Merge pull request #107 from facebookresearch/oss-merge-internal

Oss merge internal changes

fairseq/criterions/cross_entropy.py

@@ -9,7 +9,7 @@ import math
 import torch.nn.functional as F
 
 from . import FairseqCriterion, register_criterion
-
+from fairseq import utils
 
 @register_criterion('cross_entropy')
 class CrossEntropyCriterion(FairseqCriterion):
@@ -33,7 +33,7 @@ class CrossEntropyCriterion(FairseqCriterion):
                           reduce=reduce)
         sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
         logging_output = {
-            'loss': loss.data[0] if reduce else loss.data,
+            'loss': utils.item(loss.data) if reduce else loss.data,
             'ntokens': sample['ntokens'],
             'sample_size': sample_size,
         }

fairseq/criterions/label_smoothed_cross_entropy.py

@@ -7,6 +7,7 @@
 
 import math
 import torch
+from torch.autograd import Variable
 import torch.nn.functional as F
 
 from fairseq import utils
@@ -24,6 +25,8 @@ class LabelSmoothedNLLLoss(torch.autograd.Function):
 
         norm = grad_input.size(-1)
         if weights is not None:
+            if isinstance(grad_input, Variable) and not isinstance(weights, Variable):
+                weights = Variable(weights, requires_grad=False)
             norm = weights.sum()
             grad_input.mul(weights.view(1, weights.size(0)).expand_as(grad_input))
 
@@ -41,7 +44,10 @@ class LabelSmoothedNLLLoss(torch.autograd.Function):
 
     @staticmethod
     def backward(ctx, grad):
-        return utils.volatile_variable(ctx.grad_input) * grad, None, None, None, None, None
+        grad_input = ctx.grad_input
+        if not isinstance(grad_input, torch.autograd.Variable):
+            grad_input = utils.volatile_variable(grad_input)
+        return grad_input * grad, None, None, None, None, None
 
 
 @register_criterion('label_smoothed_cross_entropy')
@@ -73,8 +79,8 @@ class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
         nll_loss = F.nll_loss(lprobs, target, size_average=False, ignore_index=self.padding_idx, reduce=reduce)
         sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
         logging_output = {
-            'loss': loss.data[0] if reduce else loss.data,
-            'nll_loss': nll_loss.data[0] if reduce else loss.data,
+            'loss': utils.item(loss.data) if reduce else loss.data,
+            'nll_loss': utils.item(nll_loss.data) if reduce else loss.data,
             'ntokens': sample['ntokens'],
             'sample_size': sample_size,
         }

fairseq/distributed_utils.py

@@ -116,7 +116,7 @@ def all_gather_list(data, max_size=4096):
     if len(enc) >= max_size:
         raise ValueError('encoded data exceeds max_size: {}'.format(len(enc)))
     in_buffer[0] = len(enc)
-    in_buffer[1:len(enc)+1] = torch.ByteTensor(enc)
+    in_buffer[1:len(enc)+1] = torch.ByteTensor(list(enc))
 
     torch.distributed.all_gather(out_buffers, in_buffer.cuda())
 

fairseq/models/fairseq_incremental_decoder.py

@@ -13,100 +13,21 @@ class FairseqIncrementalDecoder(FairseqDecoder):
 
     def __init__(self, dictionary):
         super().__init__(dictionary)
-        self._is_incremental_eval = False
-        self._incremental_state = {}
 
-    def forward(self, prev_output_tokens, encoder_out):
-        if self._is_incremental_eval:
-            raise NotImplementedError
-        else:
-            raise NotImplementedError
+    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
+        raise NotImplementedError
 
-    def incremental_inference(self):
-        """Context manager for incremental inference.
-
-        This provides an optimized forward pass for incremental inference
-        (i.e., it predicts one time step at a time). If the input order changes
-        between time steps, call reorder_incremental_state to update the
-        relevant buffers. To generate a fresh sequence, first call
-        clear_incremental_state.
-
-        Usage:
-        ```
-        with model.decoder.incremental_inference():
-            for step in range(maxlen):
-                out, _ = model.decoder(tokens[:, :step], encoder_out)
-                probs = model.get_normalized_probs(out[:, -1, :], log_probs=False)
-        ```
-        """
-        class IncrementalInference(object):
-            def __init__(self, decoder):
-                self.decoder = decoder
-
-            def __enter__(self):
-                self.decoder.incremental_eval(True)
-
-            def __exit__(self, *args):
-                self.decoder.incremental_eval(False)
-        return IncrementalInference(self)
-
-    def incremental_eval(self, mode=True):
-        """Sets the decoder and all children in incremental evaluation mode."""
-        assert self._is_incremental_eval != mode, \
-            'incremental_eval already set to mode {}'.format(mode)
-
-        self._is_incremental_eval = mode
-        if mode:
-            self.clear_incremental_state()
-
-        def apply_incremental_eval(module):
-            if module != self and hasattr(module, 'incremental_eval'):
-                module.incremental_eval(mode)
-        self.apply(apply_incremental_eval)
-
-    def get_incremental_state(self, key):
-        """Return cached state or None if not in incremental inference mode."""
-        if self._is_incremental_eval and key in self._incremental_state:
-            return self._incremental_state[key]
-        return None
-
-    def set_incremental_state(self, key, value):
-        """Cache state needed for incremental inference mode."""
-        if self._is_incremental_eval:
-            self._incremental_state[key] = value
-        return value
-
-    def clear_incremental_state(self):
-        """Clear all state used for incremental generation.
-
-        **For incremental inference only**
-
-        This should be called before generating a fresh sequence.
-        beam_size is required if using BeamableMM.
-        """
-        if self._is_incremental_eval:
-            del self._incremental_state
-            self._incremental_state = {}
-
-            def apply_clear_incremental_state(module):
-                if module != self and hasattr(module, 'clear_incremental_state'):
-                    module.clear_incremental_state()
-            self.apply(apply_clear_incremental_state)
-
-    def reorder_incremental_state(self, new_order):
-        """Reorder buffered internal state (for incremental generation).
-
-        **For incremental inference only**
+    def reorder_incremental_state(self, incremental_state, new_order):
+        """Reorder incremental state.
 
         This should be called when the order of the input has changed from the
         previous time step. A typical use case is beam search, where the input
-        order changes between time steps based on the choice of beams.
+        order changes between time steps based on the selection of beams.
         """
-        if self._is_incremental_eval:
-            def apply_reorder_incremental_state(module):
-                if module != self and hasattr(module, 'reorder_incremental_state'):
-                    module.reorder_incremental_state(new_order)
-            self.apply(apply_reorder_incremental_state)
+        def apply_reorder_incremental_state(module):
+            if module != self and hasattr(module, 'reorder_incremental_state'):
+                module.reorder_incremental_state(incremental_state, new_order)
+        self.apply(apply_reorder_incremental_state)
 
     def set_beam_size(self, beam_size):
         """Sets the beam size in the decoder and all children."""

fairseq/models/fconv.py

@@ -10,6 +10,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from fairseq import utils
 from fairseq.data import LanguagePairDataset
 from fairseq.modules import BeamableMM, GradMultiply, LearnedPositionalEmbedding, LinearizedConvolution
 
@@ -229,19 +230,13 @@ class FConvDecoder(FairseqIncrementalDecoder):
         else:
             self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
 
-    def forward(self, prev_output_tokens, encoder_out):
+    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
         # split and transpose encoder outputs
-        encoder_a, encoder_b = self._split_encoder_out(encoder_out)
+        encoder_a, encoder_b = self._split_encoder_out(encoder_out, incremental_state)
 
-        # embed positions
-        positions = self.embed_positions(prev_output_tokens)
-
-        if self._is_incremental_eval:
-            # keep only the last token for incremental forward pass
-            prev_output_tokens = prev_output_tokens[:, -1:]
-
-        # embed tokens and positions
-        x = self.embed_tokens(prev_output_tokens) + positions
+        # embed tokens and combine with positional embeddings
+        x = self._embed_tokens(prev_output_tokens, incremental_state)
+        x += self.embed_positions(prev_output_tokens, incremental_state)
         x = F.dropout(x, p=self.dropout, training=self.training)
         target_embedding = x
 
@@ -249,7 +244,7 @@ class FConvDecoder(FairseqIncrementalDecoder):
         x = self.fc1(x)
 
         # B x T x C -> T x B x C
-        x = self._transpose_unless_incremental_eval(x)
+        x = self._transpose_if_training(x, incremental_state)
 
         # temporal convolutions
         avg_attn_scores = None
@@ -258,13 +253,14 @@ class FConvDecoder(FairseqIncrementalDecoder):
             residual = x if proj is None else proj(x)
 
             x = F.dropout(x, p=self.dropout, training=self.training)
-            x = conv(x)
-            x = conv.remove_future_timesteps(x)
+            x = conv(x, incremental_state)
+            if incremental_state is None:
+                x = conv.remove_future_timesteps(x)
             x = F.glu(x, dim=2)
 
             # attention
             if attention is not None:
-                x = self._transpose_unless_incremental_eval(x)
+                x = self._transpose_if_training(x, incremental_state)
 
                 x, attn_scores = attention(x, target_embedding, (encoder_a, encoder_b))
                 attn_scores = attn_scores / num_attn_layers
@@ -273,13 +269,13 @@ class FConvDecoder(FairseqIncrementalDecoder):
                 else:
                     avg_attn_scores.add_(attn_scores)
 
-                x = self._transpose_unless_incremental_eval(x)
+                x = self._transpose_if_training(x, incremental_state)
 
             # residual
             x = (x + residual) * math.sqrt(0.5)
 
         # T x B x C -> B x T x C
-        x = self._transpose_unless_incremental_eval(x)
+        x = self._transpose_if_training(x, incremental_state)
 
         # project back to size of vocabulary
         x = self.fc2(x)
@@ -288,10 +284,6 @@ class FConvDecoder(FairseqIncrementalDecoder):
 
         return x, avg_attn_scores
 
-    def reorder_incremental_state(self, new_order):
-        """Reorder buffered internal state (for incremental generation)."""
-        super().reorder_incremental_state(new_order)
-
     def max_positions(self):
         """Maximum output length supported by the decoder."""
         return self.embed_positions.max_positions()
@@ -306,13 +298,19 @@ class FConvDecoder(FairseqIncrementalDecoder):
             state_dict['decoder.version'] = torch.Tensor([1])
         return state_dict
 
-    def _split_encoder_out(self, encoder_out):
+    def _embed_tokens(self, tokens, incremental_state):
+        if incremental_state is not None:
+            # keep only the last token for incremental forward pass
+            tokens = tokens[:, -1:]
+        return self.embed_tokens(tokens)
+
+    def _split_encoder_out(self, encoder_out, incremental_state):
         """Split and transpose encoder outputs.
 
         This is cached when doing incremental inference.
         """
-        cached_result = self.get_incremental_state('encoder_out')
-        if cached_result:
+        cached_result = utils.get_incremental_state(self, incremental_state, 'encoder_out')
+        if cached_result is not None:
             return cached_result
 
         # transpose only once to speed up attention layers
@@ -320,12 +318,14 @@ class FConvDecoder(FairseqIncrementalDecoder):
         encoder_a = encoder_a.transpose(1, 2).contiguous()
         result = (encoder_a, encoder_b)
 
-        return self.set_incremental_state('encoder_out', result)
+        if incremental_state is not None:
+            utils.set_incremental_state(self, incremental_state, 'encoder_out', result)
+        return result
 
-    def _transpose_unless_incremental_eval(self, x):
-        if self._is_incremental_eval:
-            return x
-        return x.transpose(0, 1)
+    def _transpose_if_training(self, x, incremental_state):
+        if incremental_state is None:
+            x = x.transpose(0, 1)
+        return x
 
 
 def Embedding(num_embeddings, embedding_dim, padding_idx):

fairseq/models/lstm.py

@@ -183,12 +183,9 @@ class LSTMDecoder(FairseqIncrementalDecoder):
             self.additional_fc = Linear(embed_dim, out_embed_dim)
         self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
 
-    def forward(self, prev_output_tokens, encoder_out):
-        if self._is_incremental_eval:
+    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
+        if incremental_state is not None:
             prev_output_tokens = prev_output_tokens[:, -1:]
-        return self._forward(prev_output_tokens, encoder_out)
-
-    def _forward(self, prev_output_tokens, encoder_out):
         bsz, seqlen = prev_output_tokens.size()
 
         # get outputs from encoder
@@ -204,15 +201,15 @@ class LSTMDecoder(FairseqIncrementalDecoder):
         x = x.transpose(0, 1)
 
         # initialize previous states (or get from cache during incremental generation)
-        prev_hiddens = self.get_incremental_state('prev_hiddens')
-        if not prev_hiddens:
-            # first time step, initialize previous states
-            prev_hiddens, prev_cells = self._init_prev_states(encoder_out)
-            input_feed = Variable(x.data.new(bsz, embed_dim).zero_())
+        cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state')
+        if cached_state is not None:
+            prev_hiddens, prev_cells, input_feed = cached_state
         else:
-            # previous states are cached
-            prev_cells = self.get_incremental_state('prev_cells')
-            input_feed = self.get_incremental_state('input_feed')
+            _, encoder_hiddens, encoder_cells = encoder_out
+            num_layers = len(self.layers)
+            prev_hiddens = [encoder_hiddens[i] for i in range(num_layers)]
+            prev_cells = [encoder_cells[i] for i in range(num_layers)]
+            input_feed = Variable(x.data.new(bsz, embed_dim).zero_())
 
         attn_scores = Variable(x.data.new(srclen, seqlen, bsz).zero_())
         outs = []
@@ -242,9 +239,8 @@ class LSTMDecoder(FairseqIncrementalDecoder):
             outs.append(out)
 
         # cache previous states (no-op except during incremental generation)
-        self.set_incremental_state('prev_hiddens', prev_hiddens)
-        self.set_incremental_state('prev_cells', prev_cells)
-        self.set_incremental_state('input_feed', input_feed)
+        utils.set_incremental_state(
+            self, incremental_state, 'cached_state', (prev_hiddens, prev_cells, input_feed))
 
         # collect outputs across time steps
         x = torch.cat(outs, dim=0).view(seqlen, bsz, embed_dim)
@@ -263,34 +259,25 @@ class LSTMDecoder(FairseqIncrementalDecoder):
 
         return x, attn_scores
 
-    def reorder_incremental_state(self, new_order):
-        """Reorder buffered internal state (for incremental generation)."""
-        super().reorder_incremental_state(new_order)
-        new_order = Variable(new_order)
+    def reorder_incremental_state(self, incremental_state, new_order):
+        cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state')
+        if cached_state is None:
+            return
 
-        def reorder_state(key):
-            old = self.get_incremental_state(key)
-            if isinstance(old, list):
-                new = [old_i.index_select(0, new_order) for old_i in old]
-            else:
-                new = old.index_select(0, new_order)
-            self.set_incremental_state(key, new)
+        def reorder_state(state):
+            if isinstance(state, list):
+                return [reorder_state(state_i) for state_i in state]
+            return state.index_select(0, new_order)
 
-        reorder_state('prev_hiddens')
-        reorder_state('prev_cells')
-        reorder_state('input_feed')
+        if not isinstance(new_order, Variable):
+            new_order = Variable(new_order)
+        new_state = tuple(map(reorder_state, cached_state))
+        utils.set_incremental_state(self, incremental_state, 'cached_state', new_state)
 
     def max_positions(self):
         """Maximum output length supported by the decoder."""
         return int(1e5)  # an arbitrary large number
 
-    def _init_prev_states(self, encoder_out):
-        _, encoder_hiddens, encoder_cells = encoder_out
-        num_layers = len(self.layers)
-        prev_hiddens = [encoder_hiddens[i] for i in range(num_layers)]
-        prev_cells = [encoder_cells[i] for i in range(num_layers)]
-        return prev_hiddens, prev_cells
-
 
 def Embedding(num_embeddings, embedding_dim, padding_idx):
     m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)

fairseq/modules/learned_positional_embedding.py

@@ -20,14 +20,10 @@ class LearnedPositionalEmbedding(nn.Embedding):
     def __init__(self, num_embeddings, embedding_dim, padding_idx, left_pad):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.left_pad = left_pad
-        self._is_incremental_eval = False
 
-    def incremental_eval(self, mode=True):
-        self._is_incremental_eval = mode
-
-    def forward(self, input):
+    def forward(self, input, incremental_state=None):
         """Input is expected to be of size [bsz x seqlen]."""
-        if self._is_incremental_eval:
+        if incremental_state is not None:
             # positions is the same for every token when decoding a single step
             positions = Variable(
                 input.data.new(1, 1).fill_(self.padding_idx + input.size(1)))

fairseq/modules/linearized_convolution.py

@@ -22,35 +22,20 @@ class LinearizedConvolution(ConvTBC):
 
     def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
         super().__init__(in_channels, out_channels, kernel_size, **kwargs)
-        self._is_incremental_eval = False
         self._linearized_weight = None
         self.register_backward_hook(self._clear_linearized_weight)
 
-    def remove_future_timesteps(self, x):
-        """Remove future time steps created by padding."""
-        if not self._is_incremental_eval and self.kernel_size[0] > 1 and self.padding[0] > 0:
-            x = x[:-self.padding[0], :, :]
-        return x
-
-    def incremental_eval(self, mode=True):
-        self._is_incremental_eval = mode
-        if mode:
-            self.clear_incremental_state()
-
-    def forward(self, input):
-        if self._is_incremental_eval:
-            return self.incremental_forward(input)
-        else:
+    def forward(self, input, incremental_state=None):
+        """
+        Input: Time x Batch x Channel.
+        Args:
+            incremental_state: Used to buffer signal; if not None, then input is
+                expected to contain a single frame. If the input order changes
+                between time steps, call reorder_incremental_state.
+        """
+        if incremental_state is None:
             return super().forward(input)
 
-    def incremental_forward(self, input):
-        """Forward convolution one time step at a time.
-
-        This function maintains an internal state to buffer signal and accepts
-        a single frame as input. If the input order changes between time steps,
-        call reorder_incremental_state. To apply to fresh inputs, call
-        clear_incremental_state.
-        """
         # reshape weight
         weight = self._get_linearized_weight()
         kw = self.kernel_size[0]
@@ -58,25 +43,37 @@ class LinearizedConvolution(ConvTBC):
         bsz = input.size(0)  # input: bsz x len x dim
         if kw > 1:
             input = input.data
-            if self.input_buffer is None:
-                self.input_buffer = input.new(bsz, kw, input.size(2))
-                self.input_buffer.zero_()
+            input_buffer = self._get_input_buffer(incremental_state)
+            if input_buffer is None:
+                input_buffer = input.new(bsz, kw, input.size(2)).zero_()
+                self._set_input_buffer(incremental_state, input_buffer)
             else:
                 # shift buffer
-                self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()
+                input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone()
             # append next input
-            self.input_buffer[:, -1, :] = input[:, -1, :]
-            input = utils.volatile_variable(self.input_buffer)
+            input_buffer[:, -1, :] = input[:, -1, :]
+            input = utils.volatile_variable(input_buffer)
         with utils.maybe_no_grad():
             output = F.linear(input.view(bsz, -1), weight, self.bias)
         return output.view(bsz, 1, -1)
 
-    def clear_incremental_state(self):
-        self.input_buffer = None
+    def remove_future_timesteps(self, x):
+        """Remove future time steps created by padding."""
+        if self.kernel_size[0] > 1 and self.padding[0] > 0:
+            x = x[:-self.padding[0], :, :]
+        return x
+
+    def reorder_incremental_state(self, incremental_state, new_order):
+        input_buffer = self._get_input_buffer(incremental_state)
+        if input_buffer is not None:
+            input_buffer = input_buffer.index_select(0, new_order)
+            self._set_input_buffer(incremental_state, input_buffer)
+
+    def _get_input_buffer(self, incremental_state):
+        return utils.get_incremental_state(self, incremental_state, 'input_buffer')
 
-    def reorder_incremental_state(self, new_order):
-        if self.input_buffer is not None:
-            self.input_buffer = self.input_buffer.index_select(0, new_order)
+    def _set_input_buffer(self, incremental_state, new_buffer):
+        return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer)
 
     def _get_linearized_weight(self):
         if self._linearized_weight is None:

fairseq/options.py

@@ -7,6 +7,8 @@
 
 import argparse
 
+import torch
+
 from fairseq.criterions import CRITERION_REGISTRY
 from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY
 from fairseq.optim import OPTIMIZER_REGISTRY
@@ -117,8 +119,9 @@ def add_dataset_args(parser, train=False, gen=False):
 
 def add_distributed_training_args(parser):
     group = parser.add_argument_group('Distributed training')
-    group.add_argument('--distributed-world-size', default=1, type=int, metavar='N',
-                       help='total number of GPUs across all nodes, default: 1 GPU')
+    group.add_argument('--distributed-world-size', type=int, metavar='N',
+                       default=torch.cuda.device_count(),
+                       help='total number of GPUs across all nodes (default: all visible GPUs)')
     group.add_argument('--distributed-rank', default=0, type=int,
                        help='rank of the current worker')
     group.add_argument('--distributed-backend', default='nccl', type=str,
@@ -223,6 +226,8 @@ def add_generation_args(parser):
                        help='only print final scores')
     group.add_argument('--score-reference', action='store_true',
                        help='just score the reference translation')
+    group.add_argument('--prefix-size', default=0, type=int, metavar='PS',
+                       help=('initialize generation by target prefix of given length'))
     return group
 
 

fairseq/sequence_generator.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree. An additional grant of patent rights
 # can be found in the PATENTS file in the same directory.
 
-from contextlib import ExitStack
 import math
 import torch
 
@@ -51,7 +50,7 @@ class SequenceGenerator(object):
         return self
 
     def generate_batched_itr(self, data_itr, beam_size=None, maxlen_a=0.0, maxlen_b=None,
-                             cuda=False, timer=None):
+                             cuda=False, timer=None, prefix_size=0):
         """Iterate over a batched dataset and yield individual translations.
 
         Args:
@@ -75,6 +74,7 @@ class SequenceGenerator(object):
                     input['src_lengths'],
                     beam_size=beam_size,
                     maxlen=int(maxlen_a*srclen + maxlen_b),
+                    prefix_tokens=s['target'][:, :prefix_size] if prefix_size > 0 else None,
                 )
             if timer is not None:
                 timer.stop(s['ntokens'])
@@ -84,15 +84,12 @@ class SequenceGenerator(object):
                 ref = utils.strip_pad(s['target'].data[i, :], self.pad)
                 yield id, src, ref, hypos[i]
 
-    def generate(self, src_tokens, src_lengths, beam_size=None, maxlen=None):
+    def generate(self, src_tokens, src_lengths, beam_size=None, maxlen=None, prefix_tokens=None):
         """Generate a batch of translations."""
-        with ExitStack() as stack:
-            for model in self.models:
-                if isinstance(model.decoder, FairseqIncrementalDecoder):
-                    stack.enter_context(model.decoder.incremental_inference())
-            return self._generate(src_tokens, src_lengths, beam_size, maxlen)
+        with utils.maybe_no_grad():
+            return self._generate(src_tokens, src_lengths, beam_size, maxlen, prefix_tokens)
 
-    def _generate(self, src_tokens, src_lengths, beam_size=None, maxlen=None):
+    def _generate(self, src_tokens, src_lengths, beam_size=None, maxlen=None, prefix_tokens=None):
         bsz, srclen = src_tokens.size()
         maxlen = min(maxlen, self.maxlen) if maxlen is not None else self.maxlen
 
@@ -101,11 +98,14 @@ class SequenceGenerator(object):
         beam_size = min(beam_size, self.vocab_size - 1)
 
         encoder_outs = []
+        incremental_states = {}
         for model in self.models:
             if not self.retain_dropout:
                 model.eval()
             if isinstance(model.decoder, FairseqIncrementalDecoder):
-                model.decoder.set_beam_size(beam_size)
+                incremental_states[model] = {}
+            else:
+                incremental_states[model] = None
 
             # compute the encoder output for each beam
             encoder_out = model.encoder(
@@ -243,9 +243,11 @@ class SequenceGenerator(object):
             if reorder_state is not None:
                 for model in self.models:
                     if isinstance(model.decoder, FairseqIncrementalDecoder):
-                        model.decoder.reorder_incremental_state(reorder_state)
+                        model.decoder.reorder_incremental_state(
+                            incremental_states[model], reorder_state)
 
-            probs, avg_attn_scores = self._decode(tokens[:, :step+1], encoder_outs)
+            probs, avg_attn_scores = self._decode(
+                tokens[:, :step+1], encoder_outs, incremental_states)
             if step == 0:
                 # at the first step all hypotheses are equally likely, so use
                 # only the first beam
@@ -267,15 +269,24 @@ class SequenceGenerator(object):
             eos_bbsz_idx = buffer('eos_bbsz_idx')
             eos_scores = buffer('eos_scores', type_of=scores)
             if step < maxlen:
-                # take the best 2 x beam_size predictions. We'll choose the first
-                # beam_size of these which don't predict eos to continue with.
-                torch.topk(
-                    probs.view(bsz, -1),
-                    k=min(cand_size, probs.view(bsz, -1).size(1) - 1),  # -1 so we never select pad
-                    out=(cand_scores, cand_indices),
-                )
-                torch.div(cand_indices, self.vocab_size, out=cand_beams)
-                cand_indices.fmod_(self.vocab_size)
+                if prefix_tokens is not None and step < prefix_tokens.size(1):
+                    probs_slice = probs.view(bsz, -1, probs.size(-1))[:, 0, :]
+                    cand_scores = probs_slice.gather(
+                        dim=1,
+                        index=prefix_tokens[:, step].view(-1, 1).data
+                    ).expand(-1, cand_size)
+                    cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, cand_size).data
+                    cand_beams.resize_as_(cand_indices).fill_(0)
+                else:
+                    # take the best 2 x beam_size predictions. We'll choose the first
+                    # beam_size of these which don't predict eos to continue with.
+                    torch.topk(
+                        probs.view(bsz, -1),
+                        k=min(cand_size, probs.view(bsz, -1).size(1) - 1),  # -1 so we never select pad
+                        out=(cand_scores, cand_indices),
+                    )
+                    torch.div(cand_indices, self.vocab_size, out=cand_beams)
+                    cand_indices.fmod_(self.vocab_size)
             else:
                 # finalize all active hypotheses once we hit maxlen
                 # pick the hypothesis with the highest prob of EOS right now
@@ -391,7 +402,7 @@ class SequenceGenerator(object):
 
         return finalized
 
-    def _decode(self, tokens, encoder_outs):
+    def _decode(self, tokens, encoder_outs, incremental_states):
         # wrap in Variable
         tokens = utils.volatile_variable(tokens)
 
@@ -399,7 +410,7 @@ class SequenceGenerator(object):
         avg_attn = None
         for model, encoder_out in zip(self.models, encoder_outs):
             with utils.maybe_no_grad():
-                decoder_out, attn = model.decoder(tokens, encoder_out)
+                decoder_out, attn = model.decoder(tokens, encoder_out, incremental_states[model])
             probs = model.get_normalized_probs(decoder_out[:, -1, :], log_probs=False).data
             if avg_probs is None:
                 avg_probs = probs

fairseq/trainer.py

@@ -47,13 +47,13 @@ class Trainer(object):
         self.meters['train_nll_loss'] = AverageMeter()
         self.meters['valid_loss'] = AverageMeter()
         self.meters['valid_nll_loss'] = AverageMeter()
-        self.meters['wps'] = TimeMeter()      # words per second
-        self.meters['ups'] = TimeMeter()      # updates per second
-        self.meters['wpb'] = AverageMeter()   # words per batch
-        self.meters['bsz'] = AverageMeter()   # sentences per batch
-        self.meters['gnorm'] = AverageMeter() # gradient norm
-        self.meters['clip'] = AverageMeter()  # % of updates clipped
-        self.meters['oom'] = AverageMeter()   # out of memory
+        self.meters['wps'] = TimeMeter()       # words per second
+        self.meters['ups'] = TimeMeter()       # updates per second
+        self.meters['wpb'] = AverageMeter()    # words per batch
+        self.meters['bsz'] = AverageMeter()    # sentences per batch
+        self.meters['gnorm'] = AverageMeter()  # gradient norm
+        self.meters['clip'] = AverageMeter()   # % of updates clipped
+        self.meters['oom'] = AverageMeter()    # out of memory
 
         self._max_bsz_seen = 0
         self._num_updates = 0
@@ -190,7 +190,7 @@ class Trainer(object):
 
         # clip grads
         if self.args.clip_norm > 0:
-            grad_norm = torch.nn.utils.clip_grad_norm(self.model.parameters(), self.args.clip_norm)
+            grad_norm = utils.item(torch.nn.utils.clip_grad_norm(self.model.parameters(), self.args.clip_norm))
         else:
             grad_norm = math.sqrt(sum(p.grad.data.norm()**2 for p in self.model.parameters()))
 

fairseq/utils.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree. An additional grant of patent rights
 # can be found in the PATENTS file in the same directory.
 
+from collections import defaultdict
 import contextlib
 import logging
 import os
@@ -198,6 +199,36 @@ def make_variable(sample, volatile=False, cuda=False):
     return _make_variable(sample)
 
 
+INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0)
+
+
+def _get_full_incremental_state_key(module_instance, key):
+    module_name = module_instance.__class__.__name__
+
+    # assign a unique ID to each module instance, so that incremental state is
+    # not shared across module instances
+    if not hasattr(module_instance, '_fairseq_instance_id'):
+        INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1
+        module_instance._fairseq_instance_id = INCREMENTAL_STATE_INSTANCE_ID[module_name]
+
+    return '{}.{}.{}'.format(module_name, module_instance._fairseq_instance_id, key)
+
+
+def get_incremental_state(module, incremental_state, key):
+    """Helper for getting incremental state for an nn.Module."""
+    full_key = _get_full_incremental_state_key(module, key)
+    if incremental_state is None or full_key not in incremental_state:
+        return None
+    return incremental_state[full_key]
+
+
+def set_incremental_state(module, incremental_state, key, value):
+    """Helper for setting incremental state for an nn.Module."""
+    if incremental_state is not None:
+        full_key = _get_full_incremental_state_key(module, key)
+        incremental_state[full_key] = value
+
+
 def load_align_dict(replace_unk):
     if replace_unk is None:
         align_dict = None
@@ -273,3 +304,10 @@ def convert_padding_direction(
     else:
         index = torch.remainder(range + num_pads, max_len)
     return src_tokens.gather(1, index)
+
+def item(tensor):
+    if hasattr(tensor, 'item'):
+        return tensor.item()
+    if hasattr(tensor, '__getitem__'):
+        return tensor[0]
+    return tensor

generate.py

@@ -18,8 +18,6 @@ def main(args):
     print(args)
 
     use_cuda = torch.cuda.is_available() and not args.cpu
-    if hasattr(torch, 'set_grad_enabled'):
-        torch.set_grad_enabled(False)
 
     # Load dataset
     if args.replace_unk is None:
@@ -92,7 +90,7 @@ def main(args):
         else:
             translations = translator.generate_batched_itr(
                 t, maxlen_a=args.max_len_a, maxlen_b=args.max_len_b,
-                cuda=use_cuda, timer=gen_timer)
+                cuda=use_cuda, timer=gen_timer, prefix_size=args.prefix_size)
         wps_meter = TimeMeter()
         for sample_id, src_tokens, target_tokens, hypos in translations:
             # Process input and ground truth

interactive.py

@@ -18,8 +18,6 @@ def main(args):
     print(args)
 
     use_cuda = torch.cuda.is_available() and not args.cpu
-    if hasattr(torch, 'set_grad_enabled'):
-        torch.set_grad_enabled(False)
 
     # Load ensemble
     print('| loading model(s) from {}'.format(', '.join(args.path)))

preprocess.py

@@ -39,6 +39,7 @@ def get_parser():
     parser.add_argument('--joined-dictionary', action='store_true', help='Generate joined dictionary')
     return parser
 
+
 def main(args):
     print(args)
     os.makedirs(args.destdir, exist_ok=True)

singleprocess_train.py

@@ -177,6 +177,7 @@ def get_training_stats(trainer):
     stats['lr'] = trainer.get_lr()
     stats['gnorm'] = '{:.3f}'.format(trainer.get_meter('gnorm').avg)
     stats['clip'] = '{:.0%}'.format(trainer.get_meter('clip').avg)
+    stats['oom'] = trainer.get_meter('oom').avg
     return stats
 
 

tests/test_binaries.py

@@ -16,7 +16,10 @@ import torch
 
 from fairseq import options
 
-import preprocess, train, generate, interactive
+import preprocess
+import train
+import generate
+import interactive
 
 
 class TestBinaries(unittest.TestCase):
@@ -80,6 +83,7 @@ class TestBinaries(unittest.TestCase):
                 '--save-dir', data_dir,
                 '--max-epoch', '1',
                 '--no-progress-bar',
+                '--distributed-world-size', '1',
             ],
         )
         train.main(train_args)

tests/utils.py

@@ -9,7 +9,7 @@
 import torch
 from torch.autograd import Variable
 
-from fairseq import data, dictionary
+from fairseq import data, dictionary, utils
 from fairseq.models import (
     FairseqEncoder,
     FairseqIncrementalDecoder,
@@ -96,24 +96,21 @@ class TestIncrementalDecoder(FairseqIncrementalDecoder):
         args.max_decoder_positions = getattr(args, 'max_decoder_positions', 100)
         self.args = args
 
-    def forward(self, prev_output_tokens, encoder_out):
-        if self._is_incremental_eval:
+    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
+        if incremental_state is not None:
             prev_output_tokens = prev_output_tokens[:, -1:]
-        return self._forward(prev_output_tokens, encoder_out)
-
-    def _forward(self, prev_output_tokens, encoder_out):
         bbsz = prev_output_tokens.size(0)
         vocab = len(self.dictionary)
         src_len = encoder_out.size(1)
         tgt_len = prev_output_tokens.size(1)
 
         # determine number of steps
-        if self._is_incremental_eval:
+        if incremental_state is not None:
             # cache step number
-            step = self.get_incremental_state('step')
+            step = utils.get_incremental_state(self, incremental_state, 'step')
             if step is None:
                 step = 0
-            self.set_incremental_state('step', step + 1)
+            utils.set_incremental_state(self, incremental_state, 'step', step + 1)
             steps = [step]
         else:
             steps = list(range(tgt_len))

train.py

@@ -6,23 +6,23 @@
 # the root directory of this source tree. An additional grant of patent rights
 # can be found in the PATENTS file in the same directory.
 
-import torch
-
 from fairseq import options
 
 from distributed_train import main as distributed_main
 from multiprocessing_train import main as multiprocessing_main
 from singleprocess_train import main as singleprocess_main
 
+
 def main(args):
     if args.distributed_port > 0 \
             or args.distributed_init_method is not None:
         distributed_main(args)
-    elif torch.cuda.device_count() > 1:
+    elif args.distributed_world_size > 1:
         multiprocessing_main(args)
     else:
         singleprocess_main(args)
 
+
 if __name__ == '__main__':
     parser = options.get_training_parser()
     args = options.parse_args_and_arch(parser)