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