304 lines
10 KiB
Python
304 lines
10 KiB
Python
# Copyright (c) 2019 NVIDIA CORPORATION. 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.
|
|
|
|
from typing import List, Optional
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from common.utils import mask_from_lens
|
|
from common.text.symbols import pad_idx, symbols
|
|
|
|
|
|
class NoOp(nn.Module):
|
|
def forward(self, x):
|
|
return x
|
|
|
|
|
|
class PositionalEmbedding(nn.Module):
|
|
def __init__(self, demb):
|
|
super(PositionalEmbedding, self).__init__()
|
|
self.demb = demb
|
|
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
|
|
self.register_buffer('inv_freq', inv_freq)
|
|
|
|
def forward(self, pos_seq, bsz: Optional[int] = None):
|
|
sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
|
|
pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=1)
|
|
if bsz is not None:
|
|
return pos_emb[None, :, :].expand(bsz, -1, -1)
|
|
else:
|
|
return pos_emb[None, :, :]
|
|
|
|
|
|
class PositionwiseFF(nn.Module):
|
|
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False):
|
|
super(PositionwiseFF, self).__init__()
|
|
|
|
self.d_model = d_model
|
|
self.d_inner = d_inner
|
|
self.dropout = dropout
|
|
|
|
self.CoreNet = nn.Sequential(
|
|
nn.Linear(d_model, d_inner), nn.ReLU(),
|
|
nn.Dropout(dropout),
|
|
nn.Linear(d_inner, d_model),
|
|
nn.Dropout(dropout),
|
|
)
|
|
|
|
self.layer_norm = nn.LayerNorm(d_model)
|
|
self.pre_lnorm = pre_lnorm
|
|
|
|
def forward(self, inp):
|
|
if self.pre_lnorm:
|
|
# layer normalization + positionwise feed-forward
|
|
core_out = self.CoreNet(self.layer_norm(inp))
|
|
|
|
# residual connection
|
|
output = core_out + inp
|
|
else:
|
|
# positionwise feed-forward
|
|
core_out = self.CoreNet(inp)
|
|
|
|
# residual connection + layer normalization
|
|
output = self.layer_norm(inp + core_out)
|
|
|
|
return output
|
|
|
|
|
|
class PositionwiseConvFF(nn.Module):
|
|
def __init__(self, d_model, d_inner, kernel_size, dropout, pre_lnorm=False):
|
|
super(PositionwiseConvFF, self).__init__()
|
|
|
|
self.d_model = d_model
|
|
self.d_inner = d_inner
|
|
self.dropout = dropout
|
|
|
|
self.CoreNet = nn.Sequential(
|
|
nn.Conv1d(d_model, d_inner, kernel_size, 1, (kernel_size // 2)),
|
|
nn.ReLU(),
|
|
# nn.Dropout(dropout), # worse convergence
|
|
nn.Conv1d(d_inner, d_model, kernel_size, 1, (kernel_size // 2)),
|
|
nn.Dropout(dropout),
|
|
)
|
|
self.layer_norm = nn.LayerNorm(d_model)
|
|
self.pre_lnorm = pre_lnorm
|
|
|
|
def forward(self, inp):
|
|
if self.pre_lnorm:
|
|
# layer normalization + positionwise feed-forward
|
|
core_out = inp.transpose(1, 2)
|
|
core_out = self.CoreNet(self.layer_norm(core_out))
|
|
core_out = core_out.transpose(1, 2)
|
|
|
|
# residual connection
|
|
output = core_out + inp
|
|
else:
|
|
# positionwise feed-forward
|
|
core_out = inp.transpose(1, 2)
|
|
core_out = self.CoreNet(core_out)
|
|
core_out = core_out.transpose(1, 2)
|
|
|
|
# residual connection + layer normalization
|
|
output = self.layer_norm(inp + core_out)
|
|
|
|
return output
|
|
|
|
|
|
class MultiHeadAttn(nn.Module):
|
|
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1,
|
|
pre_lnorm=False):
|
|
super(MultiHeadAttn, self).__init__()
|
|
|
|
self.n_head = n_head
|
|
self.d_model = d_model
|
|
self.d_head = d_head
|
|
self.scale = 1 / (d_head ** 0.5)
|
|
self.dropout = dropout
|
|
self.pre_lnorm = pre_lnorm
|
|
|
|
self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head)
|
|
self.drop = nn.Dropout(dropout)
|
|
self.dropatt = nn.Dropout(dropatt)
|
|
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
|
|
self.layer_norm = nn.LayerNorm(d_model)
|
|
|
|
|
|
def forward(self, inp, attn_mask: Optional[torch.Tensor] = None):
|
|
residual = inp
|
|
|
|
if self.pre_lnorm:
|
|
# layer normalization
|
|
inp = self.layer_norm(inp)
|
|
|
|
n_head, d_head = self.n_head, self.d_head
|
|
|
|
head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=-1)
|
|
head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head)
|
|
head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head)
|
|
head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head)
|
|
|
|
q = head_q.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
|
|
k = head_k.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
|
|
v = head_v.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
|
|
|
|
attn_score = torch.bmm(q, k.transpose(1, 2))
|
|
attn_score.mul_(self.scale)
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.unsqueeze(1)
|
|
attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1)
|
|
attn_score.masked_fill_(attn_mask, -float('inf'))
|
|
|
|
attn_prob = F.softmax(attn_score, dim=2)
|
|
attn_prob = self.dropatt(attn_prob)
|
|
attn_vec = torch.bmm(attn_prob, v)
|
|
|
|
attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head)
|
|
attn_vec = attn_vec.permute(1, 2, 0, 3).contiguous().view(
|
|
inp.size(0), inp.size(1), n_head * d_head)
|
|
|
|
# linear projection
|
|
attn_out = self.o_net(attn_vec)
|
|
attn_out = self.drop(attn_out)
|
|
|
|
if self.pre_lnorm:
|
|
# residual connection
|
|
output = residual + attn_out
|
|
else:
|
|
# residual connection + layer normalization
|
|
|
|
# XXX Running TorchScript on 20.02 and 20.03 containers crashes here
|
|
# XXX Works well with 20.01-py3 container.
|
|
# XXX dirty fix is:
|
|
# XXX output = self.layer_norm(residual + attn_out).half()
|
|
output = self.layer_norm(residual + attn_out)
|
|
|
|
return output
|
|
|
|
# disabled; slower
|
|
def forward_einsum(self, h, attn_mask=None):
|
|
# multihead attention
|
|
# [hlen x bsz x n_head x d_head]
|
|
|
|
c = h
|
|
|
|
if self.pre_lnorm:
|
|
# layer normalization
|
|
c = self.layer_norm(c)
|
|
|
|
head_q = self.q_net(h)
|
|
head_k, head_v = torch.chunk(self.kv_net(c), 2, -1)
|
|
|
|
head_q = head_q.view(h.size(0), h.size(1), self.n_head, self.d_head)
|
|
head_k = head_k.view(c.size(0), c.size(1), self.n_head, self.d_head)
|
|
head_v = head_v.view(c.size(0), c.size(1), self.n_head, self.d_head)
|
|
|
|
# [bsz x n_head x qlen x klen]
|
|
# attn_score = torch.einsum('ibnd,jbnd->bnij', (head_q, head_k))
|
|
attn_score = torch.einsum('bind,bjnd->bnij', (head_q, head_k))
|
|
attn_score.mul_(self.scale)
|
|
if attn_mask is not None and attn_mask.any().item():
|
|
attn_score.masked_fill_(attn_mask[:, None, None, :], -float('inf'))
|
|
|
|
# [bsz x qlen x klen x n_head]
|
|
attn_prob = F.softmax(attn_score, dim=3)
|
|
attn_prob = self.dropatt(attn_prob)
|
|
|
|
# [bsz x n_head x qlen x klen] * [klen x bsz x n_head x d_head]
|
|
# -> [qlen x bsz x n_head x d_head]
|
|
attn_vec = torch.einsum('bnij,bjnd->bind', (attn_prob, head_v))
|
|
attn_vec = attn_vec.contiguous().view(
|
|
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
|
|
|
|
# linear projection
|
|
attn_out = self.o_net(attn_vec)
|
|
attn_out = self.drop(attn_out)
|
|
|
|
if self.pre_lnorm:
|
|
# residual connection
|
|
output = h + attn_out
|
|
else:
|
|
# residual connection + layer normalization
|
|
output = self.layer_norm(h + attn_out)
|
|
|
|
return output
|
|
|
|
|
|
class TransformerLayer(nn.Module):
|
|
def __init__(self, n_head, d_model, d_head, d_inner, kernel_size, dropout,
|
|
**kwargs):
|
|
super(TransformerLayer, self).__init__()
|
|
|
|
self.dec_attn = MultiHeadAttn(n_head, d_model, d_head, dropout, **kwargs)
|
|
self.pos_ff = PositionwiseConvFF(d_model, d_inner, kernel_size, dropout,
|
|
pre_lnorm=kwargs.get('pre_lnorm'))
|
|
|
|
def forward(self, dec_inp, mask):
|
|
output = self.dec_attn(dec_inp, attn_mask=~mask.squeeze(2))
|
|
output *= mask
|
|
output = self.pos_ff(output)
|
|
output *= mask
|
|
return output
|
|
|
|
|
|
class FFTransformer(nn.Module):
|
|
pad_idx = 0 # XXX
|
|
|
|
def __init__(self, n_layer, n_head, d_model, d_head, d_inner, kernel_size,
|
|
dropout, dropatt, dropemb=0.0, embed_input=True, d_embed=None,
|
|
pre_lnorm=False):
|
|
super(FFTransformer, self).__init__()
|
|
self.d_model = d_model
|
|
self.n_head = n_head
|
|
self.d_head = d_head
|
|
|
|
self.embed_input = embed_input
|
|
if embed_input:
|
|
self.word_emb = nn.Embedding(len(symbols), d_embed or d_model,
|
|
padding_idx=FFTransformer.pad_idx)
|
|
else:
|
|
self.word_emb = NoOp()
|
|
|
|
self.pos_emb = PositionalEmbedding(self.d_model)
|
|
self.drop = nn.Dropout(dropemb)
|
|
self.layers = nn.ModuleList()
|
|
|
|
for _ in range(n_layer):
|
|
self.layers.append(
|
|
TransformerLayer(
|
|
n_head, d_model, d_head, d_inner, kernel_size, dropout,
|
|
dropatt=dropatt, pre_lnorm=pre_lnorm)
|
|
)
|
|
|
|
def forward(self, dec_inp, seq_lens: Optional[torch.Tensor] = None):
|
|
if self.embed_input:
|
|
inp = self.word_emb(dec_inp)
|
|
# [bsz x L x 1]
|
|
# mask = (dec_inp != FFTransformer.pad_idx).unsqueeze(2)
|
|
mask = (dec_inp != 0).unsqueeze(2)
|
|
else:
|
|
inp = dec_inp
|
|
assert seq_lens is not None
|
|
mask = mask_from_lens(seq_lens).unsqueeze(2)
|
|
pos_seq = torch.arange(inp.size(1), device=inp.device, dtype=inp.dtype)
|
|
pos_emb = self.pos_emb(pos_seq) * mask
|
|
out = self.drop(inp + pos_emb)
|
|
|
|
for layer in self.layers:
|
|
out = layer(out, mask=mask)
|
|
|
|
# out = self.drop(out)
|
|
return out, mask
|