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125 lines
6.0 KiB
125 lines
6.0 KiB
from __future__ import annotations
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from typing import Optional, Callable, cast, TYPE_CHECKING
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import functools, itertools
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from dataclasses import dataclass, field, replace
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from tinygrad.helpers import to_function_name, dedup, prod
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from tinygrad.uop.ops import Ops, UOp, sym_infer, sint, Variable, ssimplify, GroupOp, PatternMatcher
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if TYPE_CHECKING:
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from tinygrad.opt.tc import TensorCore
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from tinygrad.opt.kernel import Opt
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@dataclass(frozen=True)
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class Estimates:
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# number of FLOPS used in the Kernel
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ops:sint = 0
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# bytes accessed in loads and stores
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lds:sint = 0
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# total bytes accessed, counting only once for bytes that are accessed multiple times
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mem:sint = 0
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def __add__(self, o:Estimates): return Estimates(self.ops + o.ops, self.lds + o.lds, self.mem + o.mem)
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def simplify(self): return Estimates(ssimplify(self.ops), ssimplify(self.lds), ssimplify(self.mem))
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@staticmethod
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def from_uops(uops:list[UOp], ignore_indexing=False) -> Estimates:
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flops: sint = 0
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lds: sint = 0
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mults: sint = 1
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mult_stack: list[sint] = []
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dont_count: set[UOp] = set()
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if ignore_indexing:
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for u in uops:
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if u.op in {Ops.LOAD, Ops.STORE}:
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dont_count = dont_count.union(u.src[0].toposort())
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if len(u.src) > 2: dont_count = dont_count.union(u.src[2].toposort())
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elif u.op is Ops.IF:
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dont_count = dont_count.union(u.src[0].toposort())
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for u in uops:
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if u.op is Ops.RANGE:
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mult_stack.append(mults)
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mults *= cast(sint, u.src[0].ssimplify())
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# SPECIAL are already counted in mults
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mults = mults.substitute({x:x.const_like(0) for x in mults.toposort() if x.op is Ops.SPECIAL}) if isinstance(mults, UOp) else mults
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elif u.op is Ops.ENDRANGE: mults = mult_stack.pop(-1)
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elif u.op is Ops.SPECIAL: mults *= u.arg[1] # NOTE: we don't push to the mult_stack here, you can't end these
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elif u.op is Ops.LOAD: lds += u.dtype.itemsize * mults
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elif u.op is Ops.STORE: lds += u.src[1].dtype.itemsize * mults
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elif u.op in GroupOp.ALU and u not in dont_count: flops += (mults * (2 if u.op is Ops.MULACC else 1)) * u.dtype.count
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elif u.op is Ops.WMMA and u not in dont_count: flops += 2 * prod(u.arg[1]) // u.arg[5] * mults
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return Estimates(flops, lds, lds) # TODO: properly track memory, lds is always a high estimate
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@dataclass
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class ProgramSpec:
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name:str
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src:str
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device:str
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ast:UOp # save the base ast (this is method cache key)
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uops:Optional[list[UOp]]=None
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# filled in from uops (if we have uops)
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global_size:Optional[list[int]]=None
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local_size:Optional[list[int]]=None
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vars:list[Variable]=field(default_factory=list)
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globals:list[int]=field(default_factory=list)
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outs:list[int]=field(default_factory=list)
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ins:list[int]=field(default_factory=list)
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_ran_post_init:bool=False # NOTE: this is needed if you call replace on the Program
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def __post_init__(self):
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if not self._ran_post_init and self.uops is not None:
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# single pass through the uops
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for u in self.uops:
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if u.op is Ops.DEFINE_VAR: self.vars.append(u)
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if u.op is Ops.DEFINE_GLOBAL: self.globals.append(u.arg)
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if u.op is Ops.STORE: self.outs.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
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if u.op is Ops.LOAD: self.ins.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
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if u.op is Ops.SPECIAL:
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# NOTE: you have to set local_size and global_size to the base [1,1,1] outside this
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if u.arg[0][0] == 'i': self.local_size = None
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special_size = self.local_size if u.arg[0][0] == 'l' else self.global_size
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if special_size is not None: special_size[int(u.arg[0][-1])] = u.arg[1]
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self.vars = sorted(self.vars, key=lambda v: v.arg)
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self.outs = sorted(dedup(self.outs))
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self.ins = sorted(dedup(self.ins))
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self._ran_post_init = True
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@functools.cached_property
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def mem_estimate(self) -> sint:
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# group non-local bufs by the op type (LOAD or STORE) and the buffer arg. take the max access of that buffer in bytes
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# TODO: these max and min don't work on symbolic, and results are very wrong.
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return sum(max(x.src[0].dtype.nbytes() for x in group)
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for _, group in itertools.groupby([x for x in self.ast.toposort() if x.op in {Ops.LOAD, Ops.STORE} and x.src[0].base.op is Ops.DEFINE_GLOBAL],
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key=lambda x: (x.op, x.src[0].base.arg)))
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@functools.cached_property
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def estimates(self) -> Estimates:
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return replace(Estimates() if self.uops is None else Estimates.from_uops(self.uops, ignore_indexing=True), mem=self.mem_estimate)
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@functools.cached_property
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def function_name(self) -> str: return to_function_name(self.name)
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@property
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def applied_opts(self) -> tuple[Opt, ...]|None: return self.uops[-1].arg.applied_opts if \
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self.uops is not None and self.uops[-1].op is Ops.SINK and self.uops[-1].arg is not None else None
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def launch_dims(self, var_vals:dict[Variable, int]):
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global_size = [sym_infer(sz, var_vals) for sz in self.global_size] if self.global_size is not None else None
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local_size = [sym_infer(sz, var_vals) for sz in self.local_size] if self.local_size is not None else None
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return global_size, local_size
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class Renderer:
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device: str = ""
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suffix: str = ""
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# TODO: make this generic with a list of supported types
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supports_float4: bool = True
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has_local: bool = True
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has_shared: bool = True
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# NOTE: these two should be in (x,y,z) order to match the max_sizes argument in get_grouped_dims
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global_max: Optional[tuple[int, ...]] = (0x8FFFFFFF,) * (3) # TODO: Ops.SPECIAL int32 indexes right now
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local_max: Optional[tuple[int, ...]] = (0x8FFFFFFF,) * (3) # TODO: Ops.SPECIAL int32 indexes right now
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shared_max: int = 32768
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tensor_cores: list[TensorCore] = []
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pre_matcher: Optional[PatternMatcher] = None
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extra_matcher: Optional[PatternMatcher] = None
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code_for_op: dict[Ops, Callable] = {}
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def __reduce__(self): return self.__class__, ()
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def render(self, uops:list[UOp]) -> str: raise NotImplementedError("needs a renderer")
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