Starts work on MPAX support #166
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Hi @PTNobel , great work on this PR and thank you for taking it on! I’ve left a few questions, mostly because I’m not very familiar with CvypyLayer. Please feel free to dismiss any of them if they seem too naive.
| if len(problem.parameters()) != len(parameters): | ||
| raise ValueError | ||
| P, c, A, data = extract_linops_as_csr(problem, solver, kwargs) | ||
| Pjax, cjax, Ajax = BCSR.from_scipy_sparse(P.reduced_mat), BCSR.from_scipy_sparse(c), BCSR.from_scipy_sparse(A.reduced_mat) |
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How often will the from_scipy_sparse method be called? Does it happen only when defining the layer, or every time during the training process?
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Only when defining the layer!
| vec = jnp.hstack( | ||
| list(itertools.chain( | ||
| (params[i].reshape(-1, order='F') for _, i in param_id_to_orig_order.items()), | ||
| (jnp.ones(1),)))) # TODO(PTNobel): Add batching |
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Yeah. Batch solving should be the key feature in this PR.
| else: | ||
| raise ValueError('Invalid MPAX algorithm') | ||
| solver = alg(warm_start=self.warm_start, **options) | ||
| self.solver = jax.jit(solver.optimize) |
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Maybe we don't need reuse jit during the training process? since the size of the problems should be the same.
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I don't follow, will JAX auto optimize problems of the same size?
| self.solver = jax.jit(solver.optimize) | ||
| self.output_slices = output_slices | ||
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| def jax_to_data(self, quad_obj_values, lin_obj_values, con_values): # TODO: Add broadcasting (will need jnp.tile to tile structures) |
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Which part of the model can be differentiated in CvxpyLayer? Matrix A, or vector b, c?
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@ZedongPeng this is initial code for the MPAX support. It doesn't work yet, hoping to continue it this week.