Small fixes

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/835

Differential Revision: D16904038

Pulled By: myleott

fbshipit-source-id: 2c9d0b913f8d688297ac80fcabd905bd1397f66a

eval_lm.py

@@ -200,8 +200,10 @@ def main(parsed_args):
                             is_bpe = False
                             w = ''
                     if args.output_word_probs:
-                        print(str(int(sample_id)) + " " +
-                                  ('\t'.join('{} [{:2f}]'.format(x[0], x[1]) for x in word_prob)))
+                        print(
+                            str(int(sample_id)) + " "
+                            + ('\t'.join('{} [{:2f}]'.format(x[0], x[1]) for x in word_prob))
+                        )
 
             wps_meter.update(sample['ntokens'])
             t.log({'wps': round(wps_meter.avg)})

examples/__init__.py

-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
+# Copyright (c) Facebook, Inc. and its affiliates.
 #
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 
-__version__ = '0.7.2'
+__version__ = '0.8.0'
 
-import examples.noisychannel # noqa
+import examples.noisychannel  # noqa

examples/noisychannel/__init__.py

-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
+# Copyright (c) Facebook, Inc. and its affiliates.
 #
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 
-from .rerank_options import *
+from .rerank_options import *  # noqa

examples/noisychannel/rerank.py

@@ -77,9 +77,11 @@ def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write
 
         for key in range(len(gen_keys)):
             if args.prefix_len is None:
-                assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], \
-                    ("pred and rescore hypo mismatch: i: " + str(key) + ", " + str(hypo_lst[key]) + str(gen_keys[key]) +
-                    str(gen_output.no_bpe_hypo[key]))
+                assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
+                    "pred and rescore hypo mismatch: i: " + str(key) + ", "
+                    + str(hypo_lst[key]) + str(gen_keys[key])
+                    + str(gen_output.no_bpe_hypo[key])
+                )
                 sys_tok = dict.encode_line(hypo_lst[key])
                 ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
                 scorer.add(ref_tok, sys_tok)

examples/noisychannel/rerank_generate.py

 #!/usr/bin/env python3 -u
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
+# Copyright (c) Facebook, Inc. and its affiliates.
 #
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 
-import rerank_utils
+"""
+Generate n-best translations using a trained model.
+"""
+
+from contextlib import redirect_stdout
 import os
 import subprocess
+
+import rerank_utils
 from examples.noisychannel import rerank_options
 from fairseq import options
 import generate
 import preprocess
-from contextlib import redirect_stdout
 
-"""
-Generate n-best translations using a trained model.
-"""
 
 def gen_and_reprocess_nbest(args):
     if args.score_dict_dir is None:

examples/noisychannel/rerank_options.py

-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
+# Copyright (c) Facebook, Inc. and its affiliates.
 #
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 
 from fairseq import options
 

examples/noisychannel/rerank_tune.py

@@ -27,12 +27,14 @@ def random_search(args):
     param_values += initial_params
     random.seed(args.seed)
 
-    random_params = np.array([[random.uniform(args.lower_bound[i], args.upper_bound[i])
-                               for i in range(len(args.tune_param))]
-                               for k in range(args.num_trials)])
-    set_params = np.array([[initial_params[i][0]
-                            for i in range(len(tuneable_parameters))]
-                            for k in range(args.num_trials)])
+    random_params = np.array([
+        [random.uniform(args.lower_bound[i], args.upper_bound[i]) for i in range(len(args.tune_param))]
+        for k in range(args.num_trials)
+    ])
+    set_params = np.array([
+        [initial_params[i][0] for i in range(len(tuneable_parameters))]
+        for k in range(args.num_trials)
+    ])
     random_params = np.concatenate((random_params, set_params), 1)
 
     rerank_args = vars(args).copy()

examples/noisychannel/rerank_utils.py

@@ -128,8 +128,8 @@ def write_reprocessed(sources, hypos, targets, source_outfile,
         "in writing reprocessed, only one type of prefix may be used"
 
     with open(source_outfile, 'w') as source_file, \
-         open(hypo_outfile, 'w') as hypo_file, \
-         open(target_outfile, 'w') as target_file:
+            open(hypo_outfile, 'w') as hypo_file, \
+            open(target_outfile, 'w') as target_file:
 
         assert len(sources) == len(hypos), "sources and hypos list length mismatch"
         if right_to_left:

examples/roberta/wsc/wsc_task.py

@@ -270,6 +270,7 @@ class WinograndeTask(WSCTask):
     Task for WinoGrande dataset. Efficient implementation for Winograd schema
     tasks with exactly two candidates, one of which is correct.
     """
+
     @classmethod
     def setup_task(cls, args, **kwargs):
         assert args.criterion == 'winogrande', 'Must set --criterion=winogrande'
@@ -280,7 +281,6 @@ class WinograndeTask(WSCTask):
 
         return cls(args, vocab)
 
-
     def load_dataset(self, split, epoch=0, combine=False, data_path=None, return_only=False, **kwargs):
         """Load a given dataset split.
 
@@ -299,7 +299,7 @@ class WinograndeTask(WSCTask):
         candidate_masks = []
         candidate_lengths = []
 
-        itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=split=='test')
+        itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=(split == 'test'))
 
         for sample in itr:
             sentence, pronoun_span, query, cand_text = sample

examples/speech_recognition/models/vggtransformer.py

@@ -13,7 +13,7 @@ from fairseq import utils
 from fairseq.models import (
     FairseqEncoder,
     FairseqIncrementalDecoder,
-    FairseqModel,
+    FairseqEncoderDecoderModel,
     register_model,
     register_model_architecture,
 )
@@ -23,7 +23,7 @@ from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer, VG
 
 
 @register_model("asr_vggtransformer")
-class VGGTransformerModel(FairseqModel):
+class VGGTransformerModel(FairseqEncoderDecoderModel):
     """
     Transformers with convolutional context for ASR
     https://arxiv.org/abs/1904.11660

fairseq/models/lightconv.py

@@ -4,7 +4,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import math
-import sys
 
 import torch
 import torch.nn as nn
@@ -174,6 +173,7 @@ class LightConvModel(FairseqEncoderDecoderModel):
         decoder = LightConvDecoder(args, tgt_dict, decoder_embed_tokens)
         return LightConvModel(encoder, decoder)
 
+
 class LightConvEncoder(FairseqEncoder):
     """
     LightConv encoder consisting of *args.encoder_layers* layers. Each layer

fairseq/modules/__init__.py

@@ -10,14 +10,12 @@ from .character_token_embedder import CharacterTokenEmbedder
 from .conv_tbc import ConvTBC
 from .downsampled_multihead_attention import DownsampledMultiHeadAttention
 from .dynamic_convolution import DynamicConv, DynamicConv1dTBC
-#from .dynamicconv_layer import DynamicconvLayer
 from .gelu import gelu, gelu_accurate
 from .grad_multiply import GradMultiply
 from .highway import Highway
 from .layer_norm import LayerNorm
 from .learned_positional_embedding import LearnedPositionalEmbedding
 from .lightweight_convolution import LightweightConv, LightweightConv1dTBC
-#from .lightconv_layer import LightconvLayer
 from .linearized_convolution import LinearizedConvolution
 from .logsumexp_moe import LogSumExpMoE
 from .mean_pool_gating_network import MeanPoolGatingNetwork
@@ -38,7 +36,6 @@ __all__ = [
     'CharacterTokenEmbedder',
     'ConvTBC',
     'DownsampledMultiHeadAttention',
-#    'DyamicconvLayer',
     'DynamicConv1dTBC',
     'DynamicConv',
     'gelu',
@@ -47,7 +44,6 @@ __all__ = [
     'Highway',
     'LayerNorm',
     'LearnedPositionalEmbedding',
-#    'LightconvLayer',
     'LightweightConv1dTBC',
     'LightweightConv',
     'LinearizedConvolution',

fairseq/modules/cuda_utils.cu

 /**
- * Copyright (c) 2018-present, Facebook, Inc.
- * All rights reserved.
- *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * 
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
  */
 
 

fairseq/modules/dynamic_convolution.py

@@ -10,6 +10,7 @@ import torch.nn.functional as F
 from fairseq import utils
 from .unfold import unfold1d
 
+
 def DynamicConv(input_size, kernel_size=1, padding_l=None, num_heads=1,
                 weight_dropout=0., weight_softmax=False,
                 renorm_padding=False, bias=False, conv_bias=False,
@@ -28,6 +29,7 @@ def DynamicConv(input_size, kernel_size=1, padding_l=None, num_heads=1,
                             weight_dropout=weight_dropout,
                             weight_softmax=weight_softmax, bias=bias)
 
+
 def Linear(in_features, out_features, bias=True):
     m = nn.Linear(in_features, out_features, bias)
     nn.init.xavier_uniform_(m.weight)
@@ -209,7 +211,7 @@ class DynamicConv1dTBC(nn.Module):
             # turn the convolution filters into band matrices
             weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False)
             weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
-            weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
+            weight_expanded = weight_expanded.narrow(2, P, T)  # B*H x T x T
         output = torch.bmm(weight_expanded, x)
         output = output.transpose(0, 1).contiguous().view(T, B, C)
         return output

fairseq/modules/dynamicconv_layer/__init__.py

-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
+# Copyright (c) Facebook, Inc. and its affiliates.
 #
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 
-from .dynamicconv_layer import DynamicconvLayer
+from .dynamicconv_layer import DynamicconvLayer  # noqa

fairseq/modules/dynamicconv_layer/cuda_function_gen.py

-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
+# Copyright (c) Facebook, Inc. and its affiliates.
 #
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
 
 
 def gen_forward():
@@ -13,9 +11,10 @@ def gen_forward():
 
     head = """
 /**
- * Copyright (c) 2018-present, Facebook, Inc.
- * All rights reserved.
+ * Copyright (c) Facebook, Inc. and its affiliates.
  *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
  */
 
 #include "dynamicconv_cuda.cuh"
@@ -103,9 +102,10 @@ def gen_backward():
 
     head = """
 /**
- * Copyright (c) 2018-present, Facebook, Inc.
- * All rights reserved.
+ * Copyright (c) Facebook, Inc. and its affiliates.
  *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
  */
 
 #include "dynamicconv_cuda.cuh"

fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp

+/**
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
 #include <torch/extension.h>
 #include <vector>
 

fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh

 /**
- * Copyright (c) 2018-present, Facebook, Inc.
- * All rights reserved.
- *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * 
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
  */
+
 #include <ATen/ATen.h>
 #include <c10/cuda/CUDAStream.h>
 

fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu

 /**
- * Copyright (c) 2018-present, Facebook, Inc.
- * All rights reserved.
- *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * 
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
  */
 
 #include "dynamicconv_cuda.cuh"

fairseq/modules/dynamicconv_layer/dynamicconv_layer.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
 import torch
 from torch import nn
 from torch.autograd import Function
 import torch.nn.functional as F
+
 import dynamicconv_cuda
 from fairseq import utils
+from fairseq.modules.unfold import unfold1d
 
 
 class dynamicconvFunction(Function):
@@ -68,7 +75,7 @@ class DynamicconvLayer(nn.Module):
 
         T, B, C = x.size()
         K, H = self.kernel_size, self.num_heads
-        R = C // H
+        # R = C // H
 
         # during inference time, incremental BMM is faster
         if incremental_state is not None:
@@ -199,7 +206,7 @@ class DynamicconvLayer(nn.Module):
             # turn the convolution filters into band matrices
             weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False)
             weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
-            weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
+            weight_expanded = weight_expanded.narrow(2, P, T)  # B*H x T x T
         output = torch.bmm(weight_expanded, x)
         output = output.transpose(0, 1).contiguous().view(T, B, C)
         return output