adding OpenAI GPT
This commit is contained in:
thomwolf committed
1 parent
8da280ebbe
commit
93f563b8a8
5 files changed
+699
-11
No files matched your search
@@ -0,0 +1,302 @@
|
||||
import copy
|
||||
import json
|
||||
import math
|
||||
import re
|
||||
import collections
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn.parameter import Parameter
|
||||
|
||||
from .modeling import BertLayerNorm as LayerNorm
|
||||
|
||||
|
||||
def gelu(x):
|
||||
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
|
||||
|
||||
|
||||
def swish(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
ACT_FNS = {
|
||||
'relu': nn.ReLU,
|
||||
'swish': swish,
|
||||
'gelu': gelu
|
||||
}
|
||||
|
||||
|
||||
class Conv1D(nn.Module):
|
||||
def __init__(self, nf, rf, nx):
|
||||
super(Conv1D, self).__init__()
|
||||
self.rf = rf
|
||||
self.nf = nf
|
||||
if rf == 1: # faster 1x1 conv
|
||||
w = torch.empty(nx, nf)
|
||||
nn.init.normal_(w, std=0.02)
|
||||
self.w = Parameter(w)
|
||||
self.b = Parameter(torch.zeros(nf))
|
||||
else: # was used to train LM
|
||||
raise NotImplementedError
|
||||
|
||||
def forward(self, x):
|
||||
if self.rf == 1:
|
||||
size_out = x.size()[:-1] + (self.nf,)
|
||||
x = torch.addmm(self.b, x.view(-1, x.size(-1)), self.w)
|
||||
x = x.view(*size_out)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, nx, n_ctx, cfg, scale=False):
|
||||
super(Attention, self).__init__()
|
||||
n_state = nx # in Attention: n_state=768 (nx=n_embd)
|
||||
# [switch nx => n_state from Block to Attention to keep identical to TF implem]
|
||||
assert n_state % cfg.n_head == 0
|
||||
self.register_buffer('b', torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
|
||||
self.n_head = cfg.n_head
|
||||
self.split_size = n_state
|
||||
self.scale = scale
|
||||
self.c_attn = Conv1D(n_state * 3, 1, nx)
|
||||
self.c_proj = Conv1D(n_state, 1, nx)
|
||||
self.attn_dropout = nn.Dropout(cfg.attn_pdrop)
|
||||
self.resid_dropout = nn.Dropout(cfg.resid_pdrop)
|
||||
|
||||
def _attn(self, q, k, v):
|
||||
w = torch.matmul(q, k)
|
||||
if self.scale:
|
||||
w = w / math.sqrt(v.size(-1))
|
||||
w = w * self.b + -1e9 * (1 - self.b) # TF implem method: mask_attn_weights
|
||||
w = nn.Softmax(dim=-1)(w)
|
||||
w = self.attn_dropout(w)
|
||||
return torch.matmul(w, v)
|
||||
|
||||
def merge_heads(self, x):
|
||||
x = x.permute(0, 2, 1, 3).contiguous()
|
||||
new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
|
||||
return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
|
||||
|
||||
def split_heads(self, x, k=False):
|
||||
new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
|
||||
x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
|
||||
if k:
|
||||
return x.permute(0, 2, 3, 1)
|
||||
else:
|
||||
return x.permute(0, 2, 1, 3)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.c_attn(x)
|
||||
query, key, value = x.split(self.split_size, dim=2)
|
||||
query = self.split_heads(query)
|
||||
key = self.split_heads(key, k=True)
|
||||
value = self.split_heads(value)
|
||||
a = self._attn(query, key, value)
|
||||
a = self.merge_heads(a)
|
||||
a = self.c_proj(a)
|
||||
a = self.resid_dropout(a)
|
||||
return a
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, n_state, cfg): # in MLP: n_state=3072 (4 * n_embd)
|
||||
super(MLP, self).__init__()
|
||||
nx = cfg.n_embd
|
||||
self.c_fc = Conv1D(n_state, 1, nx)
|
||||
self.c_proj = Conv1D(nx, 1, n_state)
|
||||
self.act = ACT_FNS[cfg.afn]
|
||||
self.dropout = nn.Dropout(cfg.resid_pdrop)
|
||||
|
||||
def forward(self, x):
|
||||
h = self.act(self.c_fc(x))
|
||||
h2 = self.c_proj(h)
|
||||
return self.dropout(h2)
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, n_ctx, cfg, scale=False):
|
||||
super(Block, self).__init__()
|
||||
nx = cfg.n_embd
|
||||
self.attn = Attention(nx, n_ctx, cfg, scale)
|
||||
self.ln_1 = LayerNorm(nx)
|
||||
self.mlp = MLP(4 * nx, cfg)
|
||||
self.ln_2 = LayerNorm(nx)
|
||||
|
||||
def forward(self, x):
|
||||
a = self.attn(x)
|
||||
n = self.ln_1(x + a)
|
||||
m = self.mlp(n)
|
||||
h = self.ln_2(n + m)
|
||||
return h
|
||||
|
||||
|
||||
class TransformerModel(nn.Module):
|
||||
""" Transformer model """
|
||||
|
||||
def __init__(self, cfg, vocab=40990, n_ctx=512):
|
||||
super(TransformerModel, self).__init__()
|
||||
self.vocab = vocab
|
||||
self.embed = nn.Embedding(vocab, cfg.n_embd)
|
||||
self.drop = nn.Dropout(cfg.embd_pdrop)
|
||||
block = Block(n_ctx, cfg, scale=True)
|
||||
self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(cfg.n_layer)])
|
||||
|
||||
nn.init.normal_(self.embed.weight, std=0.02)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.view(-1, x.size(-2), x.size(-1))
|
||||
e = self.embed(x)
|
||||
# Add the position information to the input embeddings
|
||||
h = e.sum(dim=2)
|
||||
for block in self.h:
|
||||
h = block(h)
|
||||
return h
|
||||
|
||||
|
||||
class LMHead(nn.Module):
|
||||
""" Language Model Head for the transformer """
|
||||
|
||||
def __init__(self, model, cfg):
|
||||
super(LMHead, self).__init__()
|
||||
self.n_embd = cfg.n_embd
|
||||
embed_shape = model.embed.weight.shape
|
||||
self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
|
||||
self.decoder.weight = model.embed.weight # Tied weights
|
||||
|
||||
def forward(self, h):
|
||||
# Truncated Language modeling logits (we remove the last token)
|
||||
h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd)
|
||||
lm_logits = self.decoder(h_trunc)
|
||||
return lm_logits
|
||||
|
||||
|
||||
class MultipleChoiceHead(nn.Module):
|
||||
""" Classifier Head for the transformer """
|
||||
|
||||
def __init__(self, clf_token, cfg):
|
||||
super(MultipleChoiceHead, self).__init__()
|
||||
self.n_embd = cfg.n_embd
|
||||
self.clf_token = clf_token
|
||||
self.dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
|
||||
self.linear = nn.Linear(cfg.n_embd, 1)
|
||||
|
||||
nn.init.normal_(self.linear.weight, std = 0.02)
|
||||
nn.init.normal_(self.linear.bias, 0)
|
||||
|
||||
def forward(self, h, x):
|
||||
# Classification logits
|
||||
clf_h = h.view(-1, self.n_embd)
|
||||
flat = x[..., 0].contiguous().view(-1)
|
||||
clf_h = clf_h[flat == self.clf_token, :]
|
||||
clf_h = clf_h.view(-1, x.size(1), self.n_embd, 1)
|
||||
# This double transposition is there to replicate the behavior
|
||||
# of the noise_shape argument in the tensorflow
|
||||
# implementation. For more details, see
|
||||
# https://github.com/huggingface/pytorch-openai-transformer-lm/issues/11
|
||||
clf_h = self.dropout(clf_h.transpose(1, 2)).transpose(1, 2)
|
||||
clf_h = clf_h.contiguous().view(-1, self.n_embd)
|
||||
clf_logits = self.linear(clf_h)
|
||||
|
||||
return clf_logits.view(-1, x.size(1))
|
||||
|
||||
|
||||
class ClfHead(nn.Module):
|
||||
"""Classification Head for the transformer
|
||||
|
||||
TODO: test this class."""
|
||||
def __init__(self, clf_token, cfg, n_class):
|
||||
super(ClfHead, self).__init__()
|
||||
self.n_embd = cfg.n_embd
|
||||
self.clf_token = clf_token
|
||||
self.dropout = nn.Dropout(cfg.clf_pdrop)
|
||||
self.linear = nn.Linear(cfg.n_embd, n_class)
|
||||
|
||||
nn.init.normal_(self.linear.weight, std = 0.02)
|
||||
nn.init.normal_(self.linear.bias, 0)
|
||||
|
||||
def forward(self, h, x):
|
||||
clf_h = h.view(-1, self.n_embd)
|
||||
flat = x[..., 0].contiguous().view(-1)
|
||||
clf_h = clf_h[flat == self.clf_token, :]
|
||||
clf_h = self.dropout(clf_h)
|
||||
clf_logits = self.linear(clf_h)
|
||||
|
||||
return clf_logits
|
||||
|
||||
class SimilarityHead(nn.Module):
|
||||
""" Similarity Head for the transformer
|
||||
|
||||
TODO: test this class."""
|
||||
def __init__(self, clf_token, cfg):
|
||||
super(SimilarityHead, self).__init__()
|
||||
self.n_embd = cfg.n_embd
|
||||
self.clf_token = clf_token
|
||||
self.dropout = nn.Dropout(cfg.clf_pdrop)
|
||||
self.linear = nn.Linear(cfg.n_embd, 1)
|
||||
|
||||
nn.init.normal_(self.linear.weight, std = 0.02)
|
||||
nn.init.normal_(self.linear.bias, 0)
|
||||
|
||||
def forward(self, h, x):
|
||||
sim_h = h.view(-1, self.n_embd)
|
||||
flat = x[..., 0].contiguous().view(-1)
|
||||
sim_h = sim_h[flat == self.clf_token, :]
|
||||
sim_h = self.dropout(sim_h)
|
||||
sim_h = sim_h.sum(dim = 1)
|
||||
sim_logits = self.linear(sim_h)
|
||||
|
||||
return sim_logits
|
||||
|
||||
class DoubleHeadModel(nn.Module):
|
||||
""" Transformer with language model and task specific heads """
|
||||
def __init__(self, cfg, clf_token, task_head_type, vocab=40990, n_ctx=512):
|
||||
super(DoubleHeadModel, self).__init__()
|
||||
self.transformer = TransformerModel(cfg, vocab=vocab, n_ctx=n_ctx)
|
||||
self.lm_head = LMHead(self.transformer, cfg)
|
||||
if isinstance(task_head_type, str):
|
||||
if task_head_type == 'multiple_choice':
|
||||
self.task_head = MultipleChoiceHead(clf_token, cfg)
|
||||
elif task_head_type == 'similarity':
|
||||
self.task_head = SimilarityHead(clf_token, cfg)
|
||||
elif task_head_type == 'inference':
|
||||
# the three classes correspond to entailment, contradiction and neutral.
|
||||
self.task_head = ClfHead(clf_token, cfg, 3)
|
||||
else:
|
||||
raise ValueError("task_head_type is expected to be 'multiple_choice' "
|
||||
"'similarity', 'inference' or ('classification', n_class) "
|
||||
f"got {task_head_type}.")
|
||||
elif isinstance(task_head_type, collections.abc.Sequence) and len(task_head_type) == 2 and \
|
||||
task_head_type[0] == 'classification':
|
||||
n_class = task_head_type[1]
|
||||
self.task_head = ClfHead(clf_token, cfg, n_class)
|
||||
else:
|
||||
raise ValueError("task_head_type is expected to be 'multiple_choice' "
|
||||
"'similarity', 'inference' or ('classification', n_class) "
|
||||
f"got {task_head_type}.")
|
||||
|
||||
def forward(self, x):
|
||||
h = self.transformer(x)
|
||||
lm_logits = self.lm_head(h)
|
||||
task_logits = self.task_head(h, x)
|
||||
|
||||
return lm_logits, task_logits
|
||||
|
||||
|
||||
class dotdict(dict):
|
||||
"""dot.notation access to dictionary attributes"""
|
||||
__getattr__ = dict.get
|
||||
__setattr__ = dict.__setitem__
|
||||
__delattr__ = dict.__delitem__
|
||||
|
||||
|
||||
DEFAULT_CONFIG = dotdict({
|
||||
'n_embd': 768,
|
||||
'n_head': 12,
|
||||
'n_layer': 12,
|
||||
'embd_pdrop': 0.1,
|
||||
'attn_pdrop': 0.1,
|
||||
'resid_pdrop': 0.1,
|
||||
'afn': 'gelu',
|
||||
'clf_pdrop': 0.1})
|
||||
Reference in new issue
Block a user