Author: Stephen Roller

Introduction and Standard Metrics¶

Tip

If you’re not sure what a metric means, refer to our List of metrics.

ParlAI contains a number of built-in metrics that are automatically computed when we train and evaluate models. Some of these metrics are text generation metrics, which happen any time we generate a text: this includes F1, BLEU and Accuracy.

For example, let’s try a Fixed Response model, which always returns a given fixed response, and evaluate on the DailyDialog dataset:

$parlai eval_model --model fixed_response --task dailydialog --fixed-response "how may i help you ?" ... after a while ... 14:41:40 | Evaluating task dailydialog using datatype valid. 14:41:40 | creating task(s): dailydialog 14:41:41 | Finished evaluating tasks ['dailydialog'] using datatype valid accuracy bleu-4 exs f1 .0001239 .002617 8069 .1163  We see that we got 0.01239% accuracy, 0.26% BLEU-4 score, and 11.63% F1 across 8069 examples. What do those metrics means? • Accuracy: this is perfect, exact, matching of the response, averaged across all examples in the dataset • BLEU-4: this is the BLEU score between the predicted response and the reference response. It is measured on tokenized text, and uses NLTK to compute it. • F1: This is the Unigram F1 overlap between your text and the reference response. • exs: the number of examples we have evaluated If you don’t see the BLEU-4 score, you may need to install NLTK with pip install nltk. We can also measure ROUGE. Note that we need to pip install py-rouge for this functionality: $ parlai eval_model --model fixed_response --task dailydialog --fixed-response "how may i help you ?" --metrics rouge
14:47:31 | Finished evaluating tasks ['dailydialog'] using datatype valid
accuracy  exs    f1  rouge_1  rouge_2  rouge_L
.0001239 8069 .1163   .09887  .007285   .09525


One nice thing about metrics is that they are automatically logged to the .trainstats file, and within Tensorboard (when enabled with --tensorboard-log true. As such, metrics are more reliable than adding print statements into your code.

Agent-specific metrics¶

Some agents include their own metrics that are computed for them. For example, generative models automatically compute ppl (perplexity) and token_acc, both which measure the generative model’s ability to predict individual tokens. As an example, let’s evaluate the BlenderBot 90M model on DailyDialog:

\$ parlai eval_model --task dailydialog --model-file zoo:blender/blender_90M/model --batchsize 32
...
14:54:14 | Evaluating task dailydialog using datatype valid.
...
15:26:19 | Finished evaluating tasks ['dailydialog'] using datatype valid
accuracy  bleu-4  ctpb  ctps  exps  exs    f1  gpu_mem  loss      lr  ltpb  ltps   ppl  token_acc  total_train_updates   tpb   tps
0 .002097 14202 442.5 6.446 8069 .1345    .0384 2.979 7.5e-06  3242   101 19.67      .4133               339012 17445 543.5


Here we see a number of extra metrics, each of which we explain below. They may be roughly divided into diagnostic/performance metrics, and modeling metrics. The modeling metrics are:

• ppl and token_acc: the perplexity and per-token accuracy. these are generative performance metrics.

The diagnostic metrics are:

• tpb, ctpb, ltpb: stand for tokens per batch, context-tokens per batch, and label-tokens per batch. These are useful for measuring how dense the batches are, and are helpful when experimenting with dynamic batching. tpb is always the sum of ctpb and lptb.

• tps, ctps, ltps: are similar, but stand for “tokens per second”. They measure how fast we are training. Similarly, exps measures examples per second.

• gpu_mem: measures roughly how much GPU memory your model is using, but it is only approximate. This is useful for determining if you can possibly increase the model size or the batch size.

• loss: the loss metric

• total_train_updates: the number of SGD updates this model was trained for. You will see this increase during training, but not during evaluation.

Of course, you may wish to add your own custom metrics: whether this is because you are developing a special model, special dataset, or otherwise want other information accessible to you. Metrics can be computed by either the teacher OR the model. Within the model, they may be computed either locally or globally. There are different reasons for why and where you would want to choose each location:

• Teacher metrics: This is the best spot for computing metrics that depend on a specific dataset. These metrics will only be available when evaluating on this dataset. They have the advantage of being easy to compute and understand. An example of a modeling metric is slot_p, which is part of some of our Task Oriented Datasets, such as google_sgd

• Global metrics (model metric): Global metrics are computed by the model, and are globally tracked. These metrics are easy to understand and track, but work poorly when doing multitasking. One example of a global metric includes gpu_mem, which depends on a system-wide memory usage, and cannot be tied to a specific task.

• Local metrics (model metric): Local metrics are the model-analogue of teacher metrics. They are computed and recorded on a per-example basis, and so they work well when multitasking. They can be extremely complicated for some models, however. An example of a local metric includes perplexity, which should be computed on a per-example basis, but must be computed by the model, and therefore cannot be a teacher metric.

We will take you through writing each of these methods in turn, and demonstrate examples of how to add these metrics in your setup.

Teacher metrics¶

Teacher metrics are useful for items that depend on a specific dataset. For example, in some of our task oriented datasets, like google_sgd, we want to additionally compute metrics around slots.

    def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
) -> None:
pass


The signature for this method is as follows:

• teacher_action: this is the last message the teacher sent to the model. This likely contains a “text” and “labels” field, as well as any custom fields you might have.

• labels: The gold label(s). This can also be found as information in the teacher_action, but it is conveniently extracted for you.

• model_response: The full model response, including any extra fields the model may have sent.

Let’s take an actual example. We will add a custom metric which calculates how often the model says the word “hello”, and call it hello_avg.

We will add a custom teacher. For this example, we will use the @register syntax you may have seen in our quickstart tutorial.

from parlai.core.loader import register_teacher
from parlai.core.metrics import AverageMetric
from parlai.tasks.dailydialog.agents import DefaultTeacher as DailyDialogTeacher

@register_teacher("hello_daily")
class CustomDailyDialogTeacher(DailyDialogTeacher):
def custom_evaluation(
self, teacher_action, labels, model_response
) -> None:
if 'text' not in model_response:
# model didn't speak, skip this example
return
model_text = model_response['text']
if 'hello' in model_text:
# count 1 / 1 messages having "hello"
else:
# count 0 / 1 messages having "hello"

if __name__ == '__main__':
from parlai.scripts.eval_model import EvalModel

EvalModel.main(
model_file='zoo:blender/blender_90M/model',
batchsize=32,
)


If we run the script, we will have a new metric in our output:

18:07:30 | Finished evaluating tasks ['hello_daily'] using datatype valid
accuracy  bleu-4  ctpb  ctps  exps  exs    f1  gpu_mem  hello_avg  loss  ltpb  ltps   ppl  token_acc  tpb   tps
0 .002035  2172   230 3.351 8069 .1346   .05211      .1228 2.979 495.9 52.52 19.67      .4133 2668 282.6


What is AverageMetric?

Wait, what is this AverageMetric? All metrics you want to create in ParlAI should be a Metric object. Metric objects define a way of instantiating the metric, a way of combining it with a like-metric, and a way of rendering it as a single float value. For an AverageMetric, this means we need to define a numerator and a denominator; the combination of AverageMetrics adds their numerators and denominators separately. As we do this across all examples, the numerator will be the number of examples with “hello” in it, and the denominator will be the total number of examples. When we go to print the metric, the division will be computed at the last second.

If you’re used to writing machine learning code in one-off scripts, you may ask why do I need to use this metric? Can’t I just count and divide myself? While you can do this, your code could not be run in distributed mode. If we only returned a single float, we would not be able to know if some distributed workers received more or fewer examples than others. However, when we explicitly store the numerator and denominator, we can combine and reduce the across multiple nodes, enabling us to train on hundreds of GPUs, while still ensuring correctness in all our metrics.

In addition to AverageMetric, there is also SumMetric, which keeps a running sum. SumMetric and AverageMetric are the most common ways to construct custom metrics, but others exist as well. For a full list (and views into advanced cases), please see the metrics API documentation.

Agent (model) level metrics¶

In the above example, we worked on a metric defined by a Teacher. However, sometimes our models will have special metrics that only they want to compute, which we call an Agent-level metric. Perplexity is one example.

To compute model-level metrics, we can define either a Global metric, or a Local metric. Global metrics can be computed anywhere, and are easy to use, but cannot distinguish between different teachers when multitasking. We’ll look at another example, counting the number of times the teacher says “hello”.

Global metrics¶

A global metric is computed anywhere in the model, and has an interface similar to that of the teacher:

agent.global_metrics.add('my_metric', AverageMetric(1, 2))


Global metrics are called as such because they can be called anywhere in agent code. For example, we can add a metric that counts the number of times the model sees the word “hello” in observe. We’ll do this while extending the TransformerGeneratorAgent, so that we can combined it with the BlenderBot model we used earlier.

from parlai.core.metrics import AverageMetric
from parlai.agents.transformer.transformer import TransformerGeneratorAgent

@register_agent('GlobalHelloCounter')
class GlobalHelloCounterAgent(TransformerGeneratorAgent):
def observe(self, observation):
retval = super().observe(observation)
if 'text' in observation:
text = observation['text']
'global_hello', AverageMetric(int('hello' in text), 1)
)
return retval

if __name__ == '__main__':
from parlai.scripts.eval_model import EvalModel

EvalModel.main(
model='GlobalHelloCounter',
model_file='zoo:blender/blender_90M/model',
batchsize=32,
)


Note that this is very different than the Teacher metric we implemented in the first half of the tutorial. In the teacher metric, we were counting the number of times the model said hello. Here, we are counting the number of times the teacher said hello.

How to determine where to implement your custom metric:

• If you want your metric to be model-agnostic, then it should be implemented in the Teacher.

• If you want your metric to be dataset-agnostic, then it should be implemented in the Model agent.

• If you need your metric to be both model and dataset agnostic, then you should do it within the Model, using a mixin or abstract class.

Running the script, we see that our new metric appears. As discussed above, the value differs slightly because of the difference in semantics.

21:57:50 | Finished evaluating tasks ['dailydialog'] using datatype valid
accuracy  bleu-4  ctpb  ctps  exps  exs    f1  global_hello  gpu_mem  loss  ltpb  ltps   ppl  token_acc   tpb   tps
0 .002097 14202 435.1 6.338 8069 .1345      .0009914   .02795 2.979  3242 99.32 19.67      .4133 17445 534.4


The global metric works well, but have some drawbacks: if we were to start training on a multitask datasets, we would not be able to distinguish the global_hello of the two datasets, and we could only compute the micro-average of the combination of the two. Below is an excerpt from a training log with the above agents:

09:14:52 | time:112s total_exs:90180 epochs:0.41
clip  ctpb  ctps  exps  exs  global_hello  gnorm  gpu_mem  loss  lr  ltpb  ltps   ppl  token_acc  total_train_updates   tpb   tps   ups
all             1  9831 66874 841.9 8416        .01081  2.018    .3474 5.078   1  1746 11878 163.9      .2370                  729 11577 78752 6.803
convai2                             3434        .01081                 5.288                 197.9      .2120
dailydialog                         4982        .01081                 4.868                   130      .2620


Notice how global_hello is the same in both, because the model is unable to distinguish between the two settings. In the next section we’ll show how to fix this with local metrics.

On placement: In the example above, we recorded the global metric inside the observe function. However, global metrics can be recorded from anywhere.

Local metrics¶

Having observed the limitation of global metrics being unable to distinguish settings in multitasking, we would like to improve upon this. Let’s add a local metric, which is recorded per example. By recording this metric per example, we can unambiguously identify which metrics came from which dataset, and report averages correctly.

Local metrics have a limitation: they can only be computed inside the scope of batch_act. This includes common places like compute_loss or generate, where we often want to instrument specific behavior.

Let’s look at an example. We’ll add a metric inside the batchify function, which is called from within batch_act, and is used to convert from a list of Messages objects to a Batch object. It is where we do things like padding, etc. We’ll do something slightly different than our previous runs. In this case, we’ll count the number of tokens which are the word “hello”.

from parlai.core.metrics import AverageMetric
from parlai.agents.transformer.transformer import TransformerGeneratorAgent

@register_agent('LocalHelloCounter')
class LocalHelloCounterAgent(TransformerGeneratorAgent):
def batchify(self, observations):
batch = super().batchify(observations)
if hasattr(batch, 'text_vec'):
num_hello = ["hello" in o['text'] for o in observations]
self.record_local_metric(
'local_hello',
AverageMetric.many(num_hello),
)
return batch

if __name__ == '__main__':
from parlai.scripts.train_model import TrainModel

TrainModel.main(
model='LocalHelloCounter',
dict_file='zoo:blender/blender_90M/model.dict',
batchsize=32,
)


When we run this training script, we get one such output:

09:49:00 | time:101s total_exs:56160 epochs:0.26
clip  ctpb  ctps  exps  exs  gnorm  gpu_mem  local_hello  loss  lr  ltpb  ltps   ppl  token_acc  total_train_updates  tpb   tps  ups
all             1  3676 63204 550.2 5504  2.146    .1512       .01423 4.623   1 436.2  7500 101.8      .2757                 1755 4112 70704 17.2
convai2                             3652                       .02793 4.659                 105.5      .2651
dailydialog                         1852                       .00054 4.587                 98.17      .2863


Notice how the local_hello metric can now distinguish between hellos coming from convai2 and those coming from daily dialog? The average hides the fact that one dataset has many hellos, and the other does not.

Local metrics are primarily worth the implementation when you care about the fidelity of train time metrics. During evaluation time, we evaluate each dataset individually, so we can ensure global metrics are not mixed up.

Under the hood: Local metrics work by including a “metrics” field in the return message. This is a dictionary which maps field name to a metric value. When the teacher receives the response from the model, it utilizes the metrics field to update counters on its side.

List of Metrics¶

Below is a list of metrics and a brief explanation of each.

Note

If you find a metric not listed here, please file an issue on GitHub.

Metric

Explanation

accuracy

Exact match text accuracy

auc

Area Under the Receiver Operating Characteristic Curve (true positive rate vs false positive rate curve)

bleu-4

BLEU-4 of the generation, under a standardized (model-independent) tokenizer

clen

Average length of context in number of tokens

clip

Fraction of batches with clipped gradients

ctpb

Context tokens per batch

ctps

Context tokens per second

ctrunc

Fraction of samples with some context truncation

ctrunclen

Average length of context tokens truncated

exps

Examples per second

exs

Number of examples processed since last print

f1

Unigram F1 overlap, under a standardized (model-independent) tokenizer

gen_n_toks

Average length of generated outputs in number of tokens

gnorm

gpu_mem

Fraction of GPU memory used. May slightly underestimate true value.

hits@1

Fraction of correct choices in 1 guess. (Similar to recall@K)

hits@5

Fraction of correct choices in 5 guesses. (Similar to recall@K)

interdistinct-1

Fraction of n-grams unique across all generations

interdistinct-2

Fraction of n-grams unique across all generations

intradictinct-2

Fraction of n-grams unique within each utterance

intradistinct-1

Fraction of n-grams unique within each utterance

jga

Joint Goal Accuracy

llen

Average length of label in number of tokens

loss

Loss

lr

The most recent learning rate applied

ltpb

Label tokens per batch

ltps

Label tokens per second

ltrunc

Fraction of samples with some label truncation

ltrunclen

Average length of label tokens truncated

rouge-1

ROUGE metrics

rouge-2

ROUGE metrics

rouge-L

ROUGE metrics

token_acc

Token-wise accuracy (generative only)

token_em

Utterance-level token accuracy. Roughly corresponds to perfection under greedy search (generative only)

total_train_updates

Number of SGD steps taken across all batches

tpb

Total tokens (context + label) per batch

tps

Total tokens (context + label) per second

ups