MemNN

This is a memory network agent that inherits from the TorchAgent class. Read more about Memory Networks here.

Basic Examples

Train a memory network on the bAbi task.

parlai train_model -m memnn -t babi:task10k:1 -mf /tmp/memnn_babi.mdl

MemnnAgent Options

MemNN Arguments

Argument	Description
--embedding-size, --esz	Size of token embeddings Default: 128.
--hops, --hops	Number of memory hops Default: 3.
--memsize	Size of memory, set to 0 for “nomemnn” model which just embeds query and candidates and picks most similar candidate Default: 32.
--time-features, --tf	Use time features for memory embeddings Default: True.
--position-encoding, --pe	Use position encoding instead of bag of words embedding 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.

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.

TorchRankerAgent

Argument	Description
--candidates, --cands	The source of candidates during training (see TorchRankerAgent._build_candidates() for details). Choices: batch, inline, fixed, batch-all-cands. Default: inline.
--eval-candidates, --ecands	The source of candidates during evaluation (defaults to the samevalue as –candidates if no flag is given) Choices: batch, inline, fixed, vocab, batch-all-cands. Default: inline.
--interactive-candidates, --icands	The source of candidates during interactive mode. Since in interactive mode, batchsize == 1, we cannot use batch candidates. Choices: fixed, inline, vocab. Default: fixed.
--repeat-blocking-heuristic	Block repeating previous utterances. Helpful for many models that score repeats highly, so switched on by default. Default: True.
--fixed-candidates-path, --fcp	A text file of fixed candidates to use for all examples, one candidate per line
--fixed-candidate-vecs	One of “reuse”, “replace”, or a path to a file with vectors corresponding to the candidates at –fixed-candidates-path. The default path is a /path/to/model-file.<cands_name>, where <cands_name> is the name of the file (not the full path) passed by the flag –fixed-candidates-path. By default, this file is created once and reused. To replace it, use the “replace” option. Default: reuse.
--encode-candidate-vecs	Cache and save the encoding of the candidate vecs. This might be used when interacting with the model in real time or evaluating on fixed candidate set when the encoding of the candidates is independent of the input. Default: True.
--init-model	Initialize model with weights from this file.
--train-predict	Get predictions and calculate mean rank during the train step. Turning this on may slow down training. Default: False.
--cap-num-predictions	Limit to the number of predictions in output.text_candidates Default: 100.
--ignore-bad-candidates	Ignore examples for which the label is not present in the label candidates. Default behavior results in RuntimeError. Default: False.
--rank-top-k	Ranking returns the top k results of k > 0, otherwise sorts every single candidate according to the ranking. Default: -1.
--inference	Final response output algorithm Choices: max, topk. Default: max.
--topk	K used in Top K sampling inference, when selected Default: 5.
--return-cand-scores	Return sorted candidate scores from eval_step Default: False.

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.