fix #1343 device handling in mog_log_prob (#1356)

* fix device in mog_log_prob, add test, fix tests.

* add cpu test for multiround mdn

sbi/utils/sbiutils.py

@@ -841,7 +841,9 @@ def mog_log_prob(
 
     # Split up evaluation into parts.
     weights = logits_pp - torch.logsumexp(logits_pp, dim=-1, keepdim=True)
-    constant = -(output_dim / 2.0) * torch.log(torch.tensor([2 * pi]))
+    constant = -(output_dim / 2.0) * torch.log(
+        torch.tensor([2 * pi], device=theta.device)
+    )
     log_det = 0.5 * torch.log(torch.det(precisions_pp))
     theta_minus_mean = theta.expand_as(means_pp) - means_pp
     exponent = -0.5 * batched_mixture_vmv(precisions_pp, theta_minus_mean)

tests/inference_on_device_test.py

@@ -3,7 +3,7 @@
 
 from __future__ import annotations
 
-from typing import Tuple
+from typing import Tuple, Union
 
 import pytest
 import torch
@@ -127,7 +127,7 @@ def simulator(theta):
                 model=model, num_transforms=2, dtype=torch.float32
             )
         )
-        train_kwargs = dict(force_first_round_loss=True)
+        train_kwargs = dict()
     elif method == NLE:
         kwargs = dict(
             density_estimator=likelihood_nn(
@@ -152,9 +152,12 @@ def simulator(theta):
         x = simulator(theta).to(data_device)
         theta = theta.to(data_device)
 
-        estimator = inferer.append_simulations(theta, x, data_device=data_device).train(
-            training_batch_size=100, max_num_epochs=max_num_epochs, **train_kwargs
+        data_kwargs = (
+            dict(proposal=proposals[-1]) if method in [NPE_A, NPE_C] else dict()
         )
+        estimator = inferer.append_simulations(
+            theta, x, data_device=data_device, **data_kwargs
+        ).train(max_num_epochs=max_num_epochs, **train_kwargs)
 
         # mcmc cases
         if sampling_method in ["slice_np", "slice_np_vectorized", "nuts_pymc"]:
@@ -436,3 +439,29 @@ def test_boxuniform_device_handling(arg_device, device):
         low=zeros(1).to(arg_device), high=ones(1).to(arg_device), device=device
     )
     NPE_C(prior=prior, device=arg_device)
+
+
+@pytest.mark.gpu
+@pytest.mark.parametrize("method", [NPE_A, NPE_C])
+@pytest.mark.parametrize("device", ["cpu", "gpu"])
+def test_multiround_mdn_training_on_device(method: Union[NPE_A, NPE_C], device: str):
+    num_dim = 2
+    num_rounds = 2
+    num_simulations = 100
+    device = process_device("gpu")
+    prior = BoxUniform(-torch.ones(num_dim), torch.ones(num_dim), device=device)
+    simulator = diagonal_linear_gaussian
+
+    estimator = "mdn_snpe_a" if method == NPE_A else "mdn"
+
+    trainer = method(prior, density_estimator=estimator, device=device)
+
+    theta = prior.sample((num_simulations,))
+    x = simulator(theta)
+
+    proposal = prior
+    for _ in range(num_rounds):
+        trainer.append_simulations(theta, x, proposal=proposal).train(max_num_epochs=2)
+        proposal = trainer.build_posterior().set_default_x(torch.zeros(num_dim))
+        theta = proposal.sample((num_simulations,))
+        x = simulator(theta)