test: add xfailing test for MDN bug.

tests/posterior_nn_test.py

@@ -17,7 +17,13 @@
     SNRE_C,
     DirectPosterior,
 )
-from sbi.simulators.linear_gaussian import diagonal_linear_gaussian
+from sbi.simulators.linear_gaussian import (
+    diagonal_linear_gaussian,
+    linear_gaussian,
+    true_posterior_linear_gaussian_mvn_prior,
+)
+from sbi.utils.diagnostics_utils import get_posterior_samples_on_batch
+from tests.test_utils import check_c2st
 
 
 @pytest.mark.parametrize("snpe_method", [SNPE_A, SNPE_C])
@@ -204,3 +210,70 @@ def test_batched_mcmc_sample_log_prob_with_different_x(
         assert torch.allclose(
             samples_m, samples_sep_m, atol=0.2, rtol=0.2
         ), "Batched sampling is not consistent with separate sampling."
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "density_estimator",
+    [
+        pytest.param(
+            "mdn",
+            marks=pytest.mark.xfail(
+                raises=AssertionError, reason="Due to MDN bug in pyknos", strict=True
+            ),
+        ),
+        "maf",
+        "zuko_nsf",
+    ],
+)
+def test_batched_sampling_and_logprob_accuracy(density_estimator: str):
+    """Test with two different observations and compare to sequential methods."""
+    num_dim = 2
+    num_simulations = 2000
+    num_samples = 1000
+    sample_shape = (num_samples,)
+    xos = torch.stack((-1.0 * ones(num_dim), 1.0 * ones(num_dim)))
+    num_xos = xos.shape[0]
+
+    prior = MultivariateNormal(loc=zeros(num_dim), covariance_matrix=eye(num_dim))
+    likelihood_shift = -1.0 * ones(num_dim)
+    likelihood_cov = 0.3 * eye(num_dim)
+    prior_mean = zeros(num_dim)
+    prior_cov = eye(num_dim)
+    prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
+
+    def simulator(theta):
+        return linear_gaussian(theta, likelihood_shift, likelihood_cov)
+
+    inference = SNPE_C(
+        prior=prior, show_progress_bars=False, density_estimator=density_estimator
+    )
+    theta = prior.sample((num_simulations,))
+    x = simulator(theta)
+    posterior_estimator = inference.append_simulations(theta, x).train()
+
+    posterior = DirectPosterior(posterior_estimator=posterior_estimator, prior=prior)
+
+    samples_batched = get_posterior_samples_on_batch(
+        xos, posterior, sample_shape, use_batched_sampling=True
+    )
+    log_probs_batched = posterior.log_prob_batched(samples_batched, xos)
+
+    # check c2st for each xos
+    for idx in range(0, num_xos):
+        gt_posterior = true_posterior_linear_gaussian_mvn_prior(
+            xos[idx], likelihood_shift, likelihood_cov, prior_mean, prior_cov
+        )
+        target_samples = gt_posterior.sample((num_samples,))
+        check_c2st(
+            target_samples,
+            samples_batched[:, idx],
+            alg=f"c2st-batch-vs-non-batch-{density_estimator}-x-idx{idx}",
+        )
+
+        target_log_probs = gt_posterior.log_prob(samples_batched[:, idx])
+        log_probs = posterior.log_prob(samples_batched[:, idx], xos[idx])
+        assert torch.allclose(log_probs, log_probs_batched[:, idx])
+        assert torch.allclose(
+            target_log_probs.exp(), log_probs.exp(), atol=0.4, rtol=0.4
+        ), "Batched log probs are not consistent with non-batched log probs."