v0.7.1: fix PyPI setup and tests

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/818

Differential Revision: D15916265

Pulled By: myleott

fbshipit-source-id: c66c0bd988d3472c4150226952f34ee8d4c3db86

docs/conf.py

@@ -60,9 +60,9 @@ github_doc_root = 'https://github.com/pytorch/fairseq/tree/master/docs/'
 # built documents.
 #
 # The short X.Y version.
-version = '0.7.0'
+version = '0.7.1'
 # The full version, including alpha/beta/rc tags.
-release = '0.7.0'
+release = '0.7.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

fairseq/__init__.py

@@ -6,7 +6,7 @@
 # can be found in the PATENTS file in the same directory.
 
 __all__ = ['pdb']
-__version__ = '0.7.0'
+__version__ = '0.7.1'
 
 import fairseq.criterions
 import fairseq.models

fairseq/trainer.py

@@ -233,11 +233,11 @@ class Trainer(object):
         # forward and backward pass
         logging_outputs, sample_sizes, ooms = [], [], 0
         for i, sample in enumerate(samples):
-            sample = self._prepare_sample(sample, self.args.fp16)
+            sample = self._prepare_sample(sample)
             if sample is None:
                 # when sample is None, run forward/backward on a dummy batch
                 # and ignore the resulting gradients
-                sample = self._prepare_sample(self._dummy_batch, self.args.fp16)
+                sample = self._prepare_sample(self._dummy_batch)
                 ignore_grad = True
             else:
                 ignore_grad = False
@@ -381,9 +381,9 @@ class Trainer(object):
             self.model.eval()
             self.criterion.eval()
 
-            sample = self._prepare_sample(sample, self.args.fp16)
+            sample = self._prepare_sample(sample)
             if sample is None:
-                sample = self._prepare_sample(self._dummy_batch, self.args.fp16)
+                sample = self._prepare_sample(self._dummy_batch)
                 ignore_results = True
             else:
                 ignore_results = False
@@ -488,7 +488,7 @@ class Trainer(object):
         self._num_updates = num_updates
         self.lr_step_update()
 
-    def _prepare_sample(self, sample, fp16):
+    def _prepare_sample(self, sample):
         if sample is None or len(sample) == 0:
             return None
 
@@ -500,7 +500,10 @@ class Trainer(object):
                 return t.half()
             return t
 
-        return utils.apply(apply_half, sample) if fp16 else sample
+        if self.args.fp16:
+            sample = utils.apply_to_sample(apply_half, sample)
+
+        return sample
 
     def _set_seed(self):
         # Set seed based on args.seed and the update number so that we get

fairseq/utils.py

@@ -31,26 +31,32 @@ def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
     )
 
 
-def apply(f, sample):
+def apply_to_sample(f, sample):
     if len(sample) == 0:
         return {}
-    if torch.is_tensor(sample):
+
-        return f(sample)
+    def _apply(x):
-    elif isinstance(sample, dict):
+        if torch.is_tensor(x):
-        return {
+            return f(x)
-            key: apply(f, value)
+        elif isinstance(x, dict):
-            for key, value in sample.items()
+            return {
-        }
+                key: _apply(value)
-    elif isinstance(sample, list):
+                for key, value in x.items()
-        return [apply(f, x) for x in sample]
+            }
-    else:
+        elif isinstance(x, list):
-        return sample
+            return [_apply(x) for x in x]
+        else:
+            return x
+
+    return _apply(sample)
 
 
 def move_to_cuda(sample):
+
     def _move_to_cuda(tensor):
-            return tensor.cuda()
+        return tensor.cuda()
-    return apply(_move_to_cuda, sample)
+
+    return apply_to_sample(_move_to_cuda, sample)
 
 
 INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0)

setup.py

@@ -29,7 +29,7 @@ bleu = Extension(
 
 setup(
     name='fairseq',
-    version='0.7.0',
+    version='0.7.1',
     description='Facebook AI Research Sequence-to-Sequence Toolkit',
     url='https://github.com/pytorch/fairseq',
     classifiers=[
@@ -40,6 +40,7 @@ setup(
         'Topic :: Scientific/Engineering :: Artificial Intelligence',
     ],
     long_description=readme,
+    long_description_content_type='text/markdown',
     install_requires=[
         'cffi',
         'numpy',