Commit 50fdf591 authored by Myle Ott's avatar Myle Ott
Browse files

Don't call forward directly (prefer module(x) to module.forward(x))

parent 5c7f4954

fairseq/models/fairseq_incremental_decoder.py

+4 −7
Changes for fairseq/models/fairseq_incremental_decoder.py: 4 added lines, 7 removed lines.
Original line number Diff line number Diff line
@@ -18,12 +18,10 @@ class FairseqIncrementalDecoder(FairseqDecoder): 
        self._incremental_state = {}



    def forward(self, tokens, encoder_out):

        if self._is_incremental_eval:

            raise NotImplementedError

        else:

            raise NotImplementedError



    def incremental_forward(self, tokens, encoder_out):

        """Forward pass for one time step."""

        # keep only the last token for incremental forward pass

        return self.forward(tokens[:, -1:], encoder_out)



    def incremental_inference(self):

        """Context manager for incremental inference.
@@ -38,8 +36,7 @@ class FairseqIncrementalDecoder(FairseqDecoder): 
        ```

        with model.decoder.incremental_inference():

            for step in range(maxlen):

                out, _ = model.decoder.incremental_forward(

                    tokens[:, :step], encoder_out)

                out, _ = model.decoder(tokens[:, :step], encoder_out)

                probs = torch.nn.functional.log_softmax(out[:, -1, :])

        ```

        """

fairseq/models/fconv.py

+7 −0
Changes for fairseq/models/fconv.py: 7 added lines, 0 removed lines.
Original line number Diff line number Diff line
@@ -185,6 +185,13 @@ class FConvDecoder(FairseqIncrementalDecoder): 
        self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)



    def forward(self, input_tokens, encoder_out):

        if self._is_incremental_eval:

            return self.incremental_forward(input_tokens, encoder_out)

        else:

            return self.batch_forward(input_tokens, encoder_out)



    def batch_forward(self, input_tokens, encoder_out):

        """Forward pass for decoding multiple time steps in batch mode."""

        positions = Variable(make_positions(input_tokens.data, self.dictionary.pad(),

                                            left_pad=LanguagePairDataset.LEFT_PAD_TARGET))

        return self._forward(input_tokens, positions, encoder_out)

fairseq/modules/linearized_convolution.py

+0 −7
Changes for fairseq/modules/linearized_convolution.py: 0 added lines, 7 removed lines.
Original line number Diff line number Diff line
@@ -50,13 +50,6 @@ class LinearizedConvolution(ConvTBC): 
        call reorder_incremental_state. To apply to fresh inputs, call

        clear_incremental_state.

        """

        if self.training or not self._is_incremental_eval:

            raise RuntimeError('incremental_forward only supports incremental evaluation')



        # run forward pre hooks (e.g., weight norm)

        for hook in self._forward_pre_hooks.values():

            hook(self, input)



        # reshape weight

        weight = self._get_linearized_weight()

        kw = self.kernel_size[0]

fairseq/sequence_generator.py

+1 −4
Changes for fairseq/sequence_generator.py: 1 added line, 4 removed lines.
Original line number Diff line number Diff line
@@ -325,10 +325,7 @@ class SequenceGenerator(object): 
        avg_probs = None

        avg_attn = None

        for model, encoder_out in zip(self.models, encoder_outs):

            if isinstance(model.decoder, FairseqIncrementalDecoder):

                decoder_out, attn = model.decoder.incremental_forward(tokens, encoder_out)

            else:

                decoder_out, attn = model.decoder.forward(tokens, encoder_out)

            decoder_out, attn = model.decoder(tokens, encoder_out)

            probs = F.softmax(decoder_out[:, -1, :]).data

            attn = attn[:, -1, :].data

            if avg_probs is None or avg_attn is None: