Commit 8fcdb9b7 authored by Myle Ott's avatar Myle Ott
Browse files

Fix Flake8

parent 2a84f46b

fairseq/distributed_utils.py

+0 −1
Changes for fairseq/distributed_utils.py: 0 added lines, 1 removed line.
Original line number Diff line number Diff line
@@ -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.



import math

import pickle



import torch.distributed

fairseq/fp16_trainer.py

+0 −2
Changes for fairseq/fp16_trainer.py: 0 added lines, 2 removed lines.
Original line number Diff line number Diff line
@@ -9,8 +9,6 @@ 
Train a network on multiple GPUs.

"""



import math



import torch



from fairseq import optim

fairseq/models/fconv.py

+13 −17
Changes for fairseq/models/fconv.py: 13 added lines, 17 removed lines.
Original line number Diff line number Diff line
@@ -357,6 +357,19 @@ class FConvDecoder(FairseqIncrementalDecoder): 


        return x, avg_attn_scores



    def reorder_incremental_state(self, incremental_state, new_order):

        super().reorder_incremental_state(incremental_state, new_order)

        encoder_out = utils.get_incremental_state(self, incremental_state, 'encoder_out')

        if encoder_out is not None:

            encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out)

            utils.set_incremental_state(self, incremental_state, 'encoder_out', encoder_out)



    def reorder_encoder_out(self, encoder_out_dict, new_order):

        if encoder_out_dict['encoder_padding_mask'] is not None:

            encoder_out_dict['encoder_padding_mask'] = \

                encoder_out_dict['encoder_padding_mask'].index_select(0, new_order)

        return encoder_out_dict



    def max_positions(self):

        """Maximum output length supported by the decoder."""

        return self.embed_positions.max_positions()
@@ -401,23 +414,6 @@ class FConvDecoder(FairseqIncrementalDecoder): 
        return x





    def reorder_incremental_state(self, incremental_state, new_order):

        super().reorder_incremental_state(incremental_state, new_order)



        encoder_out = utils.get_incremental_state(self, incremental_state, 'encoder_out')

        if encoder_out is not None:

            def update_enc_out(enc_out):

                return enc_out.index_select(0, new_order)



            encoder_out = tuple([update_enc_out(eo) for eo in encoder_out])

            utils.set_incremental_state(self, incremental_state, 'encoder_out', encoder_out)



    def reorder_encoder_out(self, encoder_out_dict, new_order):

        if encoder_out_dict['encoder_padding_mask'] is not None:

            encoder_out_dict['encoder_padding_mask'] = encoder_out_dict['encoder_padding_mask'].index_select(0, new_order)

        return encoder_out_dict





def Embedding(num_embeddings, embedding_dim, padding_idx):

    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)

    m.weight.data.normal_(0, 0.1)

fairseq/models/lstm.py

+6 −4
Changes for fairseq/models/lstm.py: 6 added lines, 4 removed lines.
Original line number Diff line number Diff line
@@ -373,6 +373,7 @@ class LSTMDecoder(FairseqIncrementalDecoder): 
        return x, attn_scores



    def reorder_incremental_state(self, incremental_state, new_order):

        super().reorder_incremental_state(incremental_state, new_order)

        cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state')

        if cached_state is None:

            return
@@ -382,16 +383,17 @@ class LSTMDecoder(FairseqIncrementalDecoder): 
                return [reorder_state(state_i) for state_i in state]

            return state.index_select(0, new_order)



        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 reorder_encoder_out(self, encoder_out_dict, new_order):

        encoder_out_dict['encoder_out'] = tuple(

            eo.index_select(1, new_order) for eo in encoder_out_dict['encoder_out'])

            eo.index_select(1, new_order)

            for eo in encoder_out_dict['encoder_out']

        )

        if encoder_out_dict['encoder_padding_mask'] is not None:

            encoder_out_dict['encoder_padding_mask'] = encoder_out_dict['encoder_padding_mask'].index_select(1, new_order)

            encoder_out_dict['encoder_padding_mask'] = \

                encoder_out_dict['encoder_padding_mask'].index_select(1, new_order)

        return encoder_out_dict



    def max_positions(self):

fairseq/models/transformer.py

+7 −9
Changes for fairseq/models/transformer.py: 7 added lines, 9 removed lines.
Original line number Diff line number Diff line
@@ -6,6 +6,7 @@ 
# can be found in the PATENTS file in the same directory.



import math



import torch

import torch.nn as nn

import torch.nn.functional as F
@@ -15,7 +16,6 @@ from fairseq.modules import ( 
    LearnedPositionalEmbedding, MultiheadAttention,

    SinusoidalPositionalEmbedding,

)

from fairseq import utils



from . import (

    FairseqIncrementalDecoder, FairseqEncoder, FairseqModel,
@@ -220,6 +220,12 @@ class TransformerDecoder(FairseqIncrementalDecoder): 


        return x, attn



    def reorder_encoder_out(self, encoder_out_dict, new_order):

        if encoder_out_dict['encoder_padding_mask'] is not None:

            encoder_out_dict['encoder_padding_mask'] = \

                encoder_out_dict['encoder_padding_mask'].index_select(0, new_order)

        return encoder_out_dict



    def max_positions(self):

        """Maximum output length supported by the decoder."""

        return self.embed_positions.max_positions()
@@ -233,11 +239,6 @@ class TransformerDecoder(FairseqIncrementalDecoder): 
        return state_dict





    def reorder_encoder_out(self, encoder_out, new_order):

        if encoder_out['encoder_padding_mask'] is not None:

            encoder_out['encoder_padding_mask'] = encoder_out['encoder_padding_mask'].index_select(0, new_order)

        return encoder_out



class TransformerEncoderLayer(nn.Module):

    """Encoder layer block.
@@ -312,7 +313,6 @@ class TransformerDecoderLayer(nn.Module): 
    def forward(self, x, encoder_out, encoder_padding_mask, incremental_state):

        residual = x

        x = self.maybe_layer_norm(0, x, before=True)



        x, _ = self.self_attn(

            query=x,

            key=x,
@@ -327,7 +327,6 @@ class TransformerDecoderLayer(nn.Module): 


        residual = x

        x = self.maybe_layer_norm(1, x, before=True)



        x, attn = self.encoder_attn(

            query=x,

            key=encoder_out,
@@ -336,7 +335,6 @@ class TransformerDecoderLayer(nn.Module): 
            incremental_state=incremental_state,

            static_kv=True,

        )



        x = F.dropout(x, p=self.dropout, training=self.training)

        x = residual + x

        x = self.maybe_layer_norm(1, x, after=True)