mirror of https://github.com/commaai/tinygrad.git
42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
from tinygrad import Device
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from tinygrad.helpers import getenv, DEBUG, BEAM
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from tinygrad.features.search import beam_search, time_linearizer, bufs_from_lin
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from extra.optimization.helpers import load_worlds, ast_str_to_lin
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if __name__ == "__main__":
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filter_reduce = bool(getenv("FILTER_REDUCE"))
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ast_strs = load_worlds(filter_reduce=filter_reduce, filter_novariable=True)
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dev = Device[Device.DEFAULT]
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test_n = getenv("TEST_N", 10)
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single = getenv("NUM", -1)
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if single != -1: ast_strs = ast_strs[single:single+1]
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beam_won, tested = 0, 0
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for num, ast in enumerate(ast_strs[:test_n]):
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def new_lin(): return ast_str_to_lin(ast, opts=dev.compiler.linearizer_opts)
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k = new_lin()
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# k.required_optimizations()
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if not (used_tensor_cores:=k.apply_tensor_cores(getenv("TC", 1))): k.hand_coded_optimizations()
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assert BEAM > 0
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lins = [(("tc" if used_tensor_cores else "hc"), k)]
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if used_tensor_cores:
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lins.append(("hc", new_lin()))
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lins[-1][1].hand_coded_optimizations()
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kb = new_lin()
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# kb.required_optimizations()
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test_rawbuffers = bufs_from_lin(kb) # allocate scratch buffers for optimization
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lins.append((f"beam{BEAM.value}", beam_search(kb, test_rawbuffers, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))))
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timed = sorted([(nm, tk, time_linearizer(tk, test_rawbuffers, allow_test_size=False, clear_l2=True)) for nm, tk in lins], key=lambda x: x[2])
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if DEBUG >= 1: print(" < ".join(f"{nm:6s} : {lin.colored_shape(30, dense=True)} : {tm*1e6:8.2f} us" for nm, lin, tm in timed))
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tested += 1
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if timed[0][0].startswith("beam"):
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beam_won += 1
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print(f"{beam_won=} / {tested=} = {beam_won/tested:.3f}") |