Perf tests

README.md

@@ -12,7 +12,7 @@ JAX bindings and native implementations of differentiable trust region projectio
 - Multiple projection types:
   - KL (Kullback-Leibler divergence)
   - Wasserstein (only diagonal covariance)
-  - Frobenius (wip, not tested)
+  - Frobenius (wip, problem with cov projections)
   - Identity (no projection)
 - Support for both diagonal and full covariance Gaussians (induced from cholesky decomposition)
 - Contextual and non-contextual standard deviations (non-contextual means all standard deviations in batch are expected to be the same)

itpal_jax/base_projection.py

@@ -1,6 +1,8 @@
 from abc import ABC, abstractmethod
 from typing import Dict
+import jax
 import jax.numpy as jnp
+from functools import partial
 
 class BaseProjection(ABC):
     def __init__(self, trust_region_coeff: float = 1.0, mean_bound: float = 0.01, 
@@ -8,22 +10,48 @@ class BaseProjection(ABC):
         self.trust_region_coeff = trust_region_coeff
         self.mean_bound = mean_bound
         self.cov_bound = cov_bound
-        self.full_cov = full_cov
         self.contextual_std = contextual_std
+        self.full_cov = full_cov
 
     @abstractmethod
     def project(self, policy_params: Dict[str, jnp.ndarray], 
                 old_policy_params: Dict[str, jnp.ndarray]) -> Dict[str, jnp.ndarray]:
-        """Project policy parameters.
+        """Project parameters to satisfy trust region constraints."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_trust_region_loss(self, policy_params: Dict[str, jnp.ndarray], 
+                             proj_policy_params: Dict[str, jnp.ndarray]) -> jnp.ndarray:
+        """Compute trust region loss between original and projected parameters."""
+        raise NotImplementedError
+
+    @partial(jax.jit, static_argnames=('self'))
+    def _mean_projection(self, mean: jnp.ndarray, old_mean: jnp.ndarray, 
+                        mean_part: jnp.ndarray) -> jnp.ndarray:
+        """Project mean based on the Mahalanobis objective and trust region.
         
         Args:
-            policy_params: Dictionary with:
+            mean: Current mean vectors
-                - 'loc': mean parameters (batch_size, dim)
+            old_mean: Old mean vectors
-                - 'scale': standard deviations (batch_size, dim) if full_cov=False
+            mean_part: Mahalanobis/Euclidean distance between the two mean vectors
-                - 'scale_tril': Cholesky factor (batch_size, dim, dim) if full_cov=True
+            
-            old_policy_params: Same format as policy_params
+        Returns:
+            Projected mean that satisfies the trust region
         """
-        pass
+        mask = mean_part > self.mean_bound
+        omega = jnp.ones_like(mean_part)
+        omega = jnp.where(mask, jnp.sqrt(mean_part / self.mean_bound) - 1., omega)
+        omega = jnp.maximum(-omega, omega)[..., None]
+        
+        # Use matrix operations instead of boolean indexing
+        m = (mean + omega * old_mean) / (1. + omega + 1e-16)
+        mask_matrix = mask[..., None].astype(mean.dtype)
+        return mask_matrix * m + (1 - mask_matrix) * mean
+
+    def _cov_projection(self, scale_or_tril: jnp.ndarray, old_scale_or_tril: jnp.ndarray, 
+                       cov_part: jnp.ndarray) -> jnp.ndarray:
+        """Project covariance parameters."""
+        raise NotImplementedError
 
     def _calc_covariance(self, params: Dict[str, jnp.ndarray]) -> jnp.ndarray:
         """Convert scale representation to covariance matrix."""

itpal_jax/frobenius_projection.py

@@ -2,6 +2,7 @@ import jax.numpy as jnp
 from .base_projection import BaseProjection
 from typing import Dict
 import jax
+from functools import partial
 
 class FrobeniusProjection(BaseProjection):
     def __init__(self, trust_region_coeff: float = 1.0, mean_bound: float = 0.01,
@@ -47,6 +48,7 @@ class FrobeniusProjection(BaseProjection):
         else:
             return {"loc": proj_mean, "scale": scale_or_tril}
 
+    @partial(jax.jit, static_argnames=('self'))
     def get_trust_region_loss(self, policy_params: Dict[str, jnp.ndarray], 
                              proj_policy_params: Dict[str, jnp.ndarray]) -> jnp.ndarray:
         mean = policy_params["loc"]
@@ -60,6 +62,7 @@ class FrobeniusProjection(BaseProjection):
 
         return (mean_diff + cov_diff).mean() * self.trust_region_coeff
 
+    @partial(jax.jit, static_argnames=('self'))
     def _gaussian_frobenius(self, p, q):
         mean, cov = p
         old_mean, old_cov = q
@@ -88,14 +91,6 @@ class FrobeniusProjection(BaseProjection):
 
         return mean_part, cov_part
 
-    def _mean_projection(self, mean: jnp.ndarray, old_mean: jnp.ndarray, 
-                        mean_part: jnp.ndarray) -> jnp.ndarray:
-        diff = mean - old_mean
-        norm = jnp.sqrt(mean_part)
-        return jnp.where(norm > self.mean_bound, 
-                        old_mean + diff * self.mean_bound / norm[..., None], 
-                        mean)
-
     def _cov_projection(self, cov: jnp.ndarray, old_cov: jnp.ndarray, 
                        cov_part: jnp.ndarray) -> jnp.ndarray:
         batch_shape = cov.shape[:-2] if cov.ndim > 2 else cov.shape[:-1]
@@ -110,8 +105,10 @@ class FrobeniusProjection(BaseProjection):
         if self.full_cov:
             new_cov = (cov + jnp.einsum('...,...ij->...ij', eta, old_cov)) / \
                       (1. + eta + 1e-16)[..., None, None]
-            proj_cov = jnp.where(cov_mask[..., None, None], new_cov, cov)
+            mask_matrix = cov_mask[..., None, None].astype(cov.dtype)
+            proj_cov = mask_matrix * new_cov + (1 - mask_matrix) * cov
             return jnp.linalg.cholesky(proj_cov)
         else:
             new_cov = (cov + eta[..., None] * old_cov) / (1. + eta + 1e-16)[..., None]
-            return jnp.where(cov_mask[..., None], jnp.sqrt(new_cov), cov)
+            mask_matrix = cov_mask[..., None].astype(cov.dtype)
+            return mask_matrix * jnp.sqrt(new_cov) + (1 - mask_matrix) * cov

itpal_jax/identity_projection.py

@@ -1,6 +1,8 @@
 import jax.numpy as jnp
 from .base_projection import BaseProjection
 from typing import Dict
+import jax
+from functools import partial
 
 class IdentityProjection(BaseProjection):
     def __init__(self, trust_region_coeff: float = 1.0, mean_bound: float = 0.01, 
@@ -8,10 +10,12 @@ class IdentityProjection(BaseProjection):
         super().__init__(trust_region_coeff=trust_region_coeff, mean_bound=mean_bound,
                         cov_bound=cov_bound, contextual_std=contextual_std, full_cov=full_cov)
 
+    @partial(jax.jit, static_argnames=('self'))
     def project(self, policy_params: Dict[str, jnp.ndarray], 
                 old_policy_params: Dict[str, jnp.ndarray]) -> Dict[str, jnp.ndarray]:
         return policy_params
 
+    @partial(jax.jit, static_argnames=('self'))
     def get_trust_region_loss(self, policy_params: Dict[str, jnp.ndarray], 
                              proj_policy_params: Dict[str, jnp.ndarray]) -> jnp.ndarray:
         return jnp.array(0.0)

itpal_jax/kl_projection.py

@@ -86,13 +86,25 @@ class KLProjection(BaseProjection):
         else:
             return {"loc": proj_mean, "scale": proj_scale_or_tril}
 
+    @partial(jax.jit, static_argnames=('self'))
     def get_trust_region_loss(self, policy_params: Dict[str, jnp.ndarray], 
                              proj_policy_params: Dict[str, jnp.ndarray]) -> jnp.ndarray:
-        mean, scale_or_tril = policy_params["loc"], policy_params["scale"]
+        """Compute trust region loss between original and projected parameters."""
-        proj_mean, proj_scale_or_tril = proj_policy_params["loc"], proj_policy_params["scale"]
+        # Get the right scale parameter based on full_cov
+        mean = policy_params["loc"]
+        proj_mean = proj_policy_params["loc"]
+        
+        if self.full_cov:
+            scale_or_tril = policy_params["scale_tril"]
+            proj_scale_or_tril = proj_policy_params["scale_tril"]
+        else:
+            scale_or_tril = policy_params["scale"]
+            proj_scale_or_tril = proj_policy_params["scale"]
+
         kl = sum(self._gaussian_kl((mean, scale_or_tril), (proj_mean, proj_scale_or_tril)))
         return jnp.mean(kl) * self.trust_region_coeff
 
+    @partial(jax.jit, static_argnames=('self'))
     def _gaussian_kl(self, p: Tuple[jnp.ndarray, jnp.ndarray], 
                     q: Tuple[jnp.ndarray, jnp.ndarray]) -> Tuple[jnp.ndarray, jnp.ndarray]:
         mean, scale_or_tril = p
@@ -117,6 +129,7 @@ class KLProjection(BaseProjection):
 
         return maha_part, cov_part
 
+    @partial(jax.jit, static_argnames=('self'))
     def _maha(self, x: jnp.ndarray, y: jnp.ndarray, scale_or_tril: jnp.ndarray) -> jnp.ndarray:
         diff = x - y
         if self.full_cov:
@@ -127,21 +140,17 @@ class KLProjection(BaseProjection):
         else:
             return jnp.sum(jnp.square(diff / scale_or_tril), axis=-1)
 
+    @partial(jax.jit, static_argnames=('self'))
     def _log_determinant(self, scale_or_tril: jnp.ndarray) -> jnp.ndarray:
         if self.full_cov:
             return 2 * jnp.sum(jnp.log(jnp.diagonal(scale_or_tril, axis1=-2, axis2=-1)), axis=-1)
         else:
             return 2 * jnp.sum(jnp.log(scale_or_tril), axis=-1)
 
+    @partial(jax.jit, static_argnames=('self'))
     def _batched_trace_square(self, x: jnp.ndarray) -> jnp.ndarray:
         return jnp.sum(x ** 2, axis=(-2, -1))
 
-    def _mean_projection(self, mean: jnp.ndarray, old_mean: jnp.ndarray, 
-                        mean_part: jnp.ndarray) -> jnp.ndarray:
-        return old_mean + (mean - old_mean) * jnp.sqrt(
-            self.mean_bound / (mean_part + 1e-8)
-        )[..., None]
-
     def _cov_projection(self, scale_or_tril: jnp.ndarray, old_scale_or_tril: jnp.ndarray, cov_part: jnp.ndarray) -> jnp.ndarray:
         if self.full_cov:
             cov = jnp.matmul(scale_or_tril, jnp.swapaxes(scale_or_tril, -1, -2))
@@ -151,14 +160,13 @@ class KLProjection(BaseProjection):
             old_cov = old_scale_or_tril ** 2
 
         mask = cov_part > self.cov_bound
-        proj_scale_or_tril = jnp.zeros_like(scale_or_tril)
+        proj_scale_or_tril = scale_or_tril  # Start with original scale
-        proj_scale_or_tril = jnp.where(~mask, scale_or_tril, proj_scale_or_tril)
+        
-
         if mask.any():
             if self.full_cov:
                 proj_cov = project_full_covariance(cov, scale_or_tril, old_scale_or_tril, self.cov_bound)
-                is_invalid = jnp.isnan(proj_cov.mean(axis=(-2, -1))) & mask
+                is_invalid = jnp.isnan(proj_cov.mean(axis=(-2, -1)))
-                proj_scale_or_tril = jnp.where(is_invalid, old_scale_or_tril, proj_scale_or_tril)
+                proj_scale_or_tril = jnp.where(is_invalid[..., None, None], old_scale_or_tril, scale_or_tril)
                 mask = mask & ~is_invalid
                 chol = jnp.linalg.cholesky(proj_cov)
                 proj_scale_or_tril = jnp.where(mask[..., None, None], chol, proj_scale_or_tril)
@@ -166,10 +174,12 @@ class KLProjection(BaseProjection):
                 proj_cov = project_diag_covariance(cov, old_cov, self.cov_bound)
                 is_invalid = (jnp.isnan(proj_cov.mean(axis=-1)) | 
                             jnp.isinf(proj_cov.mean(axis=-1)) | 
-                            (proj_cov.min(axis=-1) < 0)) & mask
+                            (proj_cov.min(axis=-1) < 0))
-                proj_scale_or_tril = jnp.where(is_invalid, old_scale_or_tril, proj_scale_or_tril)
+                proj_scale_or_tril = jnp.where(is_invalid[..., None], old_scale_or_tril, scale_or_tril)
                 mask = mask & ~is_invalid
-                proj_scale_or_tril = jnp.where(mask[..., None], jnp.sqrt(proj_cov), proj_scale_or_tril)
+                proj_scale_or_tril = jnp.where(mask[..., None], jnp.sqrt(proj_cov), scale_or_tril)
+        else:
+            proj_scale_or_tril = scale_or_tril
 
         return proj_scale_or_tril
 

itpal_jax/wasserstein_projection.py

@@ -2,7 +2,9 @@ import jax.numpy as jnp
 from .base_projection import BaseProjection
 from typing import Dict, Tuple
 import jax
+from functools import partial
 
+@jax.jit
 def scale_tril_to_sqrt(scale_tril: jnp.ndarray) -> jnp.ndarray:
     """
     'Converts' scale_tril to scale_sqrt.
@@ -52,6 +54,7 @@ class WassersteinProjection(BaseProjection):
 
         return {"loc": proj_mean, "scale": proj_scale}
 
+    @partial(jax.jit, static_argnames=('self'))
     def get_trust_region_loss(self, policy_params: Dict[str, jnp.ndarray], 
                              proj_policy_params: Dict[str, jnp.ndarray]) -> jnp.ndarray:
         mean = policy_params["loc"]
@@ -65,14 +68,6 @@ class WassersteinProjection(BaseProjection):
         w2 = mean_part + cov_part
         return w2.mean() * self.trust_region_coeff
 
-    def _mean_projection(self, mean: jnp.ndarray, old_mean: jnp.ndarray, 
-                        mean_part: jnp.ndarray) -> jnp.ndarray:
-        diff = mean - old_mean
-        norm = jnp.sqrt(mean_part)
-        return jnp.where(norm > self.mean_bound, 
-                        old_mean + diff * self.mean_bound / norm[..., None], 
-                        mean)
-
     def _scale_projection(self, scale: jnp.ndarray, old_scale: jnp.ndarray, 
                          scale_part: jnp.ndarray) -> jnp.ndarray:
         """Project scale parameters using multiplicative update.
@@ -96,19 +91,21 @@ class WassersteinProjection(BaseProjection):
                         eta)
         eta = jnp.maximum(-eta, eta)
         
-        # Multiplicative update with correct broadcasting
+        # Multiplicative update with matrix operations
         if scale.ndim > 2:  # Full covariance case
             new_scale = (scale + jnp.einsum('...,...ij->...ij', eta, old_scale)) / \
                        (1. + eta + 1e-16)[..., None, None]
-            mask = cov_mask[..., None, None]
+            mask_matrix = cov_mask[..., None, None].astype(scale.dtype)
+            return mask_matrix * new_scale + (1 - mask_matrix) * scale
         else:  # Diagonal case
             new_scale = (scale + eta[..., None] * old_scale) / \
                        (1. + eta + 1e-16)[..., None]
-            mask = cov_mask[..., None]
+            mask_matrix = cov_mask[..., None].astype(scale.dtype)
-        
+            return mask_matrix * new_scale + (1 - mask_matrix) * scale
-        return jnp.where(mask, new_scale, scale)
 
-    def _gaussian_wasserstein(self, p, q):
+    @staticmethod
+    @jax.jit
+    def _gaussian_wasserstein(p, q):
         mean, scale = p
         mean_other, scale_other = q
         

perf_tests/perf_test_frobenius.py

@@ -0,0 +1,50 @@
+import jax
+import jax.numpy as jnp
+import time
+from itpal_jax import FrobeniusProjection
+
+def generate_params(key, batch_size, dim):
+    keys = jax.random.split(key, 2)
+    return {
+        "loc": jax.random.normal(keys[0], (batch_size, dim)),
+        "scale": jax.nn.softplus(jax.random.normal(keys[1], (batch_size, dim)))
+    }
+
+def main():
+    # Test parameters
+    batch_size = 32
+    dim = 8
+    n_iterations = 1000
+    
+    # Initialize projector
+    proj = FrobeniusProjection(mean_bound=0.1, cov_bound=0.1, contextual_std=True)
+    
+    # Compile function
+    proj_fn = lambda p, op: proj.project(p, op)
+    proj_fn = jax.jit(proj_fn)
+    
+    # Generate initial key
+    key = jax.random.PRNGKey(0)
+    
+    # Warmup
+    for _ in range(10):
+        key, subkey1, subkey2 = jax.random.split(key, 3)
+        params = generate_params(subkey1, batch_size, dim)
+        old_params = generate_params(subkey2, batch_size, dim)
+        proj_fn(params, old_params)
+    
+    # Time projections
+    start_time = time.time()
+    for _ in range(n_iterations):
+        key, subkey1, subkey2 = jax.random.split(key, 3)
+        params = generate_params(subkey1, batch_size, dim)
+        old_params = generate_params(subkey2, batch_size, dim)
+        proj_fn(params, old_params)
+    end_time = time.time()
+    
+    print(f"Frobenius Projection:")
+    print(f"Average time per projection: {(end_time - start_time) / n_iterations * 1000:.3f} ms")
+    print(f"Total time for {n_iterations} iterations: {end_time - start_time:.3f} s")
+
+if __name__ == "__main__":
+    main() 

perf_tests/perf_test_kl.py

@@ -0,0 +1,49 @@
+import jax
+import jax.numpy as jnp
+import time
+from itpal_jax import KLProjection
+
+def generate_params(key, batch_size, dim):
+    keys = jax.random.split(key, 2)
+    return {
+        "loc": jax.random.normal(keys[0], (batch_size, dim)),
+        "scale": jax.nn.softplus(jax.random.normal(keys[1], (batch_size, dim)))
+    }
+
+def main():
+    # Test parameters
+    batch_size = 32
+    dim = 8
+    n_iterations = 1000
+    
+    # Initialize projector
+    proj = KLProjection(mean_bound=0.1, cov_bound=0.1, contextual_std=True)
+    
+    # No JIT for KL projection since it uses C++ backend
+    proj_fn = lambda p, op: proj.project(p, op)
+    
+    # Generate initial key
+    key = jax.random.PRNGKey(0)
+    
+    # Warmup
+    for _ in range(10):
+        key, subkey1, subkey2 = jax.random.split(key, 3)
+        params = generate_params(subkey1, batch_size, dim)
+        old_params = generate_params(subkey2, batch_size, dim)
+        proj_fn(params, old_params)
+    
+    # Time projections
+    start_time = time.time()
+    for _ in range(n_iterations):
+        key, subkey1, subkey2 = jax.random.split(key, 3)
+        params = generate_params(subkey1, batch_size, dim)
+        old_params = generate_params(subkey2, batch_size, dim)
+        proj_fn(params, old_params)
+    end_time = time.time()
+    
+    print(f"KL Projection:")
+    print(f"Average time per projection: {(end_time - start_time) / n_iterations * 1000:.3f} ms")
+    print(f"Total time for {n_iterations} iterations: {end_time - start_time:.3f} s")
+
+if __name__ == "__main__":
+    main()

perf_tests/perf_test_wasserstein.py

@@ -0,0 +1,50 @@
+import jax
+import jax.numpy as jnp
+import time
+from itpal_jax import WassersteinProjection
+
+def generate_params(key, batch_size, dim):
+    keys = jax.random.split(key, 2)
+    return {
+        "loc": jax.random.normal(keys[0], (batch_size, dim)),
+        "scale": jax.nn.softplus(jax.random.normal(keys[1], (batch_size, dim)))
+    }
+
+def main():
+    # Test parameters
+    batch_size = 32
+    dim = 8
+    n_iterations = 1000
+    
+    # Initialize projector
+    proj = WassersteinProjection(mean_bound=0.1, cov_bound=0.1, contextual_std=True)
+    
+    # Compile function
+    proj_fn = lambda p, op: proj.project(p, op)
+    proj_fn = jax.jit(proj_fn)
+    
+    # Generate initial key
+    key = jax.random.PRNGKey(0)
+    
+    # Warmup
+    for _ in range(10):
+        key, subkey1, subkey2 = jax.random.split(key, 3)
+        params = generate_params(subkey1, batch_size, dim)
+        old_params = generate_params(subkey2, batch_size, dim)
+        proj_fn(params, old_params)
+    
+    # Time projections
+    start_time = time.time()
+    for _ in range(n_iterations):
+        key, subkey1, subkey2 = jax.random.split(key, 3)
+        params = generate_params(subkey1, batch_size, dim)
+        old_params = generate_params(subkey2, batch_size, dim)
+        proj_fn(params, old_params)
+    end_time = time.time()
+    
+    print(f"Wasserstein Projection:")
+    print(f"Average time per projection: {(end_time - start_time) / n_iterations * 1000:.3f} ms")
+    print(f"Total time for {n_iterations} iterations: {end_time - start_time:.3f} s")
+
+if __name__ == "__main__":
+    main() 

tests/test_projections.py

@@ -151,6 +151,10 @@ def test_full_covariance_projection(ProjectionClass):
     eigvals = jnp.linalg.eigvalsh(cov)
     assert jnp.all(eigvals > 0)
     
+    # Check trust region loss computation works
+    tr_loss = proj.get_trust_region_loss(params, proj_params)
+    assert jnp.isfinite(tr_loss)
+    
     # Only check KL bounds for KL projection
     if ProjectionClass in [KLProjection]:
         kl = compute_gaussian_kl(proj_params, old_params, full_cov=True)