, copy_attn, embeddings, alignment_layer, layer_norm, norm_eps
):
super(TransformerDecoderBase, self).__init__()
self.embeddings = embeddings
# Decoder State
self.state = {}
# previously, there was a GlobalAttention module here for copy
# attention. But it was never actually used -- the "copy" attention
# just reuses the context attention.
self._copy = copy_attn
if layer_norm == "standard":
self.layer_norm = nn.LayerNorm(d_model, eps=norm_eps)
elif layer_norm == "rms":
self.layer_norm = RMSNorm(d_model, eps=norm_eps)
else:
raise ValueError(f"{layer_norm} layer norm type is not supported")
self.alignment_layer = alignment_layer
@classmethod
def from_opt(cls, opt, embeddings):
"""Alternate constructor."""
return cls(
opt.dec_layers,
opt.dec_hid_size,
opt.heads,
opt.transformer_ff,
opt.copy_attn,
opt.self_attn_type,
opt.dropout[0] if type(opt.dropout) is list else opt.dropout,
opt.attention_dropout[0]
if type(opt.attention_dropout) is list
else opt.attention_dropout,
embeddings,
opt.max_relative_positions,
opt.relative_positions_buckets,
opt.aan_useffn,
opt.full_context_alignment,
opt.alignment_layer,
alignment_heads=opt.alignment_heads,
pos_ffn_activation_fn=opt.pos_ffn_activation_fn,
add_qkvbias=opt.add_qkvbias,
num_kv=opt.num_kv,
add_ffnbias=opt.add_ffnbias,
parallel_residual=opt.parallel_residual,
shared_layer_norm=opt.shared_layer_norm,
layer_norm=opt.layer_norm,
norm_eps=opt.norm_eps,
use_ckpting=opt.use_ckpting,
parallel_gpu=opt.world_size
if opt.parallel_mode == "tensor_parallel"
else 1,
sliding_window=opt.sliding_window,
rotary_interleave=opt.rotary_interleave,
rotary_theta=opt.rotary_theta,
rotary_dim=opt.rotary_dim,
num_experts=opt.num_experts,
num_experts_per_tok=opt.num_experts_per_tok,
)
def init_state(self, src, enc_out, enc_final_hs):
"""Initialize decoder state."""
self.state["src"] = src
def map_state(self, fn):
if self.state["src"] is not None:
self.state["src"] = fn(self.state["src"], 0)
for layer in self.transformer_layers:
if hasattr(layer, "context_attn"):
if layer.context_attn.layer_cache[1]["keys"].numel() != 0:
x = fn(layer.context_attn.layer_cache[1]["keys"], 0)
y = fn(layer.context_attn.layer_cache[1]["values"], 0)
layer.context_attn.layer_cache = True, {"keys": x, "values": y}
if isinstance(layer.self_attn, AverageAttention):
if layer.self_attn.layer_cache[1]["prev_g"].numel() != 0:
x = fn(layer.self_attn.layer_cache[1]["prev_g"], 0)
layer.self_attn.layer_cache = True, {"prev_g": x}
else:
if layer.self_attn.layer_cache[1]["keys"].numel() != 0:
x = fn(layer.self_attn.layer_cache[1]["keys"], 0)
y = fn(layer.self_attn.layer_cache[1]["values"], 0)
if (
layer.self_attn.layer_cache[1].get("key_pad_mask", None)
is not None
):
z = fn(layer.self_attn.layer_cache[1]["key_pad_mask"], 0)
else:
z = None
layer.self_attn.layer_cache = True, {
"keys": x,
"values": y,
"key_pad_mask": z,
}
def detach_state(self):
raise NotImplementedError
def forward(self, *args, **kwargs):
raise NotImplementedError
def update_dropout(self, dropout, attention_dropout):
self.embeddings.update_dropout(dropout)
for layer in self.transformer_layers:
layer.update_dropout(dropout, attention_dropout)
class TransformerLMDecoderLayer(TransformerDecoderLayerBase):
"""Transformer Decoder only layer block in GPT style.
Args:
See TransformerDecoderLayerBase
"""
def _forward(
self, layer_in, tgt_pad_mask, step=None, future=False, return_attn=False
):
"""A naive forward pass for transformer decoder.
# T: could be 1 in the case of stepwise decoding or tgt_len
Args:
layer_in (FloatTensor): ``(batch_size, T, model_dim)``
tgt_pad_mask (bool): ``(batch_size, 1, T)``
layer_cache (dict or None): cached layer info when stepwise decode
step (int or None): stepwise decoding counter
future (bool): If set True, do not apply future_mask.
return_attn (bool): If set True return attn
Returns:
(FloatTensor, FloatTensor):
* layer_out ``(batch_size, T, model_dim)``
* attns ``(batch_size, head, T, T)``
"""
dec_mask = None
if layer_in.size(1) > 1:
# Masking is necessary when sequence length is greater than one
# The decoding has not started yet,
# we compute the scores on the source tokens in one shot.
dec_mask = self._compute_dec_mask(tgt_pad_mask, future)
dec_mask = dec_mask.unsqueeze(1)
dec_mask = dec_mask.expand(-1, -1, dec_mask.size(3), -1)
# mask now are (batch x 1 x tlen x tlen)
# 1 = heads to be expanded in MHA
norm_layer_in = self.layer_norm_1(layer_in)
attn_output, attns = self._forward_self_attn(
norm_layer_in, dec_mask, step, return_attn=return_attn
)
if self.dropout_p > 0:
attn_output = self.dropout(attn_output)
if self.parallel_residual:
# feed_forward applies residual, so we remove and apply residual with un-normed
if not self.shared_layer_norm:
norm_res_layer_in = self.layer_norm_res(layer_in)
ff_in = norm_res_layer_in
else:
ff_in = norm_layer_in
layer_out = self.feed_forward(ff_in) - ff_in + layer_in + attn_output
else:
layer_out = attn_output + layer_in
layer_out = self.feed_forward(layer_out)
return layer_out, attns
class TransformerLMDecoder(TransformerDecoderBase):
"""The Transformer decoder from GPT-2
Args:
num_layers (int): number of decoder layers.
d_model (int): size of the model
heads (int): number of heads
d_ff (int): size of the inner FF layer
copy_attn (bool): if using a separate copy attention
self_attn_type (str): type of self-attention scaled-dot, scaled-dot-flash, average
dropout (float): dropout in residual, self-attn(dot) and feed-forward
attention_dropout (float): dropout in context_attn (and self-attn(avg))
embeddings (onmt.modules.Embeddings):
embeddings to use, should have positional encodings
max_relative_positions (int):
Max distance between inputs in relative positions representations
relative_positions_buckets (int):
Number of buckets when using Relative positions bias
aan_useffn (bool): Turn on the FFN layer in the AAN decoder
full_context_alignment (bool):
whether enable an extra full context decoder forward for alignment
alignment_layer (int): N° Layer to supervise with for alignment guiding
alignment_heads (int):
N. of cross attention heads to use for alignment guiding
pos_ffn_activation_fn (ActivationFunction):
activation function choice for PositionwiseFeedForward layer
add_qkvbias (bool): whether to add bias to the Key/Value nn.Linear
num_kv (int): number of heads for KV when different vs Q (multiquery)
add_ffnbias (bool): whether to add bias to the FF nn.Linear
parallel_residual (bool): Use parallel residual connections in each layer block, as used
by the GPT-J and GPT-NeoX models
shared_layer_norm (bool): When using parallel residual, share the input and post
attention layer norms.
layer_norm (string): type of layer normalization standard/rms
norm_eps (float): layer norm epsilon
use_ckpting (List): layers for which we checkpoint for backward
parallel_gpu (int): Number of gpu for tensor parallelism
sliding_window (int): Width of the band mask and KV cache (cf Mistral Model)
rotary_interleave (bool): Interleave the head dimensions when rotary embeddings are applied
rotary_theta (int): rotary base theta
rotary_dim (int): in some cases the rotary dim is lower than head dim
num_experts (int): Number of experts for MoE
num_experts_per_tok (int): Number of experts choice per token
"""
def __init__(
self,
num_layers,
d_model,
heads,
d_ff,
copy_attn,
self_attn_type,
dropout,
attention_dropout,
embeddings,
max_relative_positions,
relative_positions_buckets,
aan_useffn,
full_context_alignment=None,
alignment_layer=None,
alignment_heads=None,
pos_ffn_activation_fn=ActivationFunction.relu,
add_qkvbias=False,
num_kv=0,
add_ffnbias=True,
parallel_residual=False,
shared_layer_norm=False,
layer_norm="standard",
norm_eps=1e-6,
use_ckpting=[],
parallel_gpu=1,
sliding_window=0,
rotary_interleave=True,
rotary_theta=1e4,
rotary_dim=0,
num_experts=0,
num_experts_per_tok=2,
):
super(TransformerLMDecoder, self).__init__(
d_model, copy_attn, embeddings, alignment_layer, layer_norm, norm_eps
)
self.transformer_layers = nn.ModuleList(
[
TransformerLMDecoderLayer(
d_model,
heads,
d_ff,
dropout,
attention_dropout,
self_attn_type=self_attn_type,
max_relative_positions=max_relative_positions,
relative_positions_buckets=relative_positions_buckets,
aan_useffn=aan_useffn,
full_context_alignment=None,
alignment_heads=None,
pos_ffn_activation_fn=pos_ffn_activation_fn,
add_qkvbias=add_qkvbias,
num_kv=num_kv,
add_ffnbias=add_ffnbias,
parallel_residual=parallel_residual,
shared_layer_norm=shared_layer_norm,
layer_norm=layer_norm,
norm_eps=norm_eps,
use_ckpting=use_ckpting,
parallel_gpu=parallel_gpu,
sliding_window=sliding_window,
rotary_interleave=rotary_interleave,
rotary_theta=rotary_theta,
rotary_dim=rotary_dim,
num_experts=num_experts,
num_experts_per_tok=num_experts_per_tok,
)
for i in range(num_layers)
]
)
def init_state(self, src=None, enc_out=None, enc_final_hs=None):
super(TransformerLMDecoder, self).init_state(None, None, None)
def detach_state(self):
pass
def forward(self, tgt, enc_out=None, step=None, **kwargs):
"""Decode, possibly stepwise."""
if step == 0:
# decoding mode.
# Initialize KV and key_pad_mask cache.
self._init_cache(tgt)
elif step is None:
# training mode.
for layer in self.transformer_layers:
layer.self_attn.layer_cache = (
False,
{
"keys": torch.tensor([]),
"values": torch.tensor([]),
"key_pad_mask": None,
},
)
dec_out = self.embeddings(tgt, step=step)
assert dec_out.dim() == 3 # batch x len x embedding_dim
pad_idx = self.embeddings.word_padding_idx
tgt_pad_mask = tgt[:, :, 0].eq(pad_idx).unsqueeze(1) # [B, 1, T_tgt]
with_align = kwargs.pop("with_align", False)
return_attn = kwargs.pop("return_attn", False)
return_attn = with_align or self._copy or return_attn
assert not with_align, "TransformerLMDecoder does not support align"
for layer in self.transformer_layers:
dec_out, attn, _ = layer(
dec_out,
tgt_pad_mask,
step=step,
with_align=with_align,
return_attn=return_attn,
)
dec_out = self.layer_norm(dec_out)
attns = {"std": attn}
if self._copy:
attns["copy"] = attn
# TODO change the way attns is returned dict => list or tuple (onnx)
return dec_out, attns
def _init_cache(self, tgt=None):
for layer in self.transformer_layers:
if hasattr(layer, "self_attn"):
if isinstance(layer.self_attn, AverageAttention):
raise NotImplementedError
else:
layer.self_attn.layer_cache = (
True,
{
"keys": torch.tensor([], device=tgt.device),
"values": torch.tensor([], device=tgt.device),
"key_pad_mask": tgt[:, :, 0]
.eq(self.embeddings.word_padding_idx)
.unsqueeze(1),
},
)
if hasattr(layer.self_attn, "rope"):
layer.self_attn.rope = layer.self_attn.rope.to(tgt.device)
layer.self_attn.cos = layer.self_attn.cos.to(tgt.device)
layer.self_attn.sin = layer.self_attn.sin.to(tgt.device)