39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from typing import List, Tuple
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from tinygrad.codegen.linearizer import Linearizer
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from tinygrad.features.search import get_linearizer_actions, actions
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_net = None
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def beam_q_estimate(
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beam: List[Tuple[Linearizer, float]]
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) -> List[Tuple[Linearizer, float]]:
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global _net
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if _net is None:
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from tinygrad.nn.state import load_state_dict, safe_load
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from extra.optimization.pretrain_valuenet import ValueNet
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_net = ValueNet(1021 + len(actions), 2)
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load_state_dict(_net, safe_load("/tmp/qnet.safetensors"), verbose=False)
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import Context
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from extra.optimization.helpers import lin_to_feats
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import numpy as np
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feats = []
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lins = []
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base_tms = []
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for lin, tm in beam:
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lin_feats = lin_to_feats(lin)
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for a, v in get_linearizer_actions(lin, include_0=False).items():
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acts = np.zeros(len(actions))
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acts[a - 1] = 1.0
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feats.append(np.concatenate([lin_feats, acts]))
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lins.append(v)
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base_tms.append(tm)
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with Context(BEAM=0):
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with Tensor.train(False):
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preds = _net(Tensor(feats)).numpy()
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pred_time = np.array(base_tms) / np.exp(preds[:, 0])
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return sorted(zip(lins, pred_time), key=lambda x: x[1])
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