def _test_ensemble_encoder_export(self, test_args):
samples, src_dict, tgt_dict = test_utils.prepare_inputs(test_args)
num_models = 3
model_list = []
for _ in range(num_models):
model_list.append(models.build_model(test_args, src_dict, tgt_dict))
encoder_ensemble = EncoderEnsemble(model_list)
tmp_dir = tempfile.mkdtemp()
encoder_pb_path = os.path.join(tmp_dir, 'encoder.pb')
encoder_ensemble.onnx_export(encoder_pb_path)
# test equivalence
# The discrepancy in types here is a temporary expedient.
# PyTorch indexing requires int64 while support for tracing
# pack_padded_sequence() requires int32.
sample = next(samples)
src_tokens = sample['net_input']['src_tokens'][0:1].t()
src_lengths = sample['net_input']['src_lengths'][0:1].int()
pytorch_encoder_outputs = encoder_ensemble(src_tokens, src_lengths)
with open(encoder_pb_path, 'r+b') as f:
onnx_model = onnx.load(f)
onnx_encoder = caffe2_backend.prepare(onnx_model)
caffe2_encoder_outputs = onnx_encoder.run(
(
src_tokens.numpy(),
src_lengths.numpy(),
),
)
for i in range(len(pytorch_encoder_outputs)):
caffe2_out_value = caffe2_encoder_outputs[i]
pytorch_out_value = pytorch_encoder_outputs[i].data.numpy()
np.testing.assert_allclose(
caffe2_out_value,
pytorch_out_value,
rtol=1e-4,
atol=1e-6,
)
encoder_ensemble.save_to_db(
os.path.join(tmp_dir, 'encoder.predictor_export'),
)
def _test_ensemble_encoder_export(self, test_args):
samples, src_dict, tgt_dict = test_utils.prepare_inputs(test_args)
task = tasks.DictionaryHolderTask(src_dict, tgt_dict)
num_models = 3
model_list = []
for _ in range(num_models):
model_list.append(task.build_model(test_args))
encoder_ensemble = EncoderEnsemble(model_list)
tmp_dir = tempfile.mkdtemp()
encoder_pb_path = os.path.join(tmp_dir, "encoder.pb")
encoder_ensemble.onnx_export(encoder_pb_path)
# test equivalence
# The discrepancy in types here is a temporary expedient.
# PyTorch indexing requires int64 while support for tracing
# pack_padded_sequence() requires int32.
sample = next(samples)
src_tokens = sample["net_input"]["src_tokens"][0:1].t()
src_lengths = sample["net_input"]["src_lengths"][0:1].int()
pytorch_encoder_outputs = encoder_ensemble(src_tokens, src_lengths)
onnx_encoder = caffe2_backend.prepare_zip_archive(encoder_pb_path)
caffe2_encoder_outputs = onnx_encoder.run(
(src_tokens.numpy(), src_lengths.numpy()))
for i in range(len(pytorch_encoder_outputs)):
caffe2_out_value = caffe2_encoder_outputs[i]
pytorch_out_value = pytorch_encoder_outputs[i].detach().numpy()
np.testing.assert_allclose(caffe2_out_value,
pytorch_out_value,
rtol=1e-4,
atol=1e-6)
encoder_ensemble.save_to_db(
os.path.join(tmp_dir, "encoder.predictor_export"))
