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https://github.com/firestar5683/StarPilot.git
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James
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
# opt opinionatedly transforms an ast into an optimized ast using either heuristics or beam search
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from tinygrad.codegen.opt.kernel import Kernel
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from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
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from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, KernelInfo
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from tinygrad.helpers import NOOPT, BEAM, USE_TC, getenv
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from tinygrad.renderer import Renderer
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from tinygrad.uop.spec import type_verify
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def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp|None:
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"""
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Optimize an AST based on heuristics or BEAM search.
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Args:
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ast: The Ops.SINK rooted AST
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renderer: The renderer used to generate the code
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Returns:
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The Ops.SINK rooted AST transformed to apply the opts and with a KernelInfo in the arg.
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"""
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# no shape, no opt
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if ast.src[0].st is None: return None
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new_arg = ast.arg
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if new_arg is None:
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k = Kernel(ast, opts=renderer)
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if not NOOPT:
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if not k.apply_tensor_cores(USE_TC.value): k.apply_opts(hand_coded_optimizations(k))
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if BEAM >= 1:
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from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
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kb = Kernel(ast, opts=renderer)
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rawbufs = bufs_from_lin(kb, allocate=False)
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k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
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new_arg = KernelInfo(opts_to_apply=tuple(k.applied_opts))
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elif len(new_arg.applied_opts): return None
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return Kernel(ast.replace(arg=None), opts=renderer).get_optimized_ast().replace(arg=new_arg)
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pm_get_optimization = PatternMatcher([
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(UPat(Ops.SINK, name="ast"), lambda ctx,ast: get_optimized_ast(ast, ctx)),
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])
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def apply_opt(ast:UOp, renderer:Renderer):
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k = Kernel(ast, opts=renderer)
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k.apply_opts(ast.arg.opts_to_apply)
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ret = k.get_optimized_ast()
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if __debug__: type_verify(list(ret.toposort()))
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return ret
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pm_do_optimize = PatternMatcher([
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(UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_opt(ast, ctx) if ast.arg is not None and ast.arg.opts_to_apply is not None else None),
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])
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