Rewrite test for Entropy metric

tests/ignite/metrics/test_entropy.py

@@ -1,12 +1,12 @@
-import os
-
 import numpy as np
 import pytest
 import torch
 from scipy.special import softmax
 from scipy.stats import entropy as scipy_entropy
 
 import ignite.distributed as idist
+
+from ignite.engine import Engine
 from ignite.exceptions import NotComputableError
 from ignite.metrics import Entropy
 
@@ -65,68 +65,6 @@ def test_compute(n_times, test_case):
     assert pytest.approx(ent.compute()) == np_res
 
 
-def _test_distrib_integration(device, tol=1e-6):
-    from ignite.engine import Engine
-
-    rank = idist.get_rank()
-    torch.manual_seed(12 + rank)
-
-    def _test(metric_device):
-        n_iters = 100
-        batch_size = 10
-        n_cls = 50
-
-        y_true = torch.randint(0, n_cls, size=[n_iters * batch_size], dtype=torch.long).to(device)
-        y_preds = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_cls), dtype=torch.float).to(device)
-
-        def update(engine, i):
-            return (
-                y_preds[i * batch_size : (i + 1) * batch_size],
-                y_true[i * batch_size : (i + 1) * batch_size],
-            )
-
-        engine = Engine(update)
-
-        m = Entropy(device=metric_device)
-        m.attach(engine, "entropy")
-
-        data = list(range(n_iters))
-        engine.run(data=data, max_epochs=1)
-
-        y_preds = idist.all_gather(y_preds)
-        y_true = idist.all_gather(y_true)
-
-        assert "entropy" in engine.state.metrics
-        res = engine.state.metrics["entropy"]
-
-        true_res = np_entropy(y_preds.cpu().numpy())
-
-        assert pytest.approx(res, rel=tol) == true_res
-
-    _test("cpu")
-    if device.type != "xla":
-        _test(idist.device())
-
-
-def _test_distrib_accumulator_device(device):
-    metric_devices = [torch.device("cpu")]
-    if device.type != "xla":
-        metric_devices.append(idist.device())
-    for metric_device in metric_devices:
-        device = torch.device(device)
-        ent = Entropy(device=metric_device)
-
-        for dev in [ent._device, ent._sum_of_entropies.device]:
-            assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"
-
-        y_pred = torch.tensor([[2.0], [-2.0]])
-        y = torch.zeros(2)
-        ent.update((y_pred, y))
-
-        for dev in [ent._device, ent._sum_of_entropies.device]:
-            assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"
-
-
 def test_accumulator_detached():
     ent = Entropy()
 
@@ -137,70 +75,66 @@ def test_accumulator_detached():
     assert not ent._sum_of_entropies.requires_grad
 
 
-@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_nccl_gpu(distributed_context_single_node_nccl):
-    device = idist.device()
-    _test_distrib_integration(device)
-    _test_distrib_accumulator_device(device)
+@pytest.mark.usefixtures("distributed")
+class TestDistributed:
+    def test_integration(self):
+        tol = 1e-6
+        device = idist.device()
+        rank = idist.get_rank()
+        torch.manual_seed(12 + rank)
+
+        n_iters = 100
+        batch_size = 10
+        n_cls = 50
 
+        metric_devices = [torch.device("cpu")]
+        if device.type != "xla":
+            metric_devices.append(idist.device())
 
-@pytest.mark.distributed
-@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
-def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo):
-    device = idist.device()
-    _test_distrib_integration(device)
-    _test_distrib_accumulator_device(device)
+        for metric_device in metric_devices:
+            y_true = torch.randint(0, n_cls, size=[n_iters * batch_size], dtype=torch.long).to(device)
+            y_preds = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_cls), dtype=torch.float).to(device)
 
+            def update(engine, i):
+                return (
+                    y_preds[i * batch_size : (i + 1) * batch_size],
+                    y_true[i * batch_size : (i + 1) * batch_size],
+                )
 
-@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()
+            engine = Engine(update)
 
-    gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True)
-    gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True)
+            m = Entropy(device=metric_device)
+            m.attach(engine, "entropy")
 
+            data = list(range(n_iters))
+            engine.run(data=data, max_epochs=1)
 
-@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_gloo_cpu_or_gpu(distributed_context_multi_node_gloo):
-    device = idist.device()
-    _test_distrib_integration(device)
-    _test_distrib_accumulator_device(device)
+            y_preds = idist.all_gather(y_preds)
+            y_true = idist.all_gather(y_true)
 
+            assert "entropy" in engine.state.metrics
+            res = engine.state.metrics["entropy"]
 
-@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_nccl_gpu(distributed_context_multi_node_nccl):
-    device = idist.device()
-    _test_distrib_integration(device)
-    _test_distrib_accumulator_device(device)
+            true_res = np_entropy(y_preds.cpu().numpy())
 
+            assert pytest.approx(res, rel=tol) == true_res
 
-@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_integration(device, tol=1e-4)
-    _test_distrib_accumulator_device(device)
+    def test_accumulator_device(self):
+        device = idist.device()
+        metric_devices = [torch.device("cpu")]
+        if device.type != "xla":
+            metric_devices.append(idist.device())
 
+        for metric_device in metric_devices:
+            device = torch.device(device)
+            ent = Entropy(device=metric_device)
 
-def _test_distrib_xla_nprocs(index):
-    device = idist.device()
-    _test_distrib_integration(device, tol=1e-4)
-    _test_distrib_accumulator_device(device)
+            for dev in [ent._device, ent._sum_of_entropies.device]:
+                assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"
 
+            y_pred = torch.tensor([[2.0], [-2.0]])
+            y = torch.zeros(2)
+            ent.update((y_pred, y))
 
-@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)
+            for dev in [ent._device, ent._sum_of_entropies.device]:
+                assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"