update 10 NAS algs

others/GDAS/lib/nas_rnn/basemodel.py

@@ -0,0 +1,181 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .genotypes import STEPS
+from .utils import mask2d, LockedDropout, embedded_dropout
+
+
+INITRANGE = 0.04
+
+def none_func(x):
+  return x * 0
+
+
+class DARTSCell(nn.Module):
+
+  def __init__(self, ninp, nhid, dropouth, dropoutx, genotype):
+    super(DARTSCell, self).__init__()
+    self.nhid = nhid
+    self.dropouth = dropouth
+    self.dropoutx = dropoutx
+    self.genotype = genotype
+
+    # genotype is None when doing arch search
+    steps = len(self.genotype.recurrent) if self.genotype is not None else STEPS
+    self._W0 = nn.Parameter(torch.Tensor(ninp+nhid, 2*nhid).uniform_(-INITRANGE, INITRANGE))
+    self._Ws = nn.ParameterList([
+        nn.Parameter(torch.Tensor(nhid, 2*nhid).uniform_(-INITRANGE, INITRANGE)) for i in range(steps)
+    ])
+
+  def forward(self, inputs, hidden, arch_probs):
+    T, B = inputs.size(0), inputs.size(1)
+
+    if self.training:
+      x_mask = mask2d(B, inputs.size(2), keep_prob=1.-self.dropoutx)
+      h_mask = mask2d(B, hidden.size(2), keep_prob=1.-self.dropouth)
+    else:
+      x_mask = h_mask = None
+
+    hidden = hidden[0]
+    hiddens = []
+    for t in range(T):
+      hidden = self.cell(inputs[t], hidden, x_mask, h_mask, arch_probs)
+      hiddens.append(hidden)
+    hiddens = torch.stack(hiddens)
+    return hiddens, hiddens[-1].unsqueeze(0)
+
+  def _compute_init_state(self, x, h_prev, x_mask, h_mask):
+    if self.training:
+      xh_prev = torch.cat([x * x_mask, h_prev * h_mask], dim=-1)
+    else:
+      xh_prev = torch.cat([x, h_prev], dim=-1)
+    c0, h0 = torch.split(xh_prev.mm(self._W0), self.nhid, dim=-1)
+    c0 = c0.sigmoid()
+    h0 = h0.tanh()
+    s0 = h_prev + c0 * (h0-h_prev)
+    return s0
+
+  def _get_activation(self, name):
+    if name == 'tanh':
+      f = torch.tanh
+    elif name == 'relu':
+      f = torch.relu
+    elif name == 'sigmoid':
+      f = torch.sigmoid
+    elif name == 'identity':
+      f = lambda x: x
+    elif name == 'none':
+      f = none_func
+    else:
+      raise NotImplementedError
+    return f
+
+  def cell(self, x, h_prev, x_mask, h_mask, _):
+    s0 = self._compute_init_state(x, h_prev, x_mask, h_mask)
+
+    states = [s0]
+    for i, (name, pred) in enumerate(self.genotype.recurrent):
+      s_prev = states[pred]
+      if self.training:
+        ch = (s_prev * h_mask).mm(self._Ws[i])
+      else:
+        ch = s_prev.mm(self._Ws[i])
+      c, h = torch.split(ch, self.nhid, dim=-1)
+      c = c.sigmoid()
+      fn = self._get_activation(name)
+      h = fn(h)
+      s = s_prev + c * (h-s_prev)
+      states += [s]
+    output = torch.mean(torch.stack([states[i] for i in self.genotype.concat], -1), -1)
+    return output
+
+
+class RNNModel(nn.Module):
+  """Container module with an encoder, a recurrent module, and a decoder."""
+  def __init__(self, ntoken, ninp, nhid, nhidlast, 
+                 dropout=0.5, dropouth=0.5, dropoutx=0.5, dropouti=0.5, dropoute=0.1,
+                 cell_cls=None, genotype=None):
+    super(RNNModel, self).__init__()
+    self.lockdrop = LockedDropout()
+    self.encoder = nn.Embedding(ntoken, ninp)
+        
+    assert ninp == nhid == nhidlast
+    if cell_cls == DARTSCell:
+      assert genotype is not None
+      rnns = [cell_cls(ninp, nhid, dropouth, dropoutx, genotype)]
+    else:
+      assert genotype is None
+      rnns = [cell_cls(ninp, nhid, dropouth, dropoutx)]
+
+    self.rnns    = torch.nn.ModuleList(rnns)
+    self.decoder = nn.Linear(ninp, ntoken)
+    self.decoder.weight = self.encoder.weight
+    self.init_weights()
+    self.arch_weights = None
+
+    self.ninp = ninp
+    self.nhid = nhid
+    self.nhidlast = nhidlast
+    self.dropout = dropout
+    self.dropouti = dropouti
+    self.dropoute = dropoute
+    self.ntoken = ntoken
+    self.cell_cls = cell_cls
+    # acceleration
+    self.tau = None
+    self.use_gumbel = False
+
+  def set_gumbel(self, use_gumbel, set_check):
+    self.use_gumbel = use_gumbel
+    for i, rnn in enumerate(self.rnns):
+      rnn.set_check(set_check)
+
+  def set_tau(self, tau):
+    self.tau = tau
+  
+  def get_tau(self):
+    return self.tau
+
+  def init_weights(self):
+    self.encoder.weight.data.uniform_(-INITRANGE, INITRANGE)
+    self.decoder.bias.data.fill_(0)
+    self.decoder.weight.data.uniform_(-INITRANGE, INITRANGE)
+
+  def forward(self, input, hidden, return_h=False):
+    batch_size = input.size(1)
+
+    emb = embedded_dropout(self.encoder, input, dropout=self.dropoute if self.training else 0)
+    emb = self.lockdrop(emb, self.dropouti)
+
+    raw_output = emb
+    new_hidden = []
+    raw_outputs = []
+    outputs = []
+    if self.arch_weights is None:
+      arch_probs = None
+    else:
+      if self.use_gumbel: arch_probs = F.gumbel_softmax(self.arch_weights, self.tau, False)
+      else              : arch_probs = F.softmax(self.arch_weights, dim=-1)
+
+    for l, rnn in enumerate(self.rnns):
+      current_input = raw_output
+      raw_output, new_h = rnn(raw_output, hidden[l], arch_probs)
+      new_hidden.append(new_h)
+      raw_outputs.append(raw_output)
+    hidden = new_hidden
+
+    output = self.lockdrop(raw_output, self.dropout)
+    outputs.append(output)
+
+    logit = self.decoder(output.view(-1, self.ninp))
+    log_prob = nn.functional.log_softmax(logit, dim=-1)
+    model_output = log_prob
+    model_output = model_output.view(-1, batch_size, self.ntoken)
+
+    if return_h: return model_output, hidden, raw_outputs, outputs
+    else       : return model_output, hidden
+
+  def init_hidden(self, bsz):
+    weight = next(self.parameters()).clone()
+    return [weight.new(1, bsz, self.nhid).zero_()]