mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-28 00:08:16 -05:00
move ast to the kernel (#5362)
* move ast to the kernel * locals aren't image * comment
This commit is contained in:
George Hotz
@@ -1,11 +1,13 @@
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from __future__ import annotations
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import itertools
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from typing import Optional, List, Tuple, cast, Dict, Union
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import itertools, functools
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from dataclasses import replace
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from collections import defaultdict
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from typing import Optional, List, Tuple, cast, Dict, Union, Final, DefaultDict
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from tinygrad.ops import LazyOp, UnaryOps, BinaryOps, ReduceOps, MemBuffer, ConstBuffer, BufferOps, UNSAFE_PAD_OPS, verify_lazyop
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from tinygrad.device import Device
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from tinygrad.renderer import Renderer, TensorCore
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from tinygrad.dtype import dtypes, ImageDType, DType
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from tinygrad.helpers import all_same, colored, ansilen, dedup, flatten, getenv, prod, DEBUG, round_up, all_int, get_contraction
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from tinygrad.dtype import dtypes, ImageDType
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from tinygrad.helpers import all_same, colored, ansilen, dedup, flatten, getenv, prod, DEBUG, round_up, all_int, get_contraction, to_function_name
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.symbolic import sint
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from tinygrad.shape.view import strides_for_shape
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@@ -46,14 +48,6 @@ class TensorCoreOptions:
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elif removed_axis == axes[tc_dim]: axes_exist[tc_dim] = False
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self.axes, self.axes_exist = tuple(axes), tuple(axes_exist)
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@dataclass(frozen=True)
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class LocalBuffer:
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name: str
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size: int
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dtype: DType = dtypes.float32
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realized: None = None
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def __str__(self): return f"localbuffer<{self.name}[{self.size}]>"
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class Kernel:
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def __init__(self, *ast:LazyOp, opts:Optional[Renderer]=None):
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self.opts = opts if opts is not None else Device[Device.DEFAULT].renderer
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@@ -69,14 +63,14 @@ class Kernel:
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self.outbufs, self.vars = [x.arg for x in self.ast], flatten([x.vars() for x in self.ast])
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loadops = [BufferOps.LOAD, BufferOps.CONST]
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self.bufs: List[Union[MemBuffer, ConstBuffer, LocalBuffer]] = self.outbufs + dedup([x.arg for x in self.lazyops if x.op in loadops])
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self.bufs: List[Union[MemBuffer, ConstBuffer]] = self.outbufs + dedup([x.arg for x in self.lazyops if x.op in loadops])
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# get earlybufs, before any reduceops
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self.earlybufs = [x.arg for reduceop in self.reduceops for x in reduceop.lazyops if x.op in BufferOps]
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self.full_buf_index: int = self.bufs.index(self.earlybufs[0]) if self.earlybufs else 0
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# create new shapetrackers inside this kernel, we will permute them
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self.sts: List[ShapeTracker] = [x.st for x in cast(List[Union[MemBuffer, ConstBuffer]], self.bufs)]
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self.sts: List[ShapeTracker] = [x.st for x in self.bufs]
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# move all reduce axes to the end
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reduce = list(enumerate(zip(self.full_shape, self.output_shape)))
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@@ -109,7 +103,7 @@ class Kernel:
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# things downstream of the AST
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ret.reduceops, ret.outbufs, ret.vars, ret.bufs, ret.earlybufs, ret.full_buf_index = \
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self.reduceops, self.outbufs, self.vars, [x for x in self.bufs if not isinstance(x, LocalBuffer)], self.earlybufs, self.full_buf_index
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self.reduceops, self.outbufs, self.vars, self.bufs, self.earlybufs, self.full_buf_index
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ret.sts = self.sts[:len(ret.bufs)] # NOTE: must redo the local buffers with TC in beam
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# parameters for optimizations
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@@ -620,3 +614,85 @@ class Kernel:
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will_delete_shape = local_sz == self.full_shape[axis]
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self.apply_opt(Opt(OptOps.LOCAL, axis, local_sz))
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if will_delete_shape: deleted_shape += 1
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# **** kernel outputs ****
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kernel_cnt: Final[DefaultDict[str, int]] = defaultdict(int)
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@functools.cached_property
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def name(self) -> str:
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# kernel name (before late upcast)
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name = ("r" if self.reduceop else ("C" if all(x.op in BufferOps for x in self.lazyops) else "E")) + \
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(f"{len(self.outbufs)}_" if len(self.outbufs) > 1 else "_") + \
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colored('_', 'BLACK').join([colored(str(x), c) for x,c in zip(self.full_shape, self.colors())])
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# name the function something unique
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Kernel.kernel_cnt[(function_name := to_function_name(name))] += 1
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suffix = f"{'n'+str(Kernel.kernel_cnt[function_name]-1)}" if Kernel.kernel_cnt[function_name] > 1 else ""
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return name+colored(suffix, 'BLACK')
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def get_optimized_ast(self) -> Tuple[LazyOp, ...]:
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# set the shapetrackers to the optimized ones, fixup reduceop
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# transformed to the final LazyOp
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@functools.lru_cache(None)
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def fixup_ast(op:LazyOp, apply_to_st=None) -> LazyOp:
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if op.op in BufferOps:
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idx = self.bufs.index(op.arg)
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arg = replace(op.arg, st=self.sts[idx] if apply_to_st is None else apply_to_st(self.sts[idx]))
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elif op.op in ReduceOps:
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arg = tuple(i for i in range(self.first_reduce+self.group_for_reduces, self.shape_len) if self.full_shape[i] != self.sts[0].shape[i])
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if op in self.bufs_for_tensor_core and (tc := self.tensor_core):
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rsrc = op.src[0]
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if rsrc.op is UnaryOps.CAST: rsrc = rsrc.src[0]
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assert rsrc.op is BinaryOps.MUL
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def fix_st(warp_dims, tcd_dims, tcd_expand, pattern_1, pattern_2, st1):
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wd = self.global_dims
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tcd = self.shape_len-self.upcasted
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assert st1.shape[wd:wd+len(warp_dims)] == warp_dims, "warp dims wrong"
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assert st1.shape[tcd:tcd+len(tcd_dims)] == tcd_dims, "tcd dims wrong"
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new_shape = st1.shape[:tcd] + tcd_expand + st1.shape[tcd+len(tcd_dims):] # expand the tcd
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permaxis = list(range(wd))
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for x,y in pattern_1: permaxis.append(y + (wd if x == 0 else tcd))
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permaxis += list(range(wd+len(warp_dims), tcd))
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for x,y in pattern_2: permaxis.append(y + (wd if x == 0 else tcd))
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permaxis += list(range(tcd+len(tcd_expand), self.shape_len+len(tcd_expand)-len(tcd_dims)))
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return st1.reshape(new_shape).simplify().permute(tuple(permaxis)).reshape(st1.shape)
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if self.opts.device == "AMD":
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reduce_axes = [self.shape_len-self.upcasted]
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upcast_axis = (self.shape_len-self.upcasted, self.shape_len-self.upcasted, self.shape_len-self.upcasted+1)
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fix_st1 = functools.partial(fix_st, (8,2,2), (16,8), (16,2,4), ((1,2), (0,2), (1,1), (0,1)), ((1,0), (0,0)))
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fix_st2 = None
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elif self.opts.device == "METAL":
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reduce_axes = [self.shape_len-self.upcasted]
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upcast_axis = (self.shape_len-self.upcasted+1, self.shape_len-self.upcasted+1, self.shape_len-self.upcasted+1)
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fix_st1 = functools.partial(fix_st, (2,4,2,2), (8,2), (2,2,2,2), ((1,1), (0,1), (1,0), (0,3)), ((0,0), (0,2), (1,3), (1,2)))
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fix_st2 = functools.partial(fix_st, (2,4,2,2), (8,2), (2,2,2,2), ((0,0), (1,1), (1,2), (0,2), (1,0)), ((0,1), (0,3), (1,3)))
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elif self.opts.device in {"CUDA", "NV"}:
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reduce_axes = [self.shape_len-self.upcasted, self.shape_len-self.upcasted+1]
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upcast_axis = (self.shape_len-self.upcasted, self.shape_len-self.upcasted+2, self.shape_len-self.upcasted+2)
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# https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-matrix-fragment-mma-16816-float
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fix_st1 = functools.partial(fix_st, (2,2,2,2,2), (8,2,4), (2,2,2,2,2,2),
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((1,1), (1,0), (0,2), (0,3), (0,4)), ((1,3), (1,4), (1,2), (0,0), (0,1), (1,5)))
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fix_st2 = functools.partial(fix_st, (2,2,2,2,2), (8,2,4), (2,2,2,2,2,2),
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((1,1), (1,0), (1,5), (0,0), (0,1)), ((0,4), (0,2), (1,4), (0,3), (1,3), (1,2)))
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else:
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raise RuntimeError("unsupported device for tensor cores")
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assert apply_to_st is None, "double tensor core? not supported"
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wmma_sz = [prod(l) for l in tc.thread_local_sizes]
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wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, tuple(wmma_sz), self.opts.device, upcast_axis, tuple(reduce_axes))
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ret = LazyOp(ReduceOps.WMMA, (fixup_ast(rsrc.src[0], fix_st1), fixup_ast(rsrc.src[1], fix_st2)), wmma_arg)
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new_reduce_axes = tuple(i for i in arg if i not in reduce_axes)
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return LazyOp(op.op, (ret,), new_reduce_axes) if len(new_reduce_axes) else ret
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if self.group_for_reduces:
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start = LazyOp(op.op, tuple(fixup_ast(x) for x in op.src), arg)
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sts = ShapeTracker.from_shape(tuple([1] * self.global_dims + list(self.full_shape[self.global_dims:self.global_dims+self.local_dims+self.group_for_reduces]) + [1] * (self.shape_len - self.upcasted - self.group_for_reduces - self.first_reduce) + [x[0] for x in self.upcasted_axis(0)])) # noqa: E501
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local_buffer = MemBuffer(-1, start.dtype, sts)
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local_store = LazyOp(BufferOps.STORE, (start,), local_buffer)
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local_load = LazyOp(BufferOps.LOAD, (local_store,), local_buffer)
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return LazyOp(op.op, (local_load,), tuple(range(self.first_reduce, self.first_reduce+self.group_for_reduces)))
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else:
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arg = op.arg
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return LazyOp(op.op, tuple(fixup_ast(x) for x in op.src), arg)
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return tuple(fixup_ast(x) for x in self.ast)
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@@ -1,15 +1,13 @@
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from __future__ import annotations
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from typing import List, Tuple, cast, Optional, Any, Dict, Final, DefaultDict
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from typing import List, Tuple, cast, Optional, Any, Dict
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import functools
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from dataclasses import replace
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from collections import defaultdict
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from tinygrad.codegen.kernel import LocalBuffer, Kernel
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from tinygrad.codegen.kernel import Kernel
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.dtype import dtypes, PtrDType, ImageDType, DType
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from tinygrad.ops import BufferOps, LazyOp, TernaryOps, ReduceOps, UnaryOps, MemBuffer, BinaryOps, get_lazyop_info
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from tinygrad.ops import BufferOps, LazyOp, TernaryOps, ReduceOps, UnaryOps, get_lazyop_info
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from tinygrad.codegen.uops import UOp, UOpGraph, UOps
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from tinygrad.renderer import Program
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from tinygrad.helpers import to_function_name, colored, DEBUG, getenv, prod
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from tinygrad.helpers import to_function_name, DEBUG, getenv, prod
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# TODO: this needs to be replaced, there shouldn't be variables in the shapetracker
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def variable_to_uop(x, ctx=None) -> UOp:
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@@ -59,10 +57,8 @@ class Lowerer(Kernel):
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if x.op is BufferOps.CONST:
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dtype = x.arg.dtype.base if isinstance(x.arg.dtype, ImageDType) else x.arg.dtype
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return UOp.alu(TernaryOps.WHERE, valid, UOp.const(dtype, x.arg.val), UOp.const(dtype, 0))
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if isinstance(self.bufs[x.arg.idx], LocalBuffer):
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# TODO: this should come from somewhere else
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lb = self.bufs[x.arg.idx]
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buf = UOp(UOps.DEFINE_LOCAL, PtrDType(lb.dtype), (), (lb.name, lb.size))
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if x.arg.idx == -1:
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buf = UOp(UOps.DEFINE_LOCAL, PtrDType(x.arg.dtype.base if isinstance(x.arg.dtype, ImageDType) else x.arg.dtype), (), ("temp", x.arg.st.size))
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else:
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buf = UOp(UOps.DEFINE_GLOBAL, x.arg.dtype if isinstance(x.arg.dtype, ImageDType) else PtrDType(x.arg.dtype), (),
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(x.arg.idx, any(x.arg.idx == y.idx for y in self.outbufs)))
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@@ -85,106 +81,16 @@ class Lowerer(Kernel):
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UOp(UOps.CONTRACT, dtype=cast(DType, in_uops[1].dtype).vec(wmma_sz[1]), src=(in_uops[1],), arg=(upcast_axis[1],)),
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UOp.const(dtype.vec(wmma_sz[2]), 0.0)), arg=x.arg)
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return UOp(UOps.EXPAND, dtype, tuple(UOp(UOps.GEP, dtype, (ret,), i) for i in range(wmma_sz[2])), arg=upcast_axis[2])
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src = (in_uops[0],) + tuple(self.ridxs[i] for i in x.arg)
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return UOp(UOps.REDUCE, dtype, src, x.op)
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return UOp(UOps.REDUCE, dtype, (in_uops[0],) + tuple(self.ridxs[i] for i in x.arg), x.op)
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return UOp.alu(x.op, *in_uops)
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kernel_cnt: Final[DefaultDict[str, int]] = defaultdict(int)
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def linearize(self) -> Lowerer:
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sts_backup, bufs_backup = self.sts, self.bufs
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self.uop_cache: Dict[LazyOp, UOp] = {}
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# kernel name (before late upcast)
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self.name = ("r" if self.reduceop else ("C" if all(x.op in BufferOps for x in self.lazyops) else "E")) + \
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(f"{len(self.outbufs)}_" if len(self.outbufs) > 1 else "_") + \
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colored('_', 'BLACK').join([colored(str(x), c) for x,c in zip(self.full_shape, self.colors())])
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if DEBUG >= 4: print(self.name)
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# name the function something unique
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Lowerer.kernel_cnt[(function_name := to_function_name(self.name))] += 1
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suffix = f"{'n'+str(Lowerer.kernel_cnt[function_name]-1)}" if Lowerer.kernel_cnt[function_name] > 1 else ""
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self.name = self.name+colored(suffix, 'BLACK')
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self.idxs = []
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# add a local buffer for multistage reduce.
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if self.group_for_reduces:
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for i in range(len(self.reduceops)):
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# TODO: the strides of this can be controlled
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self.sts.append(ShapeTracker.from_shape(tuple([1] * self.global_dims + list(self.full_shape[self.global_dims:self.global_dims+self.local_dims+self.group_for_reduces]) + [1] * (self.shape_len - self.upcasted - self.group_for_reduces - self.first_reduce) + [x[0] for x in self.upcasted_axis(0)]))) # noqa: E501
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temp_dtype = cast(LazyOp, self.reduceop).dtype
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self.bufs.append(LocalBuffer(f"temp{i if len(self.reduceops) > 1 else ''}", self.sts[-1].size,
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temp_dtype.base if isinstance(temp_dtype, ImageDType) else temp_dtype))
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# set the shapetrackers to the optimized ones, fixup reduceop
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# transformed to the final LazyOp
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@functools.lru_cache(None)
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def fixup_ast(op:LazyOp, apply_to_st=None) -> LazyOp:
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if op.op in BufferOps:
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idx = self.bufs.index(op.arg)
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arg = replace(op.arg, st=self.sts[idx] if apply_to_st is None else apply_to_st(self.sts[idx]))
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elif op.op in ReduceOps:
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arg = tuple(i for i in range(self.first_reduce+self.group_for_reduces, self.shape_len) if self.full_shape[i] != self.sts[0].shape[i])
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if op in self.bufs_for_tensor_core and (tc := self.tensor_core):
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rsrc = op.src[0]
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if rsrc.op is UnaryOps.CAST: rsrc = rsrc.src[0]
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assert rsrc.op is BinaryOps.MUL
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def fix_st(warp_dims, tcd_dims, tcd_expand, pattern_1, pattern_2, st1):
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wd = self.global_dims
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tcd = self.shape_len-self.upcasted
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assert st1.shape[wd:wd+len(warp_dims)] == warp_dims, "warp dims wrong"
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assert st1.shape[tcd:tcd+len(tcd_dims)] == tcd_dims, "tcd dims wrong"
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new_shape = st1.shape[:tcd] + tcd_expand + st1.shape[tcd+len(tcd_dims):] # expand the tcd
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permaxis = list(range(wd))
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for x,y in pattern_1: permaxis.append(y + (wd if x == 0 else tcd))
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permaxis += list(range(wd+len(warp_dims), tcd))
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for x,y in pattern_2: permaxis.append(y + (wd if x == 0 else tcd))
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permaxis += list(range(tcd+len(tcd_expand), self.shape_len+len(tcd_expand)-len(tcd_dims)))
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return st1.reshape(new_shape).simplify().permute(tuple(permaxis)).reshape(st1.shape)
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if self.opts.device == "AMD":
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reduce_axes = [self.shape_len-self.upcasted]
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upcast_axis = (self.shape_len-self.upcasted, self.shape_len-self.upcasted, self.shape_len-self.upcasted+1)
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fix_st1 = functools.partial(fix_st, (8,2,2), (16,8), (16,2,4), ((1,2), (0,2), (1,1), (0,1)), ((1,0), (0,0)))
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fix_st2 = None
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elif self.opts.device == "METAL":
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reduce_axes = [self.shape_len-self.upcasted]
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upcast_axis = (self.shape_len-self.upcasted+1, self.shape_len-self.upcasted+1, self.shape_len-self.upcasted+1)
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fix_st1 = functools.partial(fix_st, (2,4,2,2), (8,2), (2,2,2,2), ((1,1), (0,1), (1,0), (0,3)), ((0,0), (0,2), (1,3), (1,2)))
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fix_st2 = functools.partial(fix_st, (2,4,2,2), (8,2), (2,2,2,2), ((0,0), (1,1), (1,2), (0,2), (1,0)), ((0,1), (0,3), (1,3)))
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elif self.opts.device in {"CUDA", "NV"}:
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reduce_axes = [self.shape_len-self.upcasted, self.shape_len-self.upcasted+1]
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upcast_axis = (self.shape_len-self.upcasted, self.shape_len-self.upcasted+2, self.shape_len-self.upcasted+2)
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# https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-matrix-fragment-mma-16816-float
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fix_st1 = functools.partial(fix_st, (2,2,2,2,2), (8,2,4), (2,2,2,2,2,2),
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((1,1), (1,0), (0,2), (0,3), (0,4)), ((1,3), (1,4), (1,2), (0,0), (0,1), (1,5)))
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fix_st2 = functools.partial(fix_st, (2,2,2,2,2), (8,2,4), (2,2,2,2,2,2),
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((1,1), (1,0), (1,5), (0,0), (0,1)), ((0,4), (0,2), (1,4), (0,3), (1,3), (1,2)))
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else:
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raise RuntimeError("unsupported device for tensor cores")
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assert apply_to_st is None, "double tensor core? not supported"
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wmma_sz = [prod(l) for l in tc.thread_local_sizes]
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wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, tuple(wmma_sz), self.opts.device, upcast_axis, tuple(reduce_axes))
|
||||
ret = LazyOp(ReduceOps.WMMA, (fixup_ast(rsrc.src[0], fix_st1), fixup_ast(rsrc.src[1], fix_st2)), wmma_arg)
|
||||
new_reduce_axes = tuple(i for i in arg if i not in reduce_axes)
|
||||
return LazyOp(op.op, (ret,), new_reduce_axes) if len(new_reduce_axes) else ret
|
||||
if self.group_for_reduces:
|
||||
start = LazyOp(op.op, tuple(fixup_ast(x) for x in op.src), arg)
|
||||
local_buffer = MemBuffer(-1, start.dtype, self.sts[-1])
|
||||
local_store = LazyOp(BufferOps.STORE, (start,), local_buffer)
|
||||
local_load = LazyOp(BufferOps.LOAD, (local_store,), local_buffer)
|
||||
return LazyOp(op.op, (local_load,), tuple(range(self.first_reduce, self.first_reduce+self.group_for_reduces)))
|
||||
else:
|
||||
arg = op.arg
|
||||
return LazyOp(op.op, tuple(fixup_ast(x) for x in op.src), arg)
|
||||
modified_ast = tuple(fixup_ast(x) for x in self.ast)
|
||||
|
||||
modified_ast = self.get_optimized_ast()
|
||||
if DEBUG >= 4:
|
||||
from tinygrad.engine.graph import print_tree
|
||||
for mast in modified_ast: print_tree(mast)
|
||||
|
||||
self.idxs = []
|
||||
if self.opts.has_local:
|
||||
# define indexes
|
||||
global_idxs, loop_global_idxs = get_grouped_dims("gidx", 0, self.full_shape[:self.global_dims], 3 if self.opts.has_local else 0)
|
||||
@@ -218,10 +124,9 @@ class Lowerer(Kernel):
|
||||
for a in range(self.first_reduce, self.first_reduce+self.group_for_reduces):
|
||||
self.ridxs[a] = UOp(UOps.RANGE, dtypes.int32, (UOp.const(dtypes.int32, 0), variable_to_uop(self.full_shape[a])), (1000+a, True))
|
||||
|
||||
self.uop_cache: Dict[LazyOp, UOp] = {}
|
||||
self.uops:UOpGraph = UOpGraph([self.to_uop(x) for x in modified_ast], self.opts)
|
||||
|
||||
self.sts, self.bufs = sts_backup, bufs_backup
|
||||
|
||||
# maybe graph the uops
|
||||
if DEBUG >= 5: self.uops.print()
|
||||
if getenv("GRAPHUOPS"):
|
||||
|
||||
Reference in New Issue
Block a user