wehub-resource-sync
111 lines
4.5 KiB
Python
111 lines
4.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn.functional as F
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from fairseq import utils
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from fairseq.incremental_decoding_utils import with_incremental_state
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from .conv_tbc import ConvTBC
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from typing import Dict, Optional
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from torch import Tensor
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@with_incremental_state
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class LinearizedConvolution(ConvTBC):
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"""An optimized version of nn.Conv1d.
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At training time, this module uses ConvTBC, which is an optimized version
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of Conv1d. At inference time, it optimizes incremental generation (i.e.,
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one time step at a time) by replacing the convolutions with linear layers.
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Note that the input order changes from training to inference.
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"""
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def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
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super().__init__(in_channels, out_channels, kernel_size, **kwargs)
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self._linearized_weight = None
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self.register_backward_hook(self._clear_linearized_weight)
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def state_dict(self, destination=None, prefix="", keep_vars=False):
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state = ConvTBC.state_dict(self, destination, prefix, keep_vars=keep_vars)
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# don't store redundant _linearized_weight in checkpoints
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if prefix + "_linearized_weight" in state:
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del state[prefix + "_linearized_weight"]
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return state
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def upgrade_state_dict_named(self, state_dict, name):
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prefix = name + "." if name != "" else ""
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if prefix + "_linearized_weight" in state_dict:
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del state_dict[prefix + "_linearized_weight"]
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@torch.jit.export
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def forward(self, input, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None):
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"""
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Args:
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incremental_state: Used to buffer signal; if not None, then input is
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expected to contain a single frame. If the input order changes
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between time steps, call reorder_incremental_state.
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Input:
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Time x Batch x Channel during training
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Batch x Time x Channel during inference
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"""
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if incremental_state is None:
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output = self.conv_tbc(input)
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if self.kernel_size[0] > 1 and self.padding[0] > 0:
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# remove future timesteps added by padding
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output = output[: -self.padding[0], :, :]
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return output
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# reshape weight
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weight = self._get_linearized_weight()
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kw = self.kernel_size[0]
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bsz = input.size(0) # input: bsz x len x dim
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if kw > 1:
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input = input.data
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input_buffer = self._get_input_buffer(incremental_state)
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if input_buffer is None:
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input_buffer = input.new(bsz, kw, input.size(2)).zero_()
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self._set_input_buffer(incremental_state, input_buffer)
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else:
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# shift buffer
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input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone()
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# append next input
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input_buffer[:, -1, :] = input[:, -1, :]
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input = input_buffer
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with torch.no_grad():
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output = F.linear(input.view(bsz, -1), weight, self.bias)
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return output.view(bsz, 1, -1)
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@torch.jit.unused
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def reorder_incremental_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_order):
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input_buffer = self._get_input_buffer(incremental_state)
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if input_buffer is not None:
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input_buffer = input_buffer.index_select(0, new_order)
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self._set_input_buffer(incremental_state, input_buffer)
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@torch.jit.unused
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def _get_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]):
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return utils.get_incremental_state(self, incremental_state, "input_buffer")
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@torch.jit.unused
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def _set_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_buffer):
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return utils.set_incremental_state(
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self, incremental_state, "input_buffer", new_buffer
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)
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@torch.jit.unused
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def _get_linearized_weight(self):
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if self._linearized_weight is None:
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kw = self.kernel_size[0]
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weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous()
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assert weight.size() == (self.out_channels, kw, self.in_channels)
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return weight.view(self.out_channels, -1)
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return self._linearized_weight
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@torch.jit.unused
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def _clear_linearized_weight(self, *args):
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self._linearized_weight = None
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