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https://github.com/SillyTavern/SillyTavern-Extras.git
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105 lines
3.7 KiB
Python
105 lines
3.7 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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from typing import Dict, List, Optional, Tuple
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import torch.nn as nn
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from fairseq import utils
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from torch import Tensor
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class FairseqDecoder(nn.Module):
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"""Base class for decoders."""
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def __init__(self, dictionary):
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super().__init__()
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self.dictionary = dictionary
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self.onnx_trace = False
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self.adaptive_softmax = None
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def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
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"""
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Args:
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prev_output_tokens (LongTensor): shifted output tokens of shape
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`(batch, tgt_len)`, for teacher forcing
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encoder_out (dict, optional): output from the encoder, used for
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encoder-side attention
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Returns:
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tuple:
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- the decoder's output of shape `(batch, tgt_len, vocab)`
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- a dictionary with any model-specific outputs
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"""
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x, extra = self.extract_features(
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prev_output_tokens, encoder_out=encoder_out, **kwargs
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)
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x = self.output_layer(x)
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return x, extra
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def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs):
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"""
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Returns:
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tuple:
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- the decoder's features of shape `(batch, tgt_len, embed_dim)`
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- a dictionary with any model-specific outputs
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"""
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raise NotImplementedError
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def output_layer(self, features, **kwargs):
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"""
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Project features to the default output size, e.g., vocabulary size.
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Args:
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features (Tensor): features returned by *extract_features*.
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"""
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raise NotImplementedError
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def get_normalized_probs(
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self,
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net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
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log_probs: bool,
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sample: Optional[Dict[str, Tensor]] = None,
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):
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"""Get normalized probabilities (or log probs) from a net's output."""
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return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
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# TorchScript doesn't support super() method so that the scriptable Subclass
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# can't access the base class model in Torchscript.
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# Current workaround is to add a helper function with different name and
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# call the helper function from scriptable Subclass.
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def get_normalized_probs_scriptable(
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self,
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net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
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log_probs: bool,
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sample: Optional[Dict[str, Tensor]] = None,
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):
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"""Get normalized probabilities (or log probs) from a net's output."""
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if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None:
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if sample is not None:
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assert "target" in sample
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target = sample["target"]
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else:
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target = None
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out = self.adaptive_softmax.get_log_prob(net_output[0], target=target)
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return out.exp_() if not log_probs else out
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logits = net_output[0]
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if log_probs:
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return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
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else:
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return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
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def max_positions(self):
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"""Maximum input length supported by the decoder."""
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return 1e6 # an arbitrary large number
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def upgrade_state_dict_named(self, state_dict, name):
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"""Upgrade old state dicts to work with newer code."""
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return state_dict
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def prepare_for_onnx_export_(self):
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self.onnx_trace = True
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