JAXSCVI multi particles

docs/release_notes/v0.15.1.rst

@@ -6,6 +6,7 @@ Changes
 ~~~~~~~
 - Remove ``labels_key`` from :class:`~scvi.model.MULTIVI` as it is not used in the model (`#1393`_).
 - Use scvi-tools mean/inv_disp parameterization of negative binomial for :class:`~scvi.model.JaxSCVI` likelihood (`#1386`_).
+- Use multiple particles optionally in :class:`~scvi.model.JaxSCVI` (`#1385`_).
 
 Contributors
 ~~~~~~~~~~~~
@@ -18,5 +19,6 @@ Contributors
 .. _`@watiss`: https://github.com/watiss
 
 .. _`#1393`: https://github.com/YosefLab/scvi-tools/pull/1393
+.. _`#1385`: https://github.com/YosefLab/scvi-tools/pull/1385
 .. _`#1386`: https://github.com/YosefLab/scvi-tools/pull/1386
 

scvi/distributions/_negative_binomial.py

@@ -570,6 +570,10 @@ def __init__(
     def mean(self):
         return self._mean
 
+    @property
+    def inverse_dispersion(self):
+        return self._inverse_dispersion
+
     @validate_sample
     def log_prob(self, value):
         # theta is inverse_dispersion

scvi/model/_jaxscvi.py

@@ -270,6 +270,8 @@ def get_latent_representation(
         self,
         adata: Optional[AnnData] = None,
         indices: Optional[Sequence[int]] = None,
+        give_mean: bool = True,
+        mc_samples: int = 1,
         batch_size: Optional[int] = None,
     ) -> np.ndarray:
         r"""
@@ -301,14 +303,19 @@ def get_latent_representation(
 
         @jax.jit
         def _get_val(array_dict):
-            out = self.bound_module(array_dict)
-            return out.qz.mean
+            out = self.bound_module(array_dict, n_samples=mc_samples)
+            return out
 
         latent = []
         for array_dict in scdl:
-            mean = _get_val(array_dict)
-            latent.append(mean)
-        latent = jnp.concatenate(latent)
+            out = _get_val(array_dict)
+            if give_mean:
+                z = out.qz.mean
+            else:
+                z = out.z
+            latent.append(z)
+        concat_axis = 0 if ((mc_samples == 1) or give_mean) else 1
+        latent = jnp.concatenate(latent, axis=concat_axis)
 
         return np.array(jax.device_get(latent))
 

scvi/module/_jaxvae.py

@@ -75,7 +75,8 @@ def __call__(self, z: jnp.ndarray, batch: jnp.ndarray, is_training: bool):
         h = nn.relu(h)
         h = nn.Dropout(self.dropout_rate)(h, deterministic=not is_training)
         # skip connection
-        h = Dense(self.n_hidden)(jnp.concatenate([h, batch], axis=-1))
+        h = Dense(self.n_hidden)(h)
+        h += Dense(self.n_hidden)(batch)
         h = nn.BatchNorm(momentum=0.99, epsilon=0.001)(
             h, use_running_average=not is_training
         )
@@ -91,6 +92,7 @@ class VAEOutput(NamedTuple):
     kl: jnp.ndarray
     px: NegativeBinomial
     qz: dist.Normal
+    z: jnp.ndarray
 
 
 class JaxVAE(nn.Module):
@@ -105,7 +107,9 @@ class JaxVAE(nn.Module):
     eps: float = 1e-8
 
     @nn.compact
-    def __call__(self, array_dict: Dict[str, np.ndarray]) -> VAEOutput:
+    def __call__(
+        self, array_dict: Dict[str, np.ndarray], n_samples: int = 1
+    ) -> VAEOutput:
 
         x = array_dict[REGISTRY_KEYS.X_KEY]
         batch = array_dict[REGISTRY_KEYS.BATCH_KEY]
@@ -122,7 +126,8 @@ def __call__(self, array_dict: Dict[str, np.ndarray]) -> VAEOutput:
 
         qz = dist.Normal(mean, stddev)
         z_rng = self.make_rng("z")
-        z = qz.rsample(z_rng)
+        sample_shape = () if n_samples == 1 else (n_samples,)
+        z = qz.rsample(z_rng, sample_shape=sample_shape)
         rho_unnorm, disp = FlaxDecoder(
             n_input=self.n_input,
             dropout_rate=0.0,
@@ -142,4 +147,4 @@ def __call__(self, array_dict: Dict[str, np.ndarray]) -> VAEOutput:
         rec_loss = -px.log_prob(x).sum(-1)
         kl_div = dist.kl_divergence(qz, dist.Normal(0, 1)).sum(-1)
 
-        return VAEOutput(rec_loss, kl_div, px, qz)
+        return VAEOutput(rec_loss, kl_div, px, qz, z)

tests/models/test_models.py

@@ -99,7 +99,10 @@ def test_jax_scvi():
 
     model = JaxSCVI(adata, n_latent=n_latent, gene_likelihood="poisson")
     model.train(1, train_size=0.5)
-    model.get_latent_representation()
+    z1 = model.get_latent_representation(give_mean=True, mc_samples=1)
+    assert z1.ndim == 2
+    z2 = model.get_latent_representation(give_mean=False, mc_samples=15)
+    assert (z2.ndim == 3) and (z2.shape[0] == 15)
 
 
 def test_scvi(save_path):