Move MoE files into examples (#1040)

Summary: Pull Request resolved: fairinternal/fairseq-py#1040

Differential Revision: D20030279

Pulled By: myleott

fbshipit-source-id: 76b48a62409020039225cf98e8fcf7a494d0b7f8

Author: Myle Ott

examples/speech_recognition/criterions/ASG_loss.py

@@ -13,8 +13,6 @@
 from fairseq.criterions import FairseqCriterion, register_criterion
 from examples.speech_recognition.data.replabels import pack_replabels
 
-from wav2letter.criterion import ASGLoss, CriterionScaleMode
-
 
 @register_criterion("asg_loss")
 class ASGCriterion(FairseqCriterion):
@@ -43,6 +41,8 @@ def add_args(parser):
         )
 
     def __init__(self, args, task):
+        from wav2letter.criterion import ASGLoss, CriterionScaleMode
+
         super().__init__(args, task)
         self.tgt_dict = task.target_dictionary
         self.eos = self.tgt_dict.eos()

examples/speech_recognition/datasets/asr_prep_json.py

@@ -14,14 +14,14 @@
 import json
 import sentencepiece as spm
 import multiprocessing
-import torchaudio
 
 from fairseq.data import Dictionary
 
 MILLISECONDS_TO_SECONDS = 0.001
 
 
 def process_sample(aud_path, lable, utt_id, sp, tgt_dict):
+    import torchaudio
     input = {}
     output = {}
     si, ei = torchaudio.info(aud_path)

examples/speech_recognition/w2l_decoder.py

@@ -13,16 +13,22 @@
 import torch
 from fairseq import utils
 from examples.speech_recognition.data.replabels import unpack_replabels
-from wav2letter.common import create_word_dict, load_words
-from wav2letter.criterion import CpuViterbiPath, get_data_ptr_as_bytes
-from wav2letter.decoder import (
-    CriterionType,
-    DecoderOptions,
-    KenLM,
-    SmearingMode,
-    Trie,
-    WordLMDecoder,
-)
+
+try:
+    from wav2letter.common import create_word_dict, load_words
+    from wav2letter.criterion import CpuViterbiPath, get_data_ptr_as_bytes
+    from wav2letter.decoder import (
+        CriterionType,
+        DecoderOptions,
+        KenLM,
+        SmearingMode,
+        Trie,
+        WordLMDecoder,
+    )
+except ImportError:
+    # wav2letter is a required dependency for the speech_recognition
+    # example, but don't break on import
+    pass
 
 
 class W2lDecoder(object):

examples/translation_moe/README.md

@@ -18,7 +18,7 @@ The following command will train a `hMoElp` model with `3` experts:
 fairseq-train --ddp-backend='no_c10d' \
     data-bin/wmt17_en_de \
     --max-update 100000 \
-    --task translation_moe \
+    --task translation_moe --user-dir examples/translation_moe/src \
     --method hMoElp --mean-pool-gating-network \
     --num-experts 3 \
     --arch transformer_wmt_en_de --share-all-embeddings \
@@ -37,7 +37,7 @@ For example, to generate from expert 0:
 fairseq-generate data-bin/wmt17_en_de \
     --path checkpoints/checkpoint_best.pt \
     --beam 1 --remove-bpe \
-    --task translation_moe \
+    --task translation_moe --user-dir examples/translation_moe/src \
     --method hMoElp --mean-pool-gating-network \
     --num-experts 3 \
     --gen-expert 0
@@ -61,7 +61,7 @@ for EXPERT in $(seq 0 2); do \
         --beam 1 \
         --bpe subword_nmt --bpe-codes $BPE_CODE \
         --buffer-size 500 --max-tokens 6000 \
-        --task translation_moe \
+        --task translation_moe --user-dir examples/translation_moe/src \
         --method hMoElp --mean-pool-gating-network \
         --num-experts 3 \
         --gen-expert $EXPERT ; \

examples/translation_moe/src/__init__.py

@@ -0,0 +1,6 @@
+# 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.
+
+from . import translation_moe  # noqa

fairseq/modules/logsumexp_moe.py renamed to examples/translation_moe/src/logsumexp_moe.py

@@ -5,10 +5,13 @@
 
 import torch
 
-from fairseq import metrics, modules, utils
+from fairseq import metrics, utils
 from fairseq.tasks import register_task
 from fairseq.tasks.translation import TranslationTask
 
+from .logsumexp_moe import LogSumExpMoE
+from .mean_pool_gating_network import MeanPoolGatingNetwork
+
 
 @register_task('translation_moe')
 class TranslationMoETask(TranslationTask):
@@ -100,7 +103,7 @@ def build_model(self, args):
                 else:
                     raise ValueError('Must specify --mean-pool-gating-network-dropout')
 
-                model.gating_network = modules.MeanPoolGatingNetwork(
+                model.gating_network = MeanPoolGatingNetwork(
                     encoder_dim, args.num_experts, dropout,
                 )
             else:
@@ -171,7 +174,7 @@ def get_lprob_yz(winners=None):
             loss = -get_lprob_yz(winners)
         else:
             lprob_yz = get_lprob_yz()  # B x K
-            loss = -modules.LogSumExpMoE.apply(lprob_yz, prob_z_xy, 1)
+            loss = -LogSumExpMoE.apply(lprob_yz, prob_z_xy, 1)
 
         loss = loss.sum()
         sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']

fairseq/modules/mean_pool_gating_network.py renamed to examples/translation_moe/src/mean_pool_gating_network.py

@@ -17,8 +17,6 @@
 from .learned_positional_embedding import LearnedPositionalEmbedding
 from .lightweight_convolution import LightweightConv, LightweightConv1dTBC
 from .linearized_convolution import LinearizedConvolution
-from .logsumexp_moe import LogSumExpMoE
-from .mean_pool_gating_network import MeanPoolGatingNetwork
 from .multihead_attention import MultiheadAttention
 from .positional_embedding import PositionalEmbedding
 from .scalar_bias import ScalarBias
@@ -47,8 +45,6 @@
     'LightweightConv1dTBC',
     'LightweightConv',
     'LinearizedConvolution',
-    'LogSumExpMoE',
-    'MeanPoolGatingNetwork',
     'MultiheadAttention',
     'PositionalEmbedding',
     'ScalarBias',

fairseq/tasks/translation_moe.py renamed to examples/translation_moe/src/translation_moe.py

@@ -113,6 +113,13 @@ def parse_args_and_arch(
 
     from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY
 
+    # Before creating the true parser, we need to import optional user module
+    # in order to eagerly import custom tasks, optimizers, architectures, etc.
+    usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
+    usr_parser.add_argument("--user-dir", default=None)
+    usr_args, _ = usr_parser.parse_known_args(input_args)
+    utils.import_user_module(usr_args)
+
     if modify_parser is not None:
         modify_parser(parser)