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[fbsync] Enable model testing in FBCode #1720

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May 12, 2022
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[fbsync] Enable model testing in FBCode #1720

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80 changes: 55 additions & 25 deletions test/integration_tests/test_models.py
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Original file line number Diff line number Diff line change
@@ -1,39 +1,25 @@
import torch
from torchtext.models import ROBERTA_BASE_ENCODER, ROBERTA_LARGE_ENCODER, XLMR_BASE_ENCODER, XLMR_LARGE_ENCODER
from parameterized import parameterized
from torchtext.models import (
ROBERTA_BASE_ENCODER,
ROBERTA_LARGE_ENCODER,
XLMR_BASE_ENCODER,
XLMR_LARGE_ENCODER,
)

from ..common.assets import get_asset_path
from ..common.parameterized_utils import nested_params
from ..common.torchtext_test_case import TorchtextTestCase


class TestModels(TorchtextTestCase):
@nested_params(
[
("xlmr.base.output.pt", "XLMR base Model Comparison", XLMR_BASE_ENCODER),
("xlmr.large.output.pt", "XLMR base Model Comparison", XLMR_LARGE_ENCODER),
(
"roberta.base.output.pt",
"Roberta base Model Comparison",
ROBERTA_BASE_ENCODER,
),
(
"roberta.large.output.pt",
"Roberta base Model Comparison",
ROBERTA_LARGE_ENCODER,
),
],
[True, False],
)
def test_model(self, model_args, is_jit):
class TestRobertaEncoders(TorchtextTestCase):
def _roberta_encoders(self, is_jit, encoder, expected_asset_name, test_text):
"""Verify pre-trained XLM-R and Roberta models in torchtext produce
the same output as the reference implementation within fairseq
"""
expected_asset_name, test_text, model_bundler = model_args

expected_asset_path = get_asset_path(expected_asset_name)

transform = model_bundler.transform()
model = model_bundler.get_model()
transform = encoder.transform()
model = encoder.get_model()
model = model.eval()

if is_jit:
Expand All @@ -44,3 +30,47 @@ def test_model(self, model_args, is_jit):
actual = model(model_input)
expected = torch.load(expected_asset_path)
torch.testing.assert_close(actual, expected)

@parameterized.expand([("jit", True), ("not_jit", False)])
def test_xlmr_base_model(self, name, is_jit):
expected_asset_name = "xlmr.base.output.pt"
test_text = "XLMR base Model Comparison"
self._roberta_encoders(
is_jit=is_jit,
encoder=XLMR_BASE_ENCODER,
expected_asset_name=expected_asset_name,
test_text=test_text,
)

@parameterized.expand([("jit", True), ("not_jit", False)])
def test_xlmr_large_model(self, name, is_jit):
expected_asset_name = "xlmr.large.output.pt"
test_text = "XLMR base Model Comparison"
self._roberta_encoders(
is_jit=is_jit,
encoder=XLMR_LARGE_ENCODER,
expected_asset_name=expected_asset_name,
test_text=test_text,
)

@parameterized.expand([("jit", True), ("not_jit", False)])
def test_roberta_base_model(self, name, is_jit):
expected_asset_name = "roberta.base.output.pt"
test_text = "Roberta base Model Comparison"
self._roberta_encoders(
is_jit=is_jit,
encoder=ROBERTA_BASE_ENCODER,
expected_asset_name=expected_asset_name,
test_text=test_text,
)

@parameterized.expand([("jit", True), ("not_jit", False)])
def test_robeta_large_model(self, name, is_jit):
expected_asset_name = "roberta.large.output.pt"
test_text = "Roberta base Model Comparison"
self._roberta_encoders(
is_jit=is_jit,
encoder=ROBERTA_LARGE_ENCODER,
expected_asset_name=expected_asset_name,
test_text=test_text,
)