File size: 16,521 Bytes
e9f9fd3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 | from ...torch_core import *
from ...layers import *
from .awd_lstm import RNNDropout, LinearDecoder, SequentialRNN
__all__ = ['Activation', 'PositionalEncoding', 'GeLU', 'Swish', 'feed_forward', 'MultiHeadAttention', 'MultiHeadRelativeAttention',
'DecoderLayer', 'Transformer', 'TransformerXL', 'tfmer_lm_config', 'tfmer_clas_config', 'tfmer_lm_split', 'tfmer_clas_split',
'tfmerXL_lm_config', 'tfmerXL_clas_config', 'tfmerXL_lm_split', 'tfmerXL_clas_split']
Activation = Enum('Activation', 'ReLU Swish GeLU')
class PositionalEncoding(Module):
"Encode the position with a sinusoid."
def __init__(self, d:int): self.register_buffer('freq', 1 / (10000 ** (torch.arange(0., d, 2.)/d)))
def forward(self, pos:Tensor):
inp = torch.ger(pos, self.freq)
enc = torch.cat([inp.sin(), inp.cos()], dim=-1)
return enc
class GeLU(Module):
def forward(self, x): return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
class Swish(Module):
def forward(self, x): return x * torch.sigmoid(x)
_activ_func = {Activation.ReLU:nn.ReLU(inplace=True), Activation.GeLU:GeLU(), Activation.Swish: Swish()}
def feed_forward(d_model:int, d_ff:int, ff_p:float=0., act:Activation=Activation.ReLU, double_drop:bool=True):
layers = [nn.Linear(d_model, d_ff), _activ_func[act]]
if double_drop: layers.append(nn.Dropout(ff_p))
return SequentialEx(*layers, nn.Linear(d_ff, d_model), nn.Dropout(ff_p), MergeLayer(), nn.LayerNorm(d_model))
class MultiHeadAttention(Module):
"MutiHeadAttention."
def __init__(self, n_heads:int, d_model:int, d_head:int=None, resid_p:float=0., attn_p:float=0., bias:bool=True,
scale:bool=True):
d_head = ifnone(d_head, d_model//n_heads)
self.n_heads,self.d_head,self.scale = n_heads,d_head,scale
self.attention = nn.Linear(d_model, 3 * n_heads * d_head, bias=bias)
self.out = nn.Linear(n_heads * d_head, d_model, bias=bias)
self.drop_att,self.drop_res = nn.Dropout(attn_p),nn.Dropout(resid_p)
self.ln = nn.LayerNorm(d_model)
def forward(self, x:Tensor, mask:Tensor=None, **kwargs):
return self.ln(x + self.drop_res(self.out(self._apply_attention(x, mask=mask, **kwargs))))
def _apply_attention(self, x:Tensor, mask:Tensor=None):
bs,x_len = x.size(0),x.size(1)
wq,wk,wv = torch.chunk(self.attention(x), 3, dim=-1)
wq,wk,wv = map(lambda x:x.view(bs, x.size(1), self.n_heads, self.d_head), (wq,wk,wv))
wq,wk,wv = wq.permute(0, 2, 1, 3),wk.permute(0, 2, 3, 1),wv.permute(0, 2, 1, 3)
attn_score = torch.matmul(wq, wk)
if self.scale: attn_score.div_(self.d_head ** 0.5)
if mask is not None:
attn_score = attn_score.float().masked_fill(mask, -float('inf')).type_as(attn_score)
attn_prob = self.drop_att(F.softmax(attn_score, dim=-1))
attn_vec = torch.matmul(attn_prob, wv)
return attn_vec.permute(0, 2, 1, 3).contiguous().contiguous().view(bs, x_len, -1)
def _attention_einsum(self, x, mask=None):
# Permute and matmul is a little bit faster but this implementation is more readable
bs,x_len = x.size(0),x.size(1)
wq,wk,wv = torch.chunk(self.attention(x), 3, dim=-1)
wq,wk,wv = map(lambda x:x.view(bs, x.size(1), self.n_heads, self.d_head), (wq,wk,wv))
attn_score = torch.einsum('bind,bjnd->bijn', (wq, wk))
if self.scale: attn_score.mul_(1/(self.d_head ** 0.5))
if mask is not None:
attn_score = attn_score.float().masked_fill(mask, -float('inf')).type_as(attn_score)
attn_prob = self.drop_att(F.softmax(attn_score, dim=2))
attn_vec = torch.einsum('bijn,bjnd->bind', (attn_prob, wv))
return attn_vec.contiguous().view(bs, x_len, -1)
#def _line_shift1(x:Tensor, mask:bool=False):
# "Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal."
# bs,n,p,nh = x.size()
# x_pad = torch.cat([x.new_zeros(bs,n,1,nh), x], dim=2)
# x_shift = x_pad.view(bs,p + 1,n,nh)[:,1:].view_as(x)
# if mask: x_shift.mul_(torch.tril(x.new_ones(n,p), p-n)[None,:,:,None])
# return x_shift
def _line_shift(x:Tensor, mask:bool=False):
"Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal."
bs,nh,n,p = x.size()
x_pad = torch.cat([x.new_zeros(bs,nh,n,1), x], dim=3)
x_shift = x_pad.view(bs,nh,p + 1,n)[:,:,1:].view_as(x)
if mask: x_shift.mul_(torch.tril(x.new_ones(n,p), p-n)[None,None,])
return x_shift
class MultiHeadRelativeAttention(MultiHeadAttention):
"MutiHeadAttention with relative positional encoding."
def __init__(self, n_heads:int, d_model:int, d_head:int, resid_p:float=0., attn_p:float=0., bias:bool=True,
scale:bool=True):
super().__init__(n_heads, d_model, d_head, resid_p=resid_p, attn_p=attn_p, bias=bias, scale=scale)
self.r_attn = nn.Linear(d_model, n_heads * d_head, bias=bias)
def _apply_attention(self, x:Tensor, r:Tensor=None, u:Tensor=None, v:Tensor=None, mask:Tensor=None, mem:Tensor=None):
#Notations from the paper: x input, r vector of relative distance between two elements, u et v learnable
#parameters of the model common between all layers, mask to avoid cheating and mem the previous hidden states.
bs,x_len,seq_len = x.size(0),x.size(1),r.size(0)
context = x if mem is None else torch.cat([mem, x], dim=1)
wq,wk,wv = torch.chunk(self.attention(context), 3, dim=-1)
wq = wq[:,-x_len:]
wq,wk,wv = map(lambda x:x.view(bs, x.size(1), self.n_heads, self.d_head), (wq,wk,wv))
wq,wk,wv = wq.permute(0, 2, 1, 3),wk.permute(0, 2, 3, 1),wv.permute(0, 2, 1, 3)
wkr = self.r_attn(r)
wkr = wkr.view(seq_len, self.n_heads, self.d_head)
wkr = wkr.permute(1,2,0)
#### compute attention score (AC is (a) + (c) and BS is (b) + (d) in the paper)
AC = torch.matmul(wq+u,wk)
BD = _line_shift(torch.matmul(wq+v, wkr))
if self.scale: attn_score = (AC + BD).mul_(1/(self.d_head ** 0.5))
if mask is not None:
attn_score = attn_score.float().masked_fill(mask, -float('inf')).type_as(attn_score)
attn_prob = self.drop_att(F.softmax(attn_score, dim=-1))
attn_vec = torch.matmul(attn_prob, wv)
return attn_vec.permute(0, 2, 1, 3).contiguous().view(bs, x_len, -1)
def _attention_einsum(self, x:Tensor, r:Tensor=None, u:Tensor=None, v:Tensor=None, mask:Tensor=None, mem:Tensor=None):
# Permute and matmul is a little bit faster but this implementation is more readable
bs,x_len,seq_len = x.size(0),x.size(1),r.size(0)
context = x if mem is None else torch.cat([mem, x], dim=1)
wq,wk,wv = torch.chunk(self.attention(context), 3, dim=-1)
wq = wq[:,-x_len:]
wkr = self.r_attn(r)
wq,wk,wv = map(lambda x:x.view(bs, x.size(1), self.n_heads, self.d_head), (wq,wk,wv))
wkr = wkr.view(seq_len, self.n_heads, self.d_head)
#### compute attention score (AC is (a) + (c) and BS is (b) + (d) in the paper)
AC = torch.einsum('bind,bjnd->bijn', (wq+u, wk))
BD = _line_shift1(torch.einsum('bind,jnd->bijn', (wq+v, wkr)))
attn_score = (AC + BD).mul_(1/(self.d_head ** 0.5))
if mask is not None:
attn_score = attn_score.float().masked_fill(mask, -float('inf')).type_as(attn_score)
attn_prob = self.drop_att(F.softmax(attn_score, dim=2))
attn_vec = torch.einsum('bijn,bjnd->bind', (attn_prob, wv))
return attn_vec.contiguous().view(bs, x_len, -1)
class DecoderLayer(Module):
"Basic block of a Transformer model."
#Can't use Sequential directly cause more than one input...
def __init__(self, n_heads:int, d_model:int, d_head:int, d_inner:int, resid_p:float=0., attn_p:float=0., ff_p:float=0.,
bias:bool=True, scale:bool=True, act:Activation=Activation.ReLU, double_drop:bool=True,
attn_cls:Callable=MultiHeadAttention):
self.mhra = attn_cls(n_heads, d_model, d_head, resid_p=resid_p, attn_p=attn_p, bias=bias, scale=scale)
self.ff = feed_forward(d_model, d_inner, ff_p=ff_p, act=act, double_drop=double_drop)
def forward(self, x:Tensor, mask:Tensor=None, **kwargs): return self.ff(self.mhra(x, mask=mask, **kwargs))
class Transformer(Module):
"Transformer model: https://arxiv.org/abs/1706.03762."
def __init__(self, vocab_sz:int, ctx_len:int, n_layers:int, n_heads:int, d_model:int, d_head:int, d_inner:int,
resid_p:float=0., attn_p:float=0., ff_p:float=0., embed_p:float=0., bias:bool=True, scale:bool=True,
act:Activation=Activation.ReLU, double_drop:bool=True, attn_cls:Callable=MultiHeadAttention,
learned_pos_enc:bool=True, mask:bool=True):
self.mask = mask
self.encoder = nn.Embedding(vocab_sz, d_model)
self.pos_enc = nn.Embedding(ctx_len, d_model) if learned_pos_enc else PositionalEncoding(d_model)
self.drop_emb = nn.Dropout(embed_p)
self.layers = nn.ModuleList([DecoderLayer(n_heads, d_model, d_head, d_inner, resid_p=resid_p, attn_p=attn_p,
ff_p=ff_p, bias=bias, scale=scale, act=act, double_drop=double_drop,
attn_cls=attn_cls) for k in range(n_layers)])
def reset(self): pass
def forward(self, x):
bs, x_len = x.size()
pos = torch.arange(0, x_len, device=x.device, dtype=x.dtype)
inp = self.drop_emb(self.encoder(x) + self.pos_enc(pos)[None]) #.mul_(self.d_model ** 0.5)
mask = torch.triu(x.new_ones(x_len, x_len), diagonal=1).byte()[None,None] if self.mask else None
#[None,:,:None] for einsum implementation of attention
for layer in self.layers: inp = layer(inp, mask=mask)
return ([inp],[inp]) #For the LinearDecoder
class TransformerXL(Module):
"TransformerXL model: https://arxiv.org/abs/1901.02860."
def __init__(self, vocab_sz:int, ctx_len:int, n_layers:int, n_heads:int, d_model:int, d_head:int, d_inner:int,
resid_p:float=0., attn_p:float=0., ff_p:float=0., embed_p:float=0., bias:bool=False, scale:bool=True,
act:Activation=Activation.ReLU, double_drop:bool=True, attn_cls:Callable=MultiHeadRelativeAttention,
learned_pos_enc:bool=False, mask:bool=True, mem_len:int=0):
self.encoder = nn.Embedding(vocab_sz, d_model)
self.pos_enc = nn.Embedding(ctx_len, d_model) if learned_pos_enc else PositionalEncoding(d_model)
self.drop_emb = nn.Dropout(embed_p)
self.u = nn.Parameter(torch.Tensor(n_heads, 1, d_head)) #Remove 1 for einsum implementation of attention
self.v = nn.Parameter(torch.Tensor(n_heads, 1, d_head)) #Remove 1 for einsum implementation of attention
self.mem_len,self.n_layers,self.d_model,self.mask = mem_len,n_layers,d_model,mask
self.init = False
self.layers = nn.ModuleList([DecoderLayer(n_heads, d_model, d_head, d_inner, resid_p=resid_p, attn_p=attn_p,
ff_p=ff_p, bias=bias, scale=scale, act=act, double_drop=double_drop,
attn_cls=attn_cls) for k in range(n_layers)])
def reset(self):
"Reset the internal memory."
self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)]
def _update_mems(self, hids):
if not getattr(self, 'hidden', False): return None
assert len(hids) == len(self.hidden), 'len(hids) != len(self.hidden)'
with torch.no_grad():
for i in range(len(hids)):
cat = torch.cat([self.hidden[i], hids[i]], dim=1)
self.hidden[i] = cat[:,-self.mem_len:].detach()
def select_hidden(self, idxs): self.hidden = [h[idxs] for h in self.hidden]
def forward(self, x):
#The hidden state has to be initiliazed in the forward pass for nn.DataParallel
if self.mem_len > 0 and not self.init:
self.reset()
self.init = True
bs,x_len = x.size()
inp = self.drop_emb(self.encoder(x)) #.mul_(self.d_model ** 0.5)
m_len = self.hidden[0].size(1) if hasattr(self, 'hidden') and len(self.hidden[0].size()) > 1 else 0
seq_len = m_len + x_len
mask = torch.triu(x.new_ones(x_len, seq_len), diagonal=1+m_len).byte()[None,None] if self.mask else None
#[None,:,:None] for einsum implementation of attention
hids = []
pos = torch.arange(seq_len-1, -1, -1, device=inp.device, dtype=inp.dtype)
pos_enc = self.pos_enc(pos)
hids.append(inp)
for i, layer in enumerate(self.layers):
mem = self.hidden[i] if self.mem_len > 0 else None
inp = layer(inp, r=pos_enc, u=self.u, v=self.v, mask=mask, mem=mem)
hids.append(inp)
core_out = inp[:,-x_len:]
if self.mem_len > 0 : self._update_mems(hids)
return (self.hidden if self.mem_len > 0 else [core_out]),[core_out]
def init_transformer(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
if hasattr(m, 'weight') and m.weight is not None: nn.init.normal_(m.weight, 0., 0.02)
if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias, 0.)
elif classname.find('LayerNorm') != -1:
if hasattr(m, 'weight') and m.weight is not None: nn.init.normal_(m.weight, 1., 0.02)
if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias, 0.)
elif classname.find('TransformerXL') != -1:
if hasattr(m, 'u'): nn.init.normal_(m.u, 0., 0.02)
if hasattr(m, 'v'): nn.init.normal_(m.v, 0., 0.02)
tfmer_lm_config = dict(ctx_len=512, n_layers=12, n_heads=12, d_model=768, d_head=64, d_inner=3072, resid_p=0.1, attn_p=0.1,
ff_p=0.1, embed_p=0.1, output_p=0., bias=True, scale=True, act=Activation.GeLU, double_drop=False,
tie_weights=True, out_bias=False, init=init_transformer, mask=True)
tfmer_clas_config = dict(ctx_len=512, n_layers=12, n_heads=12, d_model=768, d_head=64, d_inner=3072, resid_p=0.1, attn_p=0.1,
ff_p=0.1, embed_p=0.1, output_p=0., bias=True, scale=True, act=Activation.GeLU, double_drop=False,
init=init_transformer, mask=False)
def tfmer_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0]
n = len(encoder.layers)//3
groups = [list(encoder.layers[:n]), list(encoder.layers[n:2*n]), list(encoder.layers[2*n:])]
return groups + [[encoder.encoder, model[1]]]
def tfmer_clas_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0].module
n = len(encoder.layers)//3
groups = [[encoder.encoder], list(encoder.layers[:n]), list(encoder.layers[n:2*n]), list(encoder.layers[2*n:])]
return groups + [[model[1]]]
tfmerXL_lm_config = dict(ctx_len=150, n_layers=12, n_heads=10, d_model=410, d_head=41, d_inner=2100, resid_p=0.1, attn_p=0.1,
ff_p=0.1, embed_p=0.1, output_p=0.1, bias=False, scale=True, act=Activation.ReLU, double_drop=True,
tie_weights=True, out_bias=True, init=init_transformer, mem_len=150, mask=True)
tfmerXL_clas_config = dict(ctx_len=150, n_layers=12, n_heads=10, d_model=410, d_head=41, d_inner=2100, resid_p=0.1, attn_p=0.1,
ff_p=0.1, embed_p=0.1, output_p=0.1, bias=False, scale=True, act=Activation.ReLU, double_drop=True,
init=init_transformer, mem_len=150, mask=False)
def tfmerXL_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0]
n = len(encoder.layers)//3
groups = [list(encoder.layers[:n]) + [ParameterModule(encoder.u), ParameterModule(encoder.v)]]
return groups + [list(encoder.layers[n:2*n]), list(encoder.layers[2*n:]), [encoder.encoder, model[1]]]
def tfmerXL_clas_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0].module
n = len(encoder.layers)//3
groups = [[encoder.encoder], list(encoder.layers[:n]) + [ParameterModule(encoder.u), ParameterModule(encoder.v)]]
return groups + [list(encoder.layers[n:2*n]), list(encoder.layers[2*n:]), [model[1]]]
|