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41bfeb2c1e
* start work on auto opt * lin failure * not beating hcopt * greedy * timing is fast * codegen.search * greedy search in handcode_opt * track running gflops * clean up those files * no failure
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import numpy as np
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np.set_printoptions(suppress=True)
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from copy import deepcopy
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from tinygrad.tensor import Tensor
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from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
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from tinygrad.codegen.search import bufs_from_lin, time_linearizer, actions
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from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats
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from extra.optimization.pretrain import PolicyNet
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if __name__ == "__main__":
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net = PolicyNet()
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load_state_dict(net, safe_load("/tmp/policynet.safetensors"))
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ast_strs = load_worlds()
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for ep_num,ast_str in enumerate(ast_strs):
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print("\nEPISODE", ep_num)
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lin = ast_str_to_lin(ast_str)
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rawbufs = bufs_from_lin(lin)
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linhc = deepcopy(lin)
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linhc.hand_coded_optimizations()
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tm, gflops = time_linearizer(linhc, rawbufs)
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print(f"{tm:10.2f}", linhc.colored_shape())
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while 1:
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probs = net(Tensor([lin_to_feats(lin)]))
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dist = probs.exp().numpy()
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act = dist.argmax()
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if act == 0: break
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try:
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lin.apply_opt(actions[act-1])
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except Exception:
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print("FAILED")
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break
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tm, gflops = time_linearizer(lin, rawbufs)
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print(f"{tm:10.2f}", lin.colored_shape()) |