itpal_jax/kl_projection.py

@@ -160,28 +160,26 @@ class KLProjection(BaseProjection):
             old_cov = old_scale_or_tril ** 2
 
         mask = cov_part > self.cov_bound
-        proj_scale_or_tril = scale_or_tril  # Start with original scale
         
-        if mask.any():
+        # Always compute both branches and use matrix operations to select
-            if self.full_cov:
+        if self.full_cov:
-                proj_cov = project_full_covariance(cov, scale_or_tril, old_scale_or_tril, self.cov_bound)
+            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)))
+            is_invalid = jnp.isnan(proj_cov.mean(axis=(-2, -1)))
-                proj_scale_or_tril = jnp.where(is_invalid[..., None, None], old_scale_or_tril, scale_or_tril)
+            valid_mask = mask & ~is_invalid
-                mask = mask & ~is_invalid
+            
-                chol = jnp.linalg.cholesky(proj_cov)
+            # Compute cholesky for all, let matrix ops handle selection
-                proj_scale_or_tril = jnp.where(mask[..., None, None], chol, proj_scale_or_tril)
+            chol = jnp.linalg.cholesky(proj_cov)
-            else:
+            mask_matrix = valid_mask[..., None, None].astype(scale_or_tril.dtype)
-                proj_cov = project_diag_covariance(cov, old_cov, self.cov_bound)
+            return mask_matrix * chol + (1 - mask_matrix) * scale_or_tril
-                is_invalid = (jnp.isnan(proj_cov.mean(axis=-1)) | 
-                            jnp.isinf(proj_cov.mean(axis=-1)) | 
-                            (proj_cov.min(axis=-1) < 0))
-                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), scale_or_tril)
         else:
-            proj_scale_or_tril = scale_or_tril
+            proj_cov = project_diag_covariance(cov, old_cov, self.cov_bound)
-
+            is_invalid = (jnp.isnan(proj_cov.mean(axis=-1)) | 
-        return proj_scale_or_tril
+                        jnp.isinf(proj_cov.mean(axis=-1)) | 
+                        (proj_cov.min(axis=-1) < 0))
+            valid_mask = mask & ~is_invalid
+            
+            mask_matrix = valid_mask[..., None].astype(scale_or_tril.dtype)
+            return mask_matrix * jnp.sqrt(proj_cov) + (1 - mask_matrix) * scale_or_tril
 
     def _validate_inputs(self, policy_params, old_policy_params):
         """Validate input parameters have correct format."""

perf_tests/perf_test_frobenius.py

@@ -1,50 +0,0 @@
-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

@@ -1,49 +0,0 @@
-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

@@ -1,50 +0,0 @@
-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()