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Shard eval dataset and aggregate eval metrics #10

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Merged
merged 3 commits into from
Apr 2, 2020
Merged

Shard eval dataset and aggregate eval metrics #10

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0a0d487
Shard eval dataset and aggregate eval metrics
jysohn23 Apr 1, 2020
9db8f3b
Change defaultdict to float
jysohn23 Apr 1, 2020
3a7ed9e
Reduce the pred, label tensors instead of metrics
jysohn23 Apr 2, 2020
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35 changes: 23 additions & 12 deletions examples/run_glue_tpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,13 +18,14 @@
from __future__ import absolute_import, division, print_function

import argparse
from collections import defaultdict
import glob
import logging
import math
import multiprocessing
import os
import time
import random
import time

import numpy as np
import torch
Expand Down Expand Up @@ -205,18 +206,18 @@ def evaluate(args, model, tokenizer, prefix="", disable_logging=False):

# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
output_dir = '{}/eval-xla{}'.format(args.output_dir, xm.get_ordinal())
eval_outputs_dirs = (output_dir, output_dir + '-MM') if args.task_name == "mnli" else (output_dir,)
eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)

results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
eval_sampler = get_sampler(eval_dataset)

if not os.path.exists(eval_output_dir):
os.makedirs(eval_output_dir)

# Note that we don't shard for TPU Multiprocess as we don't reduce loss among client processes.
dataloader = DataLoader(eval_dataset, batch_size=args.eval_batch_size, shuffle=False)
dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, shuffle=False)
eval_dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

# Eval!
Expand All @@ -238,9 +239,9 @@ def evaluate(args, model, tokenizer, prefix="", disable_logging=False):
# XLM, DistilBERT and RoBERTa don't use segment_ids
inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
batch_eval_loss, logits = outputs[:2]

eval_loss += tmp_eval_loss.mean().item()
eval_loss += batch_eval_loss
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
Expand All @@ -249,21 +250,31 @@ def evaluate(args, model, tokenizer, prefix="", disable_logging=False):
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)

# Get all predictions and labels from all workers
preds = xm.mesh_reduce('eval_preds', preds, np.concatenate)
out_label_ids = xm.mesh_reduce(
'eval_out_label_ids', out_label_ids, np.concatenate)

eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif args.output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
results['eval_loss'] = eval_loss.item()

output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
tb_writer.add_scalar(key, result[key])
if xm.is_master_ordinal():
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is everything being logged here already on cpu?

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Yes.

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maybe let's add a comment? It's a subtle point that can be missed by code readers.

with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
writer.write("%s = %s\n" % (key, str(results[key])))
tb_writer.add_scalar(key, results[key])

if args.metrics_debug:
xm.master_print(met.metrics_report())

tb_writer.close()
return results
Expand Down