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palsoftmax.py
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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import gc; import os
import torch
from torch.nn import *
import torch.nn as nn
import torch.nn.functional as F
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import gc
import os
from tqdm.notebook import tqdm
from fastai.tabular import *
import torch.nn.functional as F
import math
# Very dirty codes, please forgive me
from torch.nn.utils.rnn import pack_padded_sequence
from torch.nn.utils.rnn import pad_packed_sequence
def tile(a, dim, n_tile):
init_dim = a.size(dim)
repeat_idx = [1] * a.dim()
repeat_idx[dim] = n_tile
a = a.repeat(*(repeat_idx))
order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
return torch.index_select(a, dim, order_index)
class ProjectedAdaptiveLogSoftmax(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
tie_projs=None, out_layers_weights=None, out_projs=None,
keep_order=False):
super().__init__()
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
self.tie_projs = tie_projs
if self.n_clusters > 0:
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
if not out_layers_weights:
self.out_layers_weights = nn.ParameterList()
else:
self.out_layers_weights = out_layers_weights
self.out_layers_biases = nn.ParameterList()
self.shared_out_projs = out_projs
self.out_projs = nn.ParameterList()
if div_val == 1:
if d_proj != d_embed:
for i in range(len(self.cutoffs)):
if tie_projs[i]:
self.out_projs.append(None)
else:
self.out_projs.append(
nn.Parameter(torch.zeros(d_proj, d_embed))
)
else:
# self.out_projs = [None] * len(self.cutoffs)
self.out_projs.append(None)
self.out_layers_biases.append(
nn.Parameter(torch.zeros(n_token))
)
if not out_layers_weights:
self.out_layers_weights.append(
nn.Parameter(torch.zeros(n_token, d_embed))
)
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = d_embed // (div_val ** i)
if tie_projs[i]:
self.out_projs.append(None)
else:
self.out_projs.append(
nn.Parameter(torch.zeros(d_proj, d_emb_i))
)
self.out_layers_biases.append(
nn.Parameter(torch.zeros(r_idx - l_idx))
)
if not out_layers_weights:
self.out_layers_weights.append(
nn.Parameter(torch.zeros(r_idx - l_idx, d_emb_i))
)
self.keep_order = keep_order
def _compute_logit(self, hidden, weight, bias, proj):
if proj is None:
logit = F.linear(hidden, weight, bias=bias)
else:
logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
if bias is not None:
logit = logit + bias
return logit
def get_out_proj(self, i):
if self.tie_projs[i]:
if len(self.shared_out_projs) == 0:
return None
elif len(self.shared_out_projs) == 1:
return self.shared_out_projs[0]
else:
return self.shared_out_projs[i]
else:
return self.out_projs[i]
def forward(self, hidden, target, keep_order=False):
'''
hidden :: [len*bsz x d_proj]
target :: [len*bsz]
'''
if hidden.size(0) != target.size(0):
raise RuntimeError('Input and target should have the same size '
'in the batch dimension.')
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers_weights[0],
self.out_layers_biases[0], self.get_out_proj(0))
nll = -F.log_softmax(logit, dim=-1) \
.gather(1, target.unsqueeze(1)).squeeze(1)
else:
# construct weights and biases
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.out_layers_weights[0][l_idx:r_idx]
bias_i = self.out_layers_biases[0][l_idx:r_idx]
else:
weight_i = self.out_layers_weights[i]
bias_i = self.out_layers_biases[i]
if i == 0:
weight_i = torch.cat(
[weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat(
[bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.get_out_proj(0)
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
head_logprob = F.log_softmax(head_logit, dim=1)
nll = torch.zeros_like(target, dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]
mask_i = (target >= l_idx) & (target < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = target.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
if i == 0:
logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.get_out_proj(i)
hidden_i = hidden.index_select(0, indices_i)
tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob_i[:, -i] \
+ tail_logprob_i.gather(1, target_i[:, None]).squeeze(1)
if self.keep_order or keep_order:
nll.index_copy_(0, indices_i, -logprob_i)
else:
nll[offset:offset+logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return nll