Merge pull request #134 from chenkaiyu1997/master

[new] Add ENAS (Efficient Neural Architecture Search)

fastNLP/models/enas_controller.py

@@ -0,0 +1,223 @@
+# Code Modified from https://github.com/carpedm20/ENAS-pytorch
+"""A module with NAS controller-related code."""
+import collections
+import os
+
+import torch
+import torch.nn.functional as F
+import fastNLP
+import fastNLP.models.enas_utils as utils
+from fastNLP.models.enas_utils import Node
+
+
+def _construct_dags(prev_nodes, activations, func_names, num_blocks):
+    """Constructs a set of DAGs based on the actions, i.e., previous nodes and
+    activation functions, sampled from the controller/policy pi.
+
+    Args:
+        prev_nodes: Previous node actions from the policy.
+        activations: Activations sampled from the policy.
+        func_names: Mapping from activation function names to functions.
+        num_blocks: Number of blocks in the target RNN cell.
+
+    Returns:
+        A list of DAGs defined by the inputs.
+
+    RNN cell DAGs are represented in the following way:
+
+    1. Each element (node) in a DAG is a list of `Node`s.
+
+    2. The `Node`s in the list dag[i] correspond to the subsequent nodes
+       that take the output from node i as their own input.
+
+    3. dag[-1] is the node that takes input from x^{(t)} and h^{(t - 1)}.
+       dag[-1] always feeds dag[0].
+       dag[-1] acts as if `w_xc`, `w_hc`, `w_xh` and `w_hh` are its
+       weights.
+
+    4. dag[N - 1] is the node that produces the hidden state passed to
+       the next timestep. dag[N - 1] is also always a leaf node, and therefore
+       is always averaged with the other leaf nodes and fed to the output
+       decoder.
+    """
+    dags = []
+    for nodes, func_ids in zip(prev_nodes, activations):
+        dag = collections.defaultdict(list)
+
+        # add first node
+        dag[-1] = [Node(0, func_names[func_ids[0]])]
+        dag[-2] = [Node(0, func_names[func_ids[0]])]
+
+        # add following nodes
+        for jdx, (idx, func_id) in enumerate(zip(nodes, func_ids[1:])):
+            dag[utils.to_item(idx)].append(Node(jdx + 1, func_names[func_id]))
+
+        leaf_nodes = set(range(num_blocks)) - dag.keys()
+
+        # merge with avg
+        for idx in leaf_nodes:
+            dag[idx] = [Node(num_blocks, 'avg')]
+
+        # This is actually y^{(t)}. h^{(t)} is node N - 1 in
+        # the graph, where N Is the number of nodes. I.e., h^{(t)} takes
+        # only one other node as its input.
+        # last h[t] node
+        last_node = Node(num_blocks + 1, 'h[t]')
+        dag[num_blocks] = [last_node]
+        dags.append(dag)
+
+    return dags
+
+
+class Controller(torch.nn.Module):
+    """Based on
+    https://github.com/pytorch/examples/blob/master/word_language_model/model.py
+
+    RL controllers do not necessarily have much to do with
+    language models.
+
+    Base the controller RNN on the GRU from:
+    https://github.com/ikostrikov/pytorch-a2c-ppo-acktr/blob/master/model.py
+    """
+    def __init__(self, num_blocks=4, controller_hid=100, cuda=False):
+        torch.nn.Module.__init__(self)
+
+            # `num_tokens` here is just the activation function
+            # for every even step,
+        self.shared_rnn_activations = ['tanh', 'ReLU', 'identity', 'sigmoid']
+        self.num_tokens = [len(self.shared_rnn_activations)]
+        self.controller_hid = controller_hid
+        self.use_cuda = cuda
+        self.num_blocks = num_blocks
+        for idx in range(num_blocks):
+            self.num_tokens += [idx + 1, len(self.shared_rnn_activations)]
+        self.func_names = self.shared_rnn_activations
+
+        num_total_tokens = sum(self.num_tokens)
+
+        self.encoder = torch.nn.Embedding(num_total_tokens,
+                                          controller_hid)
+        self.lstm = torch.nn.LSTMCell(controller_hid, controller_hid)
+
+        # Perhaps these weights in the decoder should be
+        # shared? At least for the activation functions, which all have the
+        # same size.
+        self.decoders = []
+        for idx, size in enumerate(self.num_tokens):
+            decoder = torch.nn.Linear(controller_hid, size)
+            self.decoders.append(decoder)
+
+        self._decoders = torch.nn.ModuleList(self.decoders)
+
+        self.reset_parameters()
+        self.static_init_hidden = utils.keydefaultdict(self.init_hidden)
+
+        def _get_default_hidden(key):
+            return utils.get_variable(
+                torch.zeros(key, self.controller_hid),
+                self.use_cuda,
+                requires_grad=False)
+
+        self.static_inputs = utils.keydefaultdict(_get_default_hidden)
+
+    def reset_parameters(self):
+        init_range = 0.1
+        for param in self.parameters():
+            param.data.uniform_(-init_range, init_range)
+        for decoder in self.decoders:
+            decoder.bias.data.fill_(0)
+
+    def forward(self,  # pylint:disable=arguments-differ
+                inputs,
+                hidden,
+                block_idx,
+                is_embed):
+        if not is_embed:
+            embed = self.encoder(inputs)
+        else:
+            embed = inputs
+
+        hx, cx = self.lstm(embed, hidden)
+        logits = self.decoders[block_idx](hx)
+
+        logits /= 5.0
+
+        # # exploration
+        # if self.args.mode == 'train':
+        #     logits = (2.5 * F.tanh(logits))
+
+        return logits, (hx, cx)
+
+    def sample(self, batch_size=1, with_details=False, save_dir=None):
+        """Samples a set of `args.num_blocks` many computational nodes from the
+        controller, where each node is made up of an activation function, and
+        each node except the last also includes a previous node.
+        """
+        if batch_size < 1:
+            raise Exception(f'Wrong batch_size: {batch_size} < 1')
+
+        # [B, L, H]
+        inputs = self.static_inputs[batch_size]
+        hidden = self.static_init_hidden[batch_size]
+
+        activations = []
+        entropies = []
+        log_probs = []
+        prev_nodes = []
+        # The RNN controller alternately outputs an activation,
+        # followed by a previous node, for each block except the last one,
+        # which only gets an activation function. The last node is the output
+        # node, and its previous node is the average of all leaf nodes.
+        for block_idx in range(2*(self.num_blocks - 1) + 1):
+            logits, hidden = self.forward(inputs,
+                                          hidden,
+                                          block_idx,
+                                          is_embed=(block_idx == 0))
+
+            probs = F.softmax(logits, dim=-1)
+            log_prob = F.log_softmax(logits, dim=-1)
+            # .mean() for entropy?
+            entropy = -(log_prob * probs).sum(1, keepdim=False)
+
+            action = probs.multinomial(num_samples=1).data
+            selected_log_prob = log_prob.gather(
+                1, utils.get_variable(action, requires_grad=False))
+
+            # why the [:, 0] here? Should it be .squeeze(), or
+            # .view()? Same below with `action`.
+            entropies.append(entropy)
+            log_probs.append(selected_log_prob[:, 0])
+
+            # 0: function, 1: previous node
+            mode = block_idx % 2
+            inputs = utils.get_variable(
+                action[:, 0] + sum(self.num_tokens[:mode]),
+                requires_grad=False)
+
+            if mode == 0:
+                activations.append(action[:, 0])
+            elif mode == 1:
+                prev_nodes.append(action[:, 0])
+
+        prev_nodes = torch.stack(prev_nodes).transpose(0, 1)
+        activations = torch.stack(activations).transpose(0, 1)
+
+        dags = _construct_dags(prev_nodes,
+                               activations,
+                               self.func_names,
+                               self.num_blocks)
+
+        if save_dir is not None:
+            for idx, dag in enumerate(dags):
+                utils.draw_network(dag,
+                                   os.path.join(save_dir, f'graph{idx}.png'))
+
+        if with_details:
+            return dags, torch.cat(log_probs), torch.cat(entropies)
+
+        return dags
+
+    def init_hidden(self, batch_size):
+        zeros = torch.zeros(batch_size, self.controller_hid)
+        return (utils.get_variable(zeros, self.use_cuda, requires_grad=False),
+                utils.get_variable(zeros.clone(), self.use_cuda, requires_grad=False))

fastNLP/models/enas_model.py

@@ -0,0 +1,388 @@
+# Code Modified from https://github.com/carpedm20/ENAS-pytorch
+
+"""Module containing the shared RNN model."""
+import numpy as np
+import collections
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+import fastNLP.models.enas_utils as utils
+from fastNLP.models.base_model import BaseModel
+import fastNLP.modules.encoder as encoder
+
+def _get_dropped_weights(w_raw, dropout_p, is_training):
+    """Drops out weights to implement DropConnect.
+
+    Args:
+        w_raw: Full, pre-dropout, weights to be dropped out.
+        dropout_p: Proportion of weights to drop out.
+        is_training: True iff _shared_ model is training.
+
+    Returns:
+        The dropped weights.
+
+    Why does torch.nn.functional.dropout() return:
+    1. `torch.autograd.Variable()` on the training loop
+    2. `torch.nn.Parameter()` on the controller or eval loop, when
+    training = False...
+
+    Even though the call to `_setweights` in the Smerity repo's
+    `weight_drop.py` does not have this behaviour, and `F.dropout` always
+    returns `torch.autograd.Variable` there, even when `training=False`?
+
+    The above TODO is the reason for the hacky check for `torch.nn.Parameter`.
+    """
+    dropped_w = F.dropout(w_raw, p=dropout_p, training=is_training)
+
+    if isinstance(dropped_w, torch.nn.Parameter):
+        dropped_w = dropped_w.clone()
+
+    return dropped_w
+
+class EmbeddingDropout(torch.nn.Embedding):
+    """Class for dropping out embeddings by zero'ing out parameters in the
+    embedding matrix.
+
+    This is equivalent to dropping out particular words, e.g., in the sentence
+    'the quick brown fox jumps over the lazy dog', dropping out 'the' would
+    lead to the sentence '### quick brown fox jumps over ### lazy dog' (in the
+    embedding vector space).
+
+    See 'A Theoretically Grounded Application of Dropout in Recurrent Neural
+    Networks', (Gal and Ghahramani, 2016).
+    """
+    def __init__(self,
+                 num_embeddings,
+                 embedding_dim,
+                 max_norm=None,
+                 norm_type=2,
+                 scale_grad_by_freq=False,
+                 sparse=False,
+                 dropout=0.1,
+                 scale=None):
+        """Embedding constructor.
+
+        Args:
+            dropout: Dropout probability.
+            scale: Used to scale parameters of embedding weight matrix that are
+                not dropped out. Note that this is _in addition_ to the
+                `1/(1 - dropout)` scaling.
+
+        See `torch.nn.Embedding` for remaining arguments.
+        """
+        torch.nn.Embedding.__init__(self,
+                                    num_embeddings=num_embeddings,
+                                    embedding_dim=embedding_dim,
+                                    max_norm=max_norm,
+                                    norm_type=norm_type,
+                                    scale_grad_by_freq=scale_grad_by_freq,
+                                    sparse=sparse)
+        self.dropout = dropout
+        assert (dropout >= 0.0) and (dropout < 1.0), ('Dropout must be >= 0.0 '
+                                                      'and < 1.0')
+        self.scale = scale
+
+    def forward(self, inputs):  # pylint:disable=arguments-differ
+        """Embeds `inputs` with the dropped out embedding weight matrix."""
+        if self.training:
+            dropout = self.dropout
+        else:
+            dropout = 0
+
+        if dropout:
+            mask = self.weight.data.new(self.weight.size(0), 1)
+            mask.bernoulli_(1 - dropout)
+            mask = mask.expand_as(self.weight)
+            mask = mask / (1 - dropout)
+            masked_weight = self.weight * Variable(mask)
+        else:
+            masked_weight = self.weight
+        if self.scale and self.scale != 1:
+            masked_weight = masked_weight * self.scale
+
+        return F.embedding(inputs,
+                           masked_weight,
+                           max_norm=self.max_norm,
+                           norm_type=self.norm_type,
+                           scale_grad_by_freq=self.scale_grad_by_freq,
+                           sparse=self.sparse)
+
+
+class LockedDropout(nn.Module):
+    # code from https://github.com/salesforce/awd-lstm-lm/blob/master/locked_dropout.py
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, dropout=0.5):
+        if not self.training or not dropout:
+            return x
+        m = x.data.new(1, x.size(1), x.size(2)).bernoulli_(1 - dropout)
+        mask = Variable(m, requires_grad=False) / (1 - dropout)
+        mask = mask.expand_as(x)
+        return mask * x
+
+
+class ENASModel(BaseModel):
+    """Shared RNN model."""
+    def __init__(self, embed_num, num_classes, num_blocks=4, cuda=False, shared_hid=1000, shared_embed=1000):
+        super(ENASModel, self).__init__()
+
+        self.use_cuda = cuda
+
+        self.shared_hid = shared_hid
+        self.num_blocks = num_blocks
+        self.decoder = nn.Linear(self.shared_hid, num_classes)
+        self.encoder = EmbeddingDropout(embed_num,
+                                        shared_embed,
+                                        dropout=0.1)
+        self.lockdrop = LockedDropout()
+        self.dag = None
+
+        # Tie weights
+        # self.decoder.weight = self.encoder.weight
+
+        # Since W^{x, c} and W^{h, c} are always summed, there
+        # is no point duplicating their bias offset parameter. Likewise for
+        # W^{x, h} and W^{h, h}.
+        self.w_xc = nn.Linear(shared_embed, self.shared_hid)
+        self.w_xh = nn.Linear(shared_embed, self.shared_hid)
+
+        # The raw weights are stored here because the hidden-to-hidden weights
+        # are weight dropped on the forward pass.
+        self.w_hc_raw = torch.nn.Parameter(
+            torch.Tensor(self.shared_hid, self.shared_hid))
+        self.w_hh_raw = torch.nn.Parameter(
+            torch.Tensor(self.shared_hid, self.shared_hid))
+        self.w_hc = None
+        self.w_hh = None
+
+        self.w_h = collections.defaultdict(dict)
+        self.w_c = collections.defaultdict(dict)
+
+        for idx in range(self.num_blocks):
+            for jdx in range(idx + 1, self.num_blocks):
+                self.w_h[idx][jdx] = nn.Linear(self.shared_hid,
+                                               self.shared_hid,
+                                               bias=False)
+                self.w_c[idx][jdx] = nn.Linear(self.shared_hid,
+                                               self.shared_hid,
+                                               bias=False)
+
+        self._w_h = nn.ModuleList([self.w_h[idx][jdx]
+                                   for idx in self.w_h
+                                   for jdx in self.w_h[idx]])
+        self._w_c = nn.ModuleList([self.w_c[idx][jdx]
+                                   for idx in self.w_c
+                                   for jdx in self.w_c[idx]])
+
+        self.batch_norm = None
+        # if args.mode == 'train':
+        #     self.batch_norm = nn.BatchNorm1d(self.shared_hid)
+        # else:
+        #     self.batch_norm = None
+
+        self.reset_parameters()
+        self.static_init_hidden = utils.keydefaultdict(self.init_hidden)
+
+    def setDAG(self, dag):
+        if self.dag is None:
+            self.dag = dag
+
+    def forward(self, word_seq, hidden=None):
+        inputs = torch.transpose(word_seq, 0, 1)
+
+        time_steps = inputs.size(0)
+        batch_size = inputs.size(1)
+
+
+        self.w_hh = _get_dropped_weights(self.w_hh_raw,
+                                         0.5,
+                                         self.training)
+        self.w_hc = _get_dropped_weights(self.w_hc_raw,
+                                         0.5,
+                                         self.training)
+
+        # hidden = self.static_init_hidden[batch_size] if hidden is None else hidden
+        hidden = self.static_init_hidden[batch_size]
+
+        embed = self.encoder(inputs)
+
+        embed = self.lockdrop(embed, 0.65 if self.training else 0)
+
+        # The norm of hidden states are clipped here because
+        # otherwise ENAS is especially prone to exploding activations on the
+        # forward pass. This could probably be fixed in a more elegant way, but
+        # it might be exposing a weakness in the ENAS algorithm as currently
+        # proposed.
+        #
+        # For more details, see
+        # https://github.com/carpedm20/ENAS-pytorch/issues/6
+        clipped_num = 0
+        max_clipped_norm = 0
+        h1tohT = []
+        logits = []
+        for step in range(time_steps):
+            x_t = embed[step]
+            logit, hidden = self.cell(x_t, hidden, self.dag)
+
+            hidden_norms = hidden.norm(dim=-1)
+            max_norm = 25.0
+            if hidden_norms.data.max() > max_norm:
+                # Just directly use the torch slice operations
+                # in PyTorch v0.4.
+                #
+                # This workaround for PyTorch v0.3.1 does everything in numpy,
+                # because the PyTorch slicing and slice assignment is too
+                # flaky.
+                hidden_norms = hidden_norms.data.cpu().numpy()
+
+                clipped_num += 1
+                if hidden_norms.max() > max_clipped_norm:
+                    max_clipped_norm = hidden_norms.max()
+
+                clip_select = hidden_norms > max_norm
+                clip_norms = hidden_norms[clip_select]
+
+                mask = np.ones(hidden.size())
+                normalizer = max_norm/clip_norms
+                normalizer = normalizer[:, np.newaxis]
+
+                mask[clip_select] = normalizer
+
+                if self.use_cuda:
+                    hidden *= torch.autograd.Variable(
+                        torch.FloatTensor(mask).cuda(), requires_grad=False)
+                else:
+                    hidden *= torch.autograd.Variable(
+                        torch.FloatTensor(mask), requires_grad=False)                    
+            logits.append(logit)
+            h1tohT.append(hidden)
+
+        h1tohT = torch.stack(h1tohT)
+        output = torch.stack(logits)
+        raw_output = output
+
+        output = self.lockdrop(output, 0.4 if self.training else 0)
+
+        #Pooling 
+        output = torch.mean(output, 0)
+
+        decoded = self.decoder(output)
+
+        extra_out = {'dropped': decoded,
+                     'hiddens': h1tohT,
+                     'raw': raw_output}
+        return {'pred': decoded, 'hidden': hidden, 'extra_out': extra_out}
+
+    def cell(self, x, h_prev, dag):
+        """Computes a single pass through the discovered RNN cell."""
+        c = {}
+        h = {}
+        f = {}
+
+        f[0] = self.get_f(dag[-1][0].name)
+        c[0] = torch.sigmoid(self.w_xc(x) + F.linear(h_prev, self.w_hc, None))
+        h[0] = (c[0]*f[0](self.w_xh(x) + F.linear(h_prev, self.w_hh, None)) +
+                (1 - c[0])*h_prev)
+
+        leaf_node_ids = []
+        q = collections.deque()
+        q.append(0)
+
+        # Computes connections from the parent nodes `node_id`
+        # to their child nodes `next_id` recursively, skipping leaf nodes. A
+        # leaf node is a node whose id == `self.num_blocks`.
+        #
+        # Connections between parent i and child j should be computed as
+        # h_j = c_j*f_{ij}{(W^h_{ij}*h_i)} + (1 - c_j)*h_i,
+        # where c_j = \sigmoid{(W^c_{ij}*h_i)}
+        #
+        # See Training details from Section 3.1 of the paper.
+        #
+        # The following algorithm does a breadth-first (since `q.popleft()` is
+        # used) search over the nodes and computes all the hidden states.
+        while True:
+            if len(q) == 0:
+                break
+
+            node_id = q.popleft()
+            nodes = dag[node_id]
+
+            for next_node in nodes:
+                next_id = next_node.id
+                if next_id == self.num_blocks:
+                    leaf_node_ids.append(node_id)
+                    assert len(nodes) == 1, ('parent of leaf node should have '
+                                             'only one child')
+                    continue
+
+                w_h = self.w_h[node_id][next_id]
+                w_c = self.w_c[node_id][next_id]
+
+                f[next_id] = self.get_f(next_node.name)
+                c[next_id] = torch.sigmoid(w_c(h[node_id]))
+                h[next_id] = (c[next_id]*f[next_id](w_h(h[node_id])) +
+                              (1 - c[next_id])*h[node_id])
+
+                q.append(next_id)
+
+        # Instead of averaging loose ends, perhaps there should
+        # be a set of separate unshared weights for each "loose" connection
+        # between each node in a cell and the output.
+        #
+        # As it stands, all weights W^h_{ij} are doing double duty by
+        # connecting both from i to j, as well as from i to the output.
+
+        # average all the loose ends
+        leaf_nodes = [h[node_id] for node_id in leaf_node_ids]
+        output = torch.mean(torch.stack(leaf_nodes, 2), -1)
+
+        # stabilizing the Updates of omega
+        if self.batch_norm is not None:
+            output = self.batch_norm(output)
+
+        return output, h[self.num_blocks - 1]
+
+    def init_hidden(self, batch_size):
+        zeros = torch.zeros(batch_size, self.shared_hid)
+        return utils.get_variable(zeros, self.use_cuda, requires_grad=False)
+
+    def get_f(self, name):
+        name = name.lower()
+        if name == 'relu':
+            f = torch.relu
+        elif name == 'tanh':
+            f = torch.tanh
+        elif name == 'identity':
+            f = lambda x: x
+        elif name == 'sigmoid':
+            f = torch.sigmoid
+        return f
+        
+
+    @property
+    def num_parameters(self):
+        def size(p):
+            return np.prod(p.size())
+        return sum([size(param) for param in self.parameters()])
+
+
+    def reset_parameters(self):
+        init_range = 0.025
+        # init_range = 0.025 if self.args.mode == 'train' else 0.04
+        for param in self.parameters():
+            param.data.uniform_(-init_range, init_range)
+        self.decoder.bias.data.fill_(0)
+
+    def predict(self, word_seq):
+        """
+
+        :param word_seq: torch.LongTensor, [batch_size, seq_len]
+        :return predict: dict of torch.LongTensor, [batch_size, seq_len]
+        """
+        output = self(word_seq)
+        _, predict = output['pred'].max(dim=1)
+        return {'pred': predict}

fastNLP/models/enas_trainer.py

@@ -0,0 +1,385 @@
+# Code Modified from https://github.com/carpedm20/ENAS-pytorch
+
+import os
+import time
+from datetime import datetime
+from datetime import timedelta
+
+import numpy as np
+import torch
+import math
+from torch import nn
+
+try:
+    from tqdm.autonotebook import tqdm
+except:
+    from fastNLP.core.utils import pseudo_tqdm as tqdm
+
+from fastNLP.core.batch import Batch
+from fastNLP.core.callback import CallbackManager, CallbackException
+from fastNLP.core.dataset import DataSet
+from fastNLP.core.utils import CheckError
+from fastNLP.core.utils import _move_dict_value_to_device
+import fastNLP
+import fastNLP.models.enas_utils as utils
+from fastNLP.core.utils import _build_args
+
+from torch.optim import Adam
+
+
+def _get_no_grad_ctx_mgr():
+    """Returns a the `torch.no_grad` context manager for PyTorch version >=
+    0.4, or a no-op context manager otherwise.
+    """
+    return torch.no_grad()
+
+
+class ENASTrainer(fastNLP.Trainer):
+    """A class to wrap training code."""
+    def __init__(self, train_data, model, controller, **kwargs):
+        """Constructor for training algorithm.
+        :param DataSet train_data: the training data
+        :param torch.nn.modules.module model: a PyTorch model
+        :param torch.nn.modules.module controller: a PyTorch model
+        """
+        self.final_epochs = kwargs['final_epochs']
+        kwargs.pop('final_epochs')
+        super(ENASTrainer, self).__init__(train_data, model, **kwargs)
+        self.controller_step = 0
+        self.shared_step = 0
+        self.max_length = 35
+
+        self.shared = model
+        self.controller = controller
+
+        self.shared_optim = Adam(
+            self.shared.parameters(),
+            lr=20.0,
+            weight_decay=1e-7)
+
+        self.controller_optim = Adam(
+            self.controller.parameters(),
+            lr=3.5e-4)
+
+    def train(self, load_best_model=True):
+        """
+        :param bool load_best_model: 该参数只有在初始化提供了dev_data的情况下有效，如果True, trainer将在返回之前重新加载dev表现
+            最好的模型参数。
+        :return results: 返回一个字典类型的数据, 内含以下内容::
+
+            seconds: float, 表示训练时长
+            以下三个内容只有在提供了dev_data的情况下会有。
+            best_eval: Dict of Dict, 表示evaluation的结果
+            best_epoch: int，在第几个epoch取得的最佳值
+            best_step: int, 在第几个step(batch)更新取得的最佳值
+
+        """
+        results = {}
+        if self.n_epochs <= 0:
+            print(f"training epoch is {self.n_epochs}, nothing was done.")
+            results['seconds'] = 0.
+            return results
+        try:
+            if torch.cuda.is_available() and self.use_cuda:
+                self.model = self.model.cuda()
+            self._model_device = self.model.parameters().__next__().device
+            self._mode(self.model, is_test=False)
+
+            self.start_time = str(datetime.now().strftime('%Y-%m-%d-%H-%M-%S'))
+            start_time = time.time()
+            print("training epochs started " + self.start_time, flush=True)
+
+            try:
+                self.callback_manager.on_train_begin()
+                self._train()
+                self.callback_manager.on_train_end(self.model)
+            except (CallbackException, KeyboardInterrupt) as e:
+                self.callback_manager.on_exception(e, self.model)
+
+            if self.dev_data is not None:
+                print("\nIn Epoch:{}/Step:{}, got best dev performance:".format(self.best_dev_epoch, self.best_dev_step) +
+                      self.tester._format_eval_results(self.best_dev_perf),)
+                results['best_eval'] = self.best_dev_perf
+                results['best_epoch'] = self.best_dev_epoch
+                results['best_step'] = self.best_dev_step
+                if load_best_model:
+                    model_name = "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time])
+                    load_succeed = self._load_model(self.model, model_name)
+                    if load_succeed:
+                        print("Reloaded the best model.")
+                    else:
+                        print("Fail to reload best model.")
+        finally:
+            pass
+        results['seconds'] = round(time.time() - start_time, 2)
+
+        return results
+
+    def _train(self):
+        if not self.use_tqdm:
+            from fastNLP.core.utils import pseudo_tqdm as inner_tqdm
+        else:
+            inner_tqdm = tqdm
+        self.step = 0
+        start = time.time()
+        total_steps = (len(self.train_data) // self.batch_size + int(
+            len(self.train_data) % self.batch_size != 0)) * self.n_epochs
+        with inner_tqdm(total=total_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
+            avg_loss = 0
+            data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False,
+                                  prefetch=self.prefetch)
+            for epoch in range(1, self.n_epochs+1):
+                pbar.set_description_str(desc="Epoch {}/{}".format(epoch, self.n_epochs))
+                last_stage = (epoch > self.n_epochs + 1 - self.final_epochs)
+                if epoch == self.n_epochs + 1 - self.final_epochs:
+                    print('Entering the final stage. (Only train the selected structure)')
+                # early stopping
+                self.callback_manager.on_epoch_begin(epoch, self.n_epochs)
+
+                # 1. Training the shared parameters omega of the child models
+                self.train_shared(pbar)
+
+                # 2. Training the controller parameters theta
+                if not last_stage:
+                    self.train_controller()
+
+                if ((self.validate_every > 0 and self.step % self.validate_every == 0) or
+                    (self.validate_every < 0 and self.step % len(data_iterator) == 0)) \
+                        and self.dev_data is not None:
+                    if not last_stage:
+                        self.derive()
+                    eval_res = self._do_validation(epoch=epoch, step=self.step)
+                    eval_str = "Evaluation at Epoch {}/{}. Step:{}/{}. ".format(epoch, self.n_epochs, self.step,
+                                                                                total_steps) + \
+                                self.tester._format_eval_results(eval_res)
+                    pbar.write(eval_str)
+
+                # lr decay; early stopping
+                self.callback_manager.on_epoch_end(epoch, self.n_epochs, self.optimizer)
+            # =============== epochs end =================== #
+            pbar.close()
+        # ============ tqdm end ============== #
+
+
+    def get_loss(self, inputs, targets, hidden, dags):
+        """Computes the loss for the same batch for M models.
+
+        This amounts to an estimate of the loss, which is turned into an
+        estimate for the gradients of the shared model.
+        """
+        if not isinstance(dags, list):
+            dags = [dags]
+
+        loss = 0
+        for dag in dags:
+            self.shared.setDAG(dag)
+            inputs = _build_args(self.shared.forward, **inputs)
+            inputs['hidden'] = hidden
+            result = self.shared(**inputs)
+            output, hidden, extra_out = result['pred'], result['hidden'], result['extra_out']
+
+            self.callback_manager.on_loss_begin(targets, result)
+            sample_loss = self._compute_loss(result, targets)
+            loss += sample_loss
+
+        assert len(dags) == 1, 'there are multiple `hidden` for multple `dags`'
+        return loss, hidden, extra_out
+
+    def train_shared(self, pbar=None, max_step=None, dag=None):
+        """Train the language model for 400 steps of minibatches of 64
+        examples.
+
+        Args:
+            max_step: Used to run extra training steps as a warm-up.
+            dag: If not None, is used instead of calling sample().
+
+        BPTT is truncated at 35 timesteps.
+
+        For each weight update, gradients are estimated by sampling M models
+        from the fixed controller policy, and averaging their gradients
+        computed on a batch of training data.
+        """
+        model = self.shared
+        model.train()
+        self.controller.eval()
+
+        hidden = self.shared.init_hidden(self.batch_size)
+
+        abs_max_grad = 0
+        abs_max_hidden_norm = 0
+        step = 0
+        raw_total_loss = 0
+        total_loss = 0
+        train_idx = 0
+        avg_loss = 0
+        data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False,
+                prefetch=self.prefetch)
+
+        for batch_x, batch_y in data_iterator:
+            _move_dict_value_to_device(batch_x, batch_y, device=self._model_device)
+            indices = data_iterator.get_batch_indices()
+            # negative sampling; replace unknown; re-weight batch_y
+            self.callback_manager.on_batch_begin(batch_x, batch_y, indices)
+            # prediction = self._data_forward(self.model, batch_x)
+
+            dags = self.controller.sample(1)
+            inputs, targets = batch_x, batch_y
+            # self.callback_manager.on_loss_begin(batch_y, prediction)
+            loss, hidden, extra_out = self.get_loss(inputs,
+                                                    targets,
+                                                    hidden,
+                                                    dags)
+            hidden.detach_()
+           
+            avg_loss += loss.item()
+
+            # Is loss NaN or inf? requires_grad = False
+            self.callback_manager.on_backward_begin(loss, self.model)
+            self._grad_backward(loss)
+            self.callback_manager.on_backward_end(self.model)
+
+            self._update()
+            self.callback_manager.on_step_end(self.optimizer)
+
+            if (self.step+1) % self.print_every == 0:
+                if self.use_tqdm:
+                    print_output = "loss:{0:<6.5f}".format(avg_loss / self.print_every)
+                    pbar.update(self.print_every)
+                else:
+                    end = time.time()
+                    diff = timedelta(seconds=round(end - start))
+                    print_output = "[epoch: {:>3} step: {:>4}] train loss: {:>4.6} time: {}".format(
+                        epoch, self.step, avg_loss, diff)
+                pbar.set_postfix_str(print_output)
+                avg_loss = 0
+            self.step += 1
+            step += 1
+            self.shared_step += 1
+            self.callback_manager.on_batch_end()
+        # ================= mini-batch end ==================== #
+
+
+    def get_reward(self, dag, entropies, hidden, valid_idx=0):
+        """Computes the perplexity of a single sampled model on a minibatch of
+        validation data.
+        """
+        if not isinstance(entropies, np.ndarray):
+            entropies = entropies.data.cpu().numpy()
+
+        data_iterator = Batch(self.dev_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False,
+                prefetch=self.prefetch)
+
+        for inputs, targets in data_iterator:
+            valid_loss, hidden, _ = self.get_loss(inputs, targets, hidden, dag)
+            valid_loss = utils.to_item(valid_loss.data)
+
+            valid_ppl = math.exp(valid_loss)
+
+            R = 80 / valid_ppl
+
+            rewards = R + 1e-4 * entropies
+
+            return rewards, hidden
+
+    def train_controller(self):
+        """Fixes the shared parameters and updates the controller parameters.
+
+        The controller is updated with a score function gradient estimator
+        (i.e., REINFORCE), with the reward being c/valid_ppl, where valid_ppl
+        is computed on a minibatch of validation data.
+
+        A moving average baseline is used.
+
+        The controller is trained for 2000 steps per epoch (i.e.,
+        first (Train Shared) phase -> second (Train Controller) phase).
+        """
+        model = self.controller
+        model.train()
+        # Why can't we call shared.eval() here? Leads to loss
+        # being uniformly zero for the controller.
+        # self.shared.eval()
+
+        avg_reward_base = None
+        baseline = None
+        adv_history = []
+        entropy_history = []
+        reward_history = []
+
+        hidden = self.shared.init_hidden(self.batch_size)
+        total_loss = 0
+        valid_idx = 0
+        for step in range(20):
+            # sample models
+            dags, log_probs, entropies = self.controller.sample(
+                with_details=True)
+
+            # calculate reward
+            np_entropies = entropies.data.cpu().numpy()
+            # No gradients should be backpropagated to the
+            # shared model during controller training, obviously.
+            with _get_no_grad_ctx_mgr():
+                rewards, hidden = self.get_reward(dags,
+                                                  np_entropies,
+                                                  hidden,
+                                                  valid_idx)
+
+
+            reward_history.extend(rewards)
+            entropy_history.extend(np_entropies)
+
+            # moving average baseline
+            if baseline is None:
+                baseline = rewards
+            else:
+                decay = 0.95
+                baseline = decay * baseline + (1 - decay) * rewards
+
+            adv = rewards - baseline
+            adv_history.extend(adv)
+
+            # policy loss
+            loss = -log_probs*utils.get_variable(adv,
+                                                 self.use_cuda,
+                                                 requires_grad=False)
+
+            loss = loss.sum()  # or loss.mean()
+
+            # update
+            self.controller_optim.zero_grad()
+            loss.backward()
+
+            self.controller_optim.step()
+
+            total_loss += utils.to_item(loss.data)
+
+            if ((step % 50) == 0) and (step > 0):
+                reward_history, adv_history, entropy_history = [], [], []
+                total_loss = 0
+
+            self.controller_step += 1
+            # prev_valid_idx = valid_idx
+            # valid_idx = ((valid_idx + self.max_length) %
+            #              (self.valid_data.size(0) - 1))
+            # # Whenever we wrap around to the beginning of the
+            # # validation data, we reset the hidden states.
+            # if prev_valid_idx > valid_idx:
+            #     hidden = self.shared.init_hidden(self.batch_size)
+
+    def derive(self, sample_num=10, valid_idx=0):
+        """We are always deriving based on the very first batch
+        of validation data? This seems wrong...
+        """
+        hidden = self.shared.init_hidden(self.batch_size)
+
+        dags, _, entropies = self.controller.sample(sample_num,
+                                                    with_details=True)
+
+        max_R = 0
+        best_dag = None
+        for dag in dags:
+            R, _ = self.get_reward(dag, entropies, hidden, valid_idx)
+            if R.max() > max_R:
+                max_R = R.max()
+                best_dag = dag
+
+        self.model.setDAG(best_dag)

fastNLP/models/enas_utils.py

@@ -0,0 +1,56 @@
+# Code Modified from https://github.com/carpedm20/ENAS-pytorch
+
+from __future__ import print_function
+
+from collections import defaultdict
+import collections
+from datetime import datetime
+import os
+import json
+
+import numpy as np
+
+import torch
+from torch.autograd import Variable
+
+def detach(h):
+    if type(h) == Variable:
+        return Variable(h.data)
+    else:
+        return tuple(detach(v) for v in h)
+
+def get_variable(inputs, cuda=False, **kwargs):
+    if type(inputs) in [list, np.ndarray]:
+        inputs = torch.Tensor(inputs)
+    if cuda:
+        out = Variable(inputs.cuda(), **kwargs)
+    else:
+        out = Variable(inputs, **kwargs)
+    return out
+
+def update_lr(optimizer, lr):
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+Node = collections.namedtuple('Node', ['id', 'name'])
+
+
+class keydefaultdict(defaultdict):
+    def __missing__(self, key):
+        if self.default_factory is None:
+            raise KeyError(key)
+        else:
+            ret = self[key] = self.default_factory(key)
+            return ret
+
+
+def to_item(x):
+    """Converts x, possibly scalar and possibly tensor, to a Python scalar."""
+    if isinstance(x, (float, int)):
+        return x
+
+    if float(torch.__version__[0:3]) < 0.4:
+        assert (x.dim() == 1) and (len(x) == 1)
+        return x[0]
+
+    return x.item()

test/models/test_enas.py

@@ -0,0 +1,112 @@
+import unittest
+
+from fastNLP import DataSet
+from fastNLP import Instance
+from fastNLP import Vocabulary
+from fastNLP.core.losses import CrossEntropyLoss
+from fastNLP.core.metrics import AccuracyMetric
+
+
+class TestENAS(unittest.TestCase):
+    def testENAS(self):
+        # 从csv读取数据到DataSet
+        sample_path = "tutorials/sample_data/tutorial_sample_dataset.csv"
+        dataset = DataSet.read_csv(sample_path, headers=('raw_sentence', 'label'),
+                                   sep='\t')
+        print(len(dataset))
+        print(dataset[0])
+        print(dataset[-3])
+
+        dataset.append(Instance(raw_sentence='fake data', label='0'))
+        # 将所有数字转为小写
+        dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')
+        # label转int
+        dataset.apply(lambda x: int(x['label']), new_field_name='label')
+
+        # 使用空格分割句子
+        def split_sent(ins):
+            return ins['raw_sentence'].split()
+
+        dataset.apply(split_sent, new_field_name='words')
+
+        # 增加长度信息
+        dataset.apply(lambda x: len(x['words']), new_field_name='seq_len')
+        print(len(dataset))
+        print(dataset[0])
+
+        # DataSet.drop(func)筛除数据
+        dataset.drop(lambda x: x['seq_len'] <= 3)
+        print(len(dataset))
+
+        # 设置DataSet中，哪些field要转为tensor
+        # set target，loss或evaluate中的golden，计算loss，模型评估时使用
+        dataset.set_target("label")
+        # set input，模型forward时使用
+        dataset.set_input("words", "seq_len")
+
+        # 分出测试集、训练集
+        test_data, train_data = dataset.split(0.5)
+        print(len(test_data))
+        print(len(train_data))
+
+        # 构建词表, Vocabulary.add(word)
+        vocab = Vocabulary(min_freq=2)
+        train_data.apply(lambda x: [vocab.add(word) for word in x['words']])
+        vocab.build_vocab()
+
+        # index句子, Vocabulary.to_index(word)
+        train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')
+        test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')
+        print(test_data[0])
+
+        # 如果你们需要做强化学习或者GAN之类的项目，你们也可以使用这些数据预处理的工具
+        from fastNLP.core.batch import Batch
+        from fastNLP.core.sampler import RandomSampler
+
+        batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())
+        for batch_x, batch_y in batch_iterator:
+            print("batch_x has: ", batch_x)
+            print("batch_y has: ", batch_y)
+            break
+
+        from fastNLP.models.enas_model import ENASModel
+        from fastNLP.models.enas_controller import Controller
+        model = ENASModel(embed_num=len(vocab), num_classes=5)
+        controller = Controller()
+
+        from fastNLP.models.enas_trainer import ENASTrainer
+        from copy import deepcopy
+
+        # 更改DataSet中对应field的名称，要以模型的forward等参数名一致
+        train_data.rename_field('words', 'word_seq')  # input field 与 forward 参数一致
+        train_data.rename_field('label', 'label_seq')
+        test_data.rename_field('words', 'word_seq')
+        test_data.rename_field('label', 'label_seq')
+
+        loss = CrossEntropyLoss(pred="output", target="label_seq")
+        metric = AccuracyMetric(pred="predict", target="label_seq")
+
+        trainer = ENASTrainer(model=model, controller=controller, train_data=train_data, dev_data=test_data,
+                          loss=CrossEntropyLoss(pred="output", target="label_seq"),
+                          metrics=AccuracyMetric(pred="predict", target="label_seq"),
+                          check_code_level=-1,
+                          save_path=None,
+                          batch_size=32,
+                          print_every=1,
+                          n_epochs=3,
+                          final_epochs=1)
+        trainer.train()
+        print('Train finished!')
+
+        # 调用Tester在test_data上评价效果
+        from fastNLP import Tester
+
+        tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred="predict", target="label_seq"),
+                        batch_size=4)
+
+        acc = tester.test()
+        print(acc)
+
+
+if __name__ == '__main__':
+    unittest.main()