Seq2Seq Agent

The Seq2Seq agent takes an input sequence and produces an output sequence.

The agent supports encoding and decoding via a variety of RNN flavors, including LSTMs and GRUs. The agent supports encoding and decoding via a variety of RNN flavors, including LSTMs and GRUs. It also supports numerous decoding strategies, like beam search and nucleus decoding.

The following papers outline more information regarding this model:

• Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau et al. 2014)

• Sequence to Sequence Learning with Neural Networks (Sutskever et al. 2014)

• Effective Approaches to Attention-based Neural Machine Translation (Luong et al. 2015)

Seq2seqAgent Options

optional arguments

Argument	Description
--gpu-beam-blocking	Set to use CUDA kernel for beam search ngram blocking Default: False.
--verbose-topk	Return the topk logits in the act message, if verbose mode is set. Default: -1.

Seq2Seq Arguments

Argument	Description
--hiddensize, --hs	Size of the hidden layers Default: 128.
--embeddingsize, --esz	Size of the token embeddings Default: 128.
--numlayers, --nl	Number of hidden layers Default: 2.
--dropout, --dr	Dropout rate Default: 0.1.
--bidirectional, --bi	Whether to encode the context with a bidirectional rnn Default: False.
--attention, --att	Choices: none, concat, general, local. If set local, also set attention-length. (see arxiv.org/abs/1508.04025) Choices: none, concat, general, dot, local. Default: none.
--attention-length, --attl	Length of local attention. Default: 48.
--attention-time	Whether to apply attention before or after decoding. Choices: pre, post. Default: post.
--rnn-class, --rnn	Choose between different types of RNNs. Choices: rnn, gru, lstm. Default: lstm.
--decoder, --dec	Choose between different decoder modules. Default “same” uses same class as encoder, while “shared” also uses the same weights. Note that shared disabled some encoder options–in particular, bidirectionality. Choices: same, shared. Default: same.
--lookuptable, --lt	The encoder, decoder, and output modules can share weights, or not. Unique has independent embeddings for each. Enc_dec shares the embedding for the encoder and decoder. Dec_out shares decoder embedding and output weights. All shares all three weights. Choices: unique, enc_dec, dec_out, all. Default: unique.
--numsoftmax, --soft	Default 1, if greater then uses mixture of softmax (see arxiv.org/abs/1711.03953). Default: 1.
--input-dropout, --idr	Probability of replacing tokens with UNK in training. Default: 0.0.

Torch Generator Agent

Argument	Description
--beam-size	Beam size, if 1 then greedy search Default: 1.
--beam-min-length	Minimum length of prediction to be generated by the beam search Default: 1.
--beam-context-block-ngram	Size n-grams to block in beam search from the context. val <= 0 implies no blocking Default: -1.
--beam-block-ngram	Size n-grams to block in beam search. val <= 0 implies no blocking Default: -1.
--beam-block-full-context	Block n-grams from the full history context. Specify False to block up to m tokens in the past, where m is truncation parameter for agent Default: True.
--beam-length-penalty	Applies a length penalty. Set to 0 for no penalty. Default: 0.65.
--inference	Generation algorithm Choices: beam, nucleus, delayedbeam, greedy, delayednucleusbeam, topk, factual_nucleus. Default: greedy.
--topk	K used in Top K sampling Default: 10.
--topp	P used in nucleus sampling Default: 0.9.
--beam-delay	Used in delayedbeam search Default: 30.
--lambda-decay	Decay factor in factual nucleus sampling Default: 0.9.
--omega-bound	Lower bound in factual nucleus sampling Default: 0.3.
--p-reset	Whether to reset p value in factual nucleus at full stops Default: True.
--beam-block-list-filename	Load a text file of hard blocks for beam search to never say.
--temperature	Temperature to add during decoding Default: 1.0.
--compute-tokenized-bleu	If true, compute tokenized bleu scores Default: False.

TorchAgent Arguments

Argument	Description
--interactive-mode, --i	Whether in full interactive mode or not, which means generating text or retrieving from a full set of candidates, which is necessary to actually do full dialogue. However, during training or quick validation (e.g. PPL for generation or ranking a few candidates for ranking models) you might want these set to off. Typically, scripts can set their preferred default behavior at the start, e.g. eval scripts. Default: False.
--embedding-type, --emb	Choose between different strategies for initializing word embeddings. Default is random, but can also preinitialize from Glove or Fasttext. Preinitialized embeddings can also be fixed so they are not updated during training. Choices: random, glove, glove-fixed, fasttext, fasttext-fixed, fasttext_cc, fasttext_cc-fixed. Default: random.
--embedding-projection, --embp	If pretrained embeddings have a different dimensionality than your embedding size, strategy for projecting to the correct size. If the dimensions are the same, this is ignored unless you append “-force” to your choice. Default: random.
--fp16	Use fp16 computations. Default: False.
--fp16-impl	Implementation of FP16 to use Choices: safe, mem_efficient. Default: safe.
--rank-candidates, --rc	Whether the model should parse candidates for ranking. Default: False.
--truncate, --tr	Truncate input lengths to increase speed / use less memory. Default: -1.
--text-truncate	Text input truncation length: if not specified, this will default to truncate
--label-truncate	Label truncation length: if not specified, this will default to truncate
--history-reversed	Reverse the history Default: False.
--history-size, --histsz	Number of past dialog utterances to remember. Default: -1.
--person-tokens, --pt	Add person tokens to history. adds p1 in front of input text and p2 in front of past labels when available or past utterances generated by the model. these are added to the dictionary during initialization. Default: False.
--split-lines	Split the dialogue history on newlines and save in separate vectors Default: False.
--delimiter	Join history lines with this token, defaults to newline Default: \n.
--special-tok-lst	Comma separated list of special tokens. In case of ambiguous parses from special tokens, the ordering provided in this arg sets precedence.
-gpu, --gpu	Which GPU to use Default: -1.
--no-cuda	Disable GPUs even if available. otherwise, will use GPUs if available on the device. Default: False.

Optimizer Arguments

Argument	Description
--optimizer, --opt	Optimizer choice. Possible values: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Choices: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Default: sgd.
--learningrate, --lr	Learning rate Default: 1.
--gradient-clip, --clip	Gradient clipping using l2 norm Default: 0.1.
--adafactor-eps	Epsilon values for adafactor optimizer: regularization constants for square gradient and parameter scale respectively Default: 1e-30,1e-3. Recommended: 1e-30,1e-3.
--momentum, --mom	If applicable, momentum value for optimizer. Default: 0.
--nesterov	If applicable, whether to use nesterov momentum. Default: True.
--nus, --nu	If applicable, nu value(s) for optimizer. can use a single value like 0.7 or a comma-separated tuple like 0.7,1.0 Default: 0.7.
--betas, --beta	If applicable, beta value(s) for optimizer. can use a single value like 0.9 or a comma-separated tuple like 0.9,0.999 Default: 0.9,0.999.
--weight-decay, --wdecay	Weight decay on the weights.

BPEHelper Arguments

Argument	Description
--bpe-vocab	Path to pre-trained tokenizer vocab
--bpe-merge	Path to pre-trained tokenizer merge
--bpe-dropout	Use BPE dropout during training.

Learning Rate Scheduler

Argument	Description
--lr-scheduler	Learning rate scheduler. Choices: reduceonplateau, none, fixed, invsqrt, cosine, linear. Default: reduceonplateau.
--lr-scheduler-patience	LR scheduler patience. In number of validation runs. If using fixed scheduler, LR is decayed every validations. Default: 3.
--lr-scheduler-decay	Decay factor for LR scheduler, or how much LR is multiplied by when it is lowered. Default: 0.5.
--invsqrt-lr-decay-gamma	Constant used only to find the lr multiplier for the invsqrt scheduler. Must be set for –lr-scheduler invsqrt Default: -1.