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Add the calculation of a score, which will be used during ML training. The score qualifies the quality of a regalloc policy, and is independent of what we train (currently, just eviction), or the regalloc algo itself. We can then use scores to guide training (which happens offline), by formulating a reward based on score variation - the goal being lowering scores (currently, that reward is percentage reduction relative to Greedy's heuristic) Currently, we compute the score by factoring different instruction counts (loads, stores, etc) with the machine basic block frequency, regardless of the instructions' provenance - i.e. they could be due to the regalloc policy or be introduced previously. This is different from RAGreedy::reportStats, which accummulates the effects of the allocator alone. We explored this alternative but found (at least currently) that the more naive alternative introduced here produces better policies. We do intend to consolidate the two, however, as we are actively investigating improvements to our reward function, and will likely want to re-explore scoring just the effects of the allocator. In either case, we want to decouple score calculation from allocation algorighm, as we currently evaluate it after a few more passes after allocation (also, because score calculation should be reusable regardless of allocation algorithm). We intentionally accummulate counts independently because it facilitates per-block reporting, which we found useful for debugging - for instance, we can easily report the counts indepdently, and then cross-reference with perf counter measurements. Differential Revision: https://reviews.llvm.org/D115195
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