Improvements to random sampling at decode time.

.travis.yml

@@ -58,6 +58,8 @@ script:
   - head /tmp/speech/src-val.txt > /tmp/speech/src-val-head.txt; head /tmp/speech/tgt-val.txt > /tmp/speech/tgt-val-head.txt; python preprocess.py -data_type audio -src_dir /tmp/speech/an4_dataset -train_src /tmp/speech/src-val-head.txt -train_tgt /tmp/speech/tgt-val-head.txt -valid_src /tmp/speech/src-val-head.txt -valid_tgt /tmp/speech/tgt-val-head.txt -save_data /tmp/speech/q; python train.py -model_type audio -data /tmp/speech/q -rnn_size 2 -batch_size 10 -word_vec_size 5 -report_every 5 -rnn_size 10 -train_steps 10 && rm -rf /tmp/speech/q*.pt
   # test nmt translation
   - python translate.py -model onmt/tests/test_model2.pt  -src  data/morph/src.valid  -verbose -batch_size 10 -beam_size 10 -tgt data/morph/tgt.valid -out /tmp/trans; diff  data/morph/tgt.valid /tmp/trans
+  # test nmt translation with random sampling
+  - python translate.py -model onmt/tests/test_model2.pt  -src  data/morph/src.valid  -verbose -batch_size 10 -beam_size 1 -seed 1 -random_sampling_topk "-1" -random_sampling_temp 0.0001 -tgt data/morph/tgt.valid -out /tmp/trans; diff  data/morph/tgt.valid /tmp/trans
   # test tool
   - PYTHONPATH=$PYTHONPATH:. python tools/extract_embeddings.py -model onmt/tests/test_model.pt
 

onmt/opts.py

@@ -538,6 +538,8 @@ def translate_opts(parser):
               default=1., type=float,
               help="""If doing random sampling, divide the logits by
                        this before computing softmax during decoding.""")
+    group.add('--seed', '-seed', type=int, default=829,
+              help="Random seed")
 
     group = parser.add_argument_group('Beam')
     group.add('--fast', '-fast', action="store_true",

onmt/tests/pull_request_chk.sh

@@ -171,6 +171,18 @@ ${PYTHON} translate.py -model ${TEST_DIR}/test_model2.pt  \
 		    -out /tmp/trans             >> ${LOG_FILE} 2>&1
 diff ${DATA_DIR}/morph/tgt.valid /tmp/trans
 [ "$?" -eq 0 ] || error_exit
+
+${PYTHON} translate.py -model ${TEST_DIR}/test_model2.pt  \
+		    -src ${DATA_DIR}/morph/src.valid   \
+		    -verbose -batch_size 10     \
+		    -beam_size 1                \
+		    -seed 1                     \
+		    -random_sampling_topk=-1    \
+		    -random_sampling_temp=0.0001    \
+		    -tgt ${DATA_DIR}/morph/tgt.valid   \
+		    -out /tmp/trans             >> ${LOG_FILE} 2>&1
+diff ${DATA_DIR}/morph/tgt.valid /tmp/trans
+[ "$?" -eq 0 ] || error_exit
 echo "Succeeded" | tee -a ${LOG_FILE}
 
 
@@ -186,7 +198,7 @@ ${PYTHON} preprocess.py -train_src /tmp/src-val.txt \
 		     -save_data /tmp/q           \
 		     -src_vocab_size 1000        \
 		     -tgt_vocab_size 1000        \
-		     -max_shard_size 1           \
+		     -shard_size 1           \
              -dynamic_dict               >> ${LOG_FILE} 2>&1
 ${PYTHON} train.py -data /tmp/q -rnn_size 2 -batch_size 10 \
 		-word_vec_size 5 -report_every 5        \

onmt/tests/test_preprocess.py

@@ -108,7 +108,7 @@ def test_method(self):
                   [('dynamic_dict', True),
                    ('share_vocab', True)],
                   [('dynamic_dict', True),
-                   ('max_shard_size', 500000)],
+                   ('shard_size', 500000)],
                   [('src_vocab', '/tmp/src_vocab.txt'),
                    ('tgt_vocab', '/tmp/tgt_vocab.txt')],
                   ]

onmt/train_single.py

@@ -7,7 +7,6 @@
 
 import os
 import glob
-import random
 from itertools import chain
 
 import torch
@@ -18,6 +17,7 @@
     load_fields_from_vocab, old_style_vocab
 from onmt.model_builder import build_model
 from onmt.utils.optimizers import build_optim
+from onmt.utils.misc import set_random_seed
 from onmt.trainer import build_trainer
 from onmt.models import build_model_saver
 from onmt.utils.logging import init_logger, logger
@@ -70,20 +70,9 @@ def training_opt_postprocessing(opt, device_id):
         logger.info("WARNING: You have a CUDA device, \
                     should run with -gpu_ranks")
 
-    if opt.seed > 0:
-        torch.manual_seed(opt.seed)
-        # this one is needed for torchtext random call (shuffled iterator)
-        # in multi gpu it ensures datasets are read in the same order
-        random.seed(opt.seed)
-        # some cudnn methods can be random even after fixing the seed
-        # unless you tell it to be deterministic
-        torch.backends.cudnn.deterministic = True
-
     if device_id >= 0:
         torch.cuda.set_device(device_id)
-        if opt.seed > 0:
-            # These ensure same initialization in multi gpu mode
-            torch.cuda.manual_seed(opt.seed)
+    set_random_seed(opt.seed, device_id >= 0)
 
     return opt
 

onmt/translate/translation_server.py

@@ -14,6 +14,7 @@
 import onmt.opts
 
 from onmt.utils.logging import init_logger
+from onmt.utils.misc import set_random_seed
 from onmt.translate.translator import build_translator
 
 
@@ -207,6 +208,8 @@ def __init__(self, opt, model_id, tokenizer_opt=None, load=False,
         self.loading_lock.set()
         self.running_lock = threading.Semaphore(value=1)
 
+        set_random_seed(opt.seed, opt.cuda)
+
         if load:
             self.load()
 

onmt/translate/translator.py

@@ -16,6 +16,7 @@
 import onmt.inputters as inputters
 import onmt.opts as opts
 import onmt.decoders.ensemble
+from onmt.utils.misc import set_random_seed
 
 
 def build_translator(opt, report_score=True, logger=None, out_file=None):
@@ -128,6 +129,8 @@ def __init__(
                 "scores": [],
                 "log_probs": []}
 
+        set_random_seed(opt.seed, self.cuda)
+
     def translate(
         self,
         src,
@@ -294,12 +297,11 @@ def sample_with_temperature(self, logits, sampling_temp, keep_topk):
             if keep_topk > 0:
                 top_values, top_indices = torch.topk(logits, keep_topk, dim=1)
                 kth_best = top_values[:, -1].view([-1, 1])
-                kth_best = kth_best.repeat([1, logits.shape[1]])
-                kth_best = kth_best.type(torch.cuda.FloatTensor)
+                kth_best = kth_best.repeat([1, logits.shape[1]]).float()
 
                 # Set all logits that are not in the top-k to -1000.
                 # This puts the probabilities close to 0.
-                keep = torch.ge(logits, kth_best).type(torch.cuda.FloatTensor)
+                keep = torch.ge(logits, kth_best).float()
                 logits = (keep * logits) + ((1-keep) * -10000)
 
             dist = torch.distributions.Multinomial(
@@ -326,9 +328,12 @@ def _translate_random_sampling(
         assert self.block_ngram_repeat == 0
 
         batch_size = batch.batch_size
-        vocab = self.fields["tgt"].vocab
-        start_token = vocab.stoi[self.fields["tgt"].init_token]
-        end_token = vocab.stoi[self.fields["tgt"].eos_token]
+
+        tgt_field = self.fields['tgt'][0][1]
+        vocab = tgt_field.vocab
+
+        start_token = vocab.stoi[tgt_field.init_token]
+        end_token = vocab.stoi[tgt_field.eos_token]
 
         # Encoder forward.
         src, enc_states, memory_bank, src_lengths = self._run_encoder(

onmt/utils/__init__.py

@@ -1,10 +1,10 @@
 """Module defining various utilities."""
-from onmt.utils.misc import aeq, use_gpu
+from onmt.utils.misc import aeq, use_gpu, set_random_seed
 from onmt.utils.report_manager import ReportMgr, build_report_manager
 from onmt.utils.statistics import Statistics
 from onmt.utils.optimizers import build_optim, MultipleOptimizer, \
     Optimizer, AdaFactor
 
-__all__ = ["aeq", "use_gpu", "ReportMgr",
+__all__ = ["aeq", "use_gpu", "set_random_seed", "ReportMgr",
            "build_report_manager", "Statistics",
            "build_optim", "MultipleOptimizer", "Optimizer", "AdaFactor"]

onmt/utils/misc.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 
 import torch
+import random
 
 
 def aeq(*args):
@@ -53,3 +54,19 @@ def use_gpu(opt):
     """
     return (hasattr(opt, 'gpu_ranks') and len(opt.gpu_ranks) > 0) or \
         (hasattr(opt, 'gpu') and opt.gpu > -1)
+
+
+def set_random_seed(seed, is_cuda):
+    """Sets the random seed."""
+    if seed > 0:
+        torch.manual_seed(seed)
+        # this one is needed for torchtext random call (shuffled iterator)
+        # in multi gpu it ensures datasets are read in the same order
+        random.seed(seed)
+        # some cudnn methods can be random even after fixing the seed
+        # unless you tell it to be deterministic
+        torch.backends.cudnn.deterministic = True
+
+    if is_cuda and seed > 0:
+        # These ensure same initialization in multi gpu mode
+        torch.cuda.manual_seed(seed)