Improve R2Score metric for DDP

ignite/contrib/metrics/regression/r2_score.py

@@ -1,7 +1,10 @@
+from typing import Callable, Union
+
 import torch
 
 from ignite.contrib.metrics.regression._base import _BaseRegression
 from ignite.exceptions import NotComputableError
+from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
 
 
 class R2Score(_BaseRegression):
@@ -18,21 +21,34 @@ class R2Score(_BaseRegression):
         - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.
     """
 
+    def __init__(
+        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
+    ):
+        self._num_examples = None
+        self._sum_of_errors = None
+        self._y_sq_sum = None
+        self._y_sum = None
+        super(R2Score, self).__init__(output_transform, device)
+
+    @reinit__is_reduced
     def reset(self):
-        self._num_examples = 0
-        self._sum_of_errors = 0
-        self._y_sq_sum = 0
-        self._y_sum = 0
+        self._num_examples = torch.tensor(0, device=self._device)
+        self._sum_of_errors = torch.tensor(0.0, device=self._device)
+        self._y_sq_sum = torch.tensor(0.0, device=self._device)
+        self._y_sum = torch.tensor(0.0, device=self._device)
 
     def _update(self, output):
         y_pred, y = output
         self._num_examples += y.shape[0]
-        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).item()
+        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).to(self._device)
 
-        self._y_sum += torch.sum(y).item()
-        self._y_sq_sum += torch.sum(torch.pow(y, 2)).item()
+        self._y_sum += torch.sum(y).to(self._device)
+        self._y_sq_sum += torch.sum(torch.pow(y, 2)).to(self._device)
 
+    @sync_all_reduce("_num_examples", "_sum_of_errors", "_y_sq_sum", "_y_sum")
     def compute(self):
-        if self._num_examples == 0:
+        if self._num_examples.item() == 0:
             raise NotComputableError("R2Score must have at least one example before it can be computed.")
-        return 1 - self._sum_of_errors / (self._y_sq_sum - (self._y_sum ** 2) / self._num_examples)
+        return 1 - self._sum_of_errors.item() / (
+            self._y_sq_sum.item() - (self._y_sum.item() ** 2) / self._num_examples.item()
+        )

tests/ignite/contrib/metrics/regression/test_r2_score.py

@@ -1,8 +1,11 @@
+import os
+
 import numpy as np
 import pytest
 import torch
 from sklearn.metrics import r2_score
 
+import ignite.distributed as idist
 from ignite.contrib.metrics.regression import R2Score
 from ignite.engine import Engine
 
@@ -86,3 +89,95 @@ def update_fn(engine, batch):
     r_squared = engine.run(data, max_epochs=1).metrics["r2_score"]
 
     assert r2_score(np_y, np_y_pred) == pytest.approx(r_squared)
+
+
+def _test_distrib_compute(device):
+    rank = idist.get_rank()
+
+    def _test(metric_device):
+        metric_device = torch.device(metric_device)
+        m = R2Score(device=metric_device)
+        torch.manual_seed(10 + rank)
+
+        y_pred = torch.randint(0, 10, size=(10,), device=device).float()
+        y = torch.randint(0, 10, size=(10,), device=device).float()
+
+        m.update((y_pred, y))
+
+        # gather y_pred, y
+        y_pred = idist.all_gather(y_pred)
+        y = idist.all_gather(y)
+
+        np_y_pred = y_pred.cpu().numpy()
+        np_y = y.cpu().numpy()
+        res = m.compute()
+        assert r2_score(np_y, np_y_pred) == pytest.approx(res)
+
+    for _ in range(3):
+        _test("cpu")
+        if device.type != "xla":
+            _test(idist.device())
+
+
+@pytest.mark.distributed
+@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
+@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")
+def test_distrib_gpu(distributed_context_single_node_nccl):
+    device = torch.device("cuda:{}".format(distributed_context_single_node_nccl["local_rank"]))
+    _test_distrib_compute(device)
+
+
+@pytest.mark.distributed
+@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
+def test_distrib_cpu(distributed_context_single_node_gloo):
+
+    device = torch.device("cpu")
+    _test_distrib_compute(device)
+
+
+@pytest.mark.distributed
+@pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support")
+@pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc")
+def test_distrib_hvd(gloo_hvd_executor):
+
+    device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
+    nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count()
+
+    gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True)
+
+
+@pytest.mark.multinode_distributed
+@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
+@pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
+def test_multinode_distrib_cpu(distributed_context_multi_node_gloo):
+    device = torch.device("cpu")
+    _test_distrib_compute(device)
+
+
+@pytest.mark.multinode_distributed
+@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
+@pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
+def test_multinode_distrib_gpu(distributed_context_multi_node_nccl):
+    device = torch.device("cuda:{}".format(distributed_context_multi_node_nccl["local_rank"]))
+    _test_distrib_compute(device)
+
+
+@pytest.mark.tpu
+@pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars")
+@pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package")
+def test_distrib_single_device_xla():
+    device = idist.device()
+    _test_distrib_compute(device)
+
+
+def _test_distrib_xla_nprocs(index):
+    device = idist.device()
+    _test_distrib_compute(device)
+
+
+@pytest.mark.tpu
+@pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars")
+@pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package")
+def test_distrib_xla_nprocs(xmp_executor):
+    n = int(os.environ["NUM_TPU_WORKERS"])
+    xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n)