def test_metric_result_respects_dtype(floating_dtype):
torch.set_default_dtype(floating_dtype)
fixed_dtype = torch.long # default by PyTorch
metadata = _Metadata("foo", "bar")
metadata.sync = _Sync()
rm = _ResultMetric(metadata, is_tensor=True)
assert rm.value.dtype == floating_dtype
assert rm.cumulated_batch_size.dtype == fixed_dtype
# two fixed point numbers - should be converted
value, batch_size = torch.tensor(2), 3
assert value.dtype == fixed_dtype
with pytest.warns(
UserWarning,
match=
rf"`self.log\('bar', ...\)` in your `foo` .* Converting it to {floating_dtype}"
):
rm.update(value, batch_size)
# floating and fixed
rm.update(torch.tensor(4.0), 5)
total = rm.compute()
assert total == (2 * 3 + 4 * 5) / (5 + 3)
assert total.dtype == floating_dtype
# restore to avoid impacting other tests
torch.set_default_dtype(torch.float)
def _ddp_test_fn(rank, worldsize):
_setup_ddp(rank, worldsize)
tensor = torch.tensor([1.0])
sync = _Sync(sync_ddp_if_available, _should=True, op="SUM")
actual = sync(tensor)
assert actual.item() == dist.get_world_size(
), "Result-Log does not work properly with DDP and Tensors"
def test_sync_dist(_):
sync = _Sync(TPUSpawnPlugin().reduce,
should=True,
op=torch.distributed.ReduceOp.SUM)
value = torch.tensor([1.0])
value = (sync(value), )
assert value.item() == 8
def test_metric_result_computed_check():
"""Unittest ``_get_cache`` with multielement tensors."""
metadata = _Metadata("foo", "bar", on_epoch=True, enable_graph=True)
metadata.sync = _Sync()
rm = _ResultMetric(metadata, is_tensor=True)
computed_value = torch.tensor([1, 2, 3])
rm._computed = computed_value
cache = _ResultCollection._get_cache(rm, on_step=False)
# `enable_graph=True` so no detach, identity works
assert cache is computed_value
def test_metric_result_dtype_promotion(reduce_fx):
metadata = _Metadata("foo", "bar", reduce_fx=reduce_fx)
metadata.sync = _Sync()
rm = _ResultMetric(metadata, is_tensor=True)
assert rm.value.dtype == torch.float
# log a double
rm.update(torch.tensor(0, dtype=torch.double), 1)
# `rm.value.dtype` is promoted
assert rm.value.dtype == torch.double
# log a float
rm.update(torch.tensor(0, dtype=torch.float), 1)
# the previous dtype stays
assert rm.value.dtype == torch.double
total = rm.compute()
assert total.dtype == torch.double
def test_logger_sync_dist(distributed_env):
# self.log('bar', 7, ..., sync_dist=False)
meta = _Metadata("foo", "bar")
meta.sync = _Sync(_should=False)
result_metric = _ResultMetric(metadata=meta, is_tensor=True)
result_metric.update(torch.tensor(7.0), 10)
warning_ctx = pytest.warns if distributed_env else no_warning_call
with mock.patch(
"pytorch_lightning.trainer.connectors.logger_connector.result.distributed_available",
return_value=distributed_env,
):
with warning_ctx(
PossibleUserWarning,
match=
r"recommended to use `self.log\('bar', ..., sync_dist=True\)`"
):
value = _ResultCollection._get_cache(result_metric, on_step=False)
assert value == 7.0
def test_result_metric_max_min(reduce_fx, expected):
metadata = _Metadata("foo", "bar", reduce_fx=reduce_fx)
metadata.sync = _Sync()
rm = _ResultMetric(metadata, is_tensor=True)
rm.update(torch.tensor(expected), 1)
assert rm.compute() == expected
