mirror of
https://github.com/firestar5683/StarPilot.git
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September 27th, 2025 Update
This commit is contained in:
James
@@ -0,0 +1,51 @@
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# 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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@@ -0,0 +1,125 @@
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import itertools
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from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps, KernelOptError, AxisType
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from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS
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from tinygrad.dtype import ImageDType
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from tinygrad.uop.ops import Ops, resolve
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def hand_coded_optimizations(k:Kernel) -> list[Opt]:
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# make a copy so it does not mutate the input
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k = k.copy()
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# should use matvec - TODO: adjust/tune based on the wide vs tall/large vs small mat
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MV_BLOCKSIZE, MV_THREADS_PER_ROW, MV_ROWS_PER_THREAD = getenv("MV_BLOCKSIZE", 4), getenv("MV_THREADS_PER_ROW", 8), getenv("MV_ROWS_PER_THREAD", 4)
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if k.opts.has_local and getenv("MV",1) != 0 and (MV_BLOCKSIZE > 1 or MV_THREADS_PER_ROW > 1 or MV_ROWS_PER_THREAD > 1) and \
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k.reduceop is not None and k.reduceop.arg[0] is Ops.ADD and len(k.full_shape) >= 2 and k.opts.has_shared and \
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(mulop:=k.reduceop.src[0]).op is Ops.MUL and mulop.src[0].op is Ops.LOAD and mulop.src[1].op is Ops.LOAD:
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st0, st1 = k.sts[k.bufs.index(mulop.src[0])], k.sts[k.bufs.index(mulop.src[1])]
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strides0, strides1 = st0.real_strides(), st1.real_strides()
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def has_expanded_axis(shape, strides): return any(resolve(s > 1) and not resolve(st != 0) for s,st in zip(shape,strides))
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if strides0[first_reduce:=(k.axes_of(AxisType.REDUCE)[0])] == 1 and \
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not (has_expanded_axis(st0.shape, strides0) and has_expanded_axis(st1.shape, strides1)):
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for global_idx in k.axes_of(AxisType.GLOBAL):
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if k.full_shape[first_reduce]%MV_THREADS_PER_ROW == 0 and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
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if DEBUG >= 3:
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print(f"MATVEC: {k.full_shape=} {first_reduce=} {strides0=} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
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if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
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if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
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if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
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return k.applied_opts
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# are we grouping? (requires local shape support)
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if resolve(prod(k.output_shape[i] for i in k.upcastable_dims) <= 2048, False):
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for sz in [16]:
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try:
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k.apply_opt(Opt(OptOps.GROUPTOP, 0, sz))
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break
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except KernelOptError: pass
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# upcast float4 images
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for buf_index,buf in enumerate(k.bufs):
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if isinstance(buf.src[0].dtype, ImageDType):
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if (unit_stride_axes_mul_4 := [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]):
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if (axis:=unit_stride_axes_mul_4[0]) in k.upcastable_dims:
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k.apply_opt(Opt(OptOps.UPCAST, axis, 4))
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elif axis in k.unrollable_dims:
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k.apply_opt(Opt(OptOps.UNROLL, k.unrollable_dims.index(axis), 4))
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# no more opt if we are grouping
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if k.group_for_reduces: return k.applied_opts
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# **** below this line need to be optional and benchmarked ****
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# if there are small dims with lots of valid masks, upcast them (they might be from Tensor.stack)
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to_upcast: list[int] = []
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# upcast leading axes first (hack-ish for winograd; we actually want to upcast masked axes with low stride first)
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for axis in k.upcastable_dims:
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if k.full_shape[axis] <= 7 and any(st.axis_is_masked(axis) for st in k.sts) and \
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prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
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if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
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to_upcast.append(axis)
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for axis in to_upcast[::-1]: k.apply_opt(Opt(OptOps.UPCAST, axis, 0))
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# potentially do more upcasts of non reduce axes based on a heuristic
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is_dsp = k.opts is not None and k.opts.device == "DSP"
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upcasted_axis: set[int] = set()
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while resolve(prod(k.output_shape[i] for i in k.upcastable_dims) >= 1024):
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xb_choices = []
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# consider all upcastable axes with 3 or 4 upcast (128 on the DSP)
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for axis, upcast_amount in itertools.product(k.upcastable_dims, ([128] if not len(upcasted_axis) else []) if is_dsp else [3,4]):
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# if we haven't upcasted it, it mods, and buffer has stride 0 on axis while having no stride 0 in the upcasted axis already
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if axis in upcasted_axis or k.full_shape[axis]%upcast_amount != 0: continue
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if any(st.views[-1].strides[axis] == 0 and \
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all(x != 0 for t,x in zip(k.axis_types, st.real_strides()) if t in (AxisType.UPCAST, AxisType.UNROLL)) for st in k.sts):
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xb_choices.append((sum(st.views[-1].strides[axis]>0 for st in k.sts),
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sum(st.views[-1].strides[axis] for st in k.sts), axis, upcast_amount))
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if xb_choices:
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xb_choices = sorted(xb_choices)
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if DEBUG >= 4: print(f"more upcast axis : {xb_choices}")
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k.apply_opt(Opt(OptOps.UPCAST, xb_choices[0][2], xb_choices[0][3]))
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upcasted_axis.add(xb_choices[0][2])
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else: break
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# if last reduce dim is small(ish), loop unroll the reduce
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# NOTE: this can fail on multireduce with mismatching dimensions, this is okay
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try:
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upcast_size = prod(k.full_shape[a] for a in k.axes_of(AxisType.UPCAST, AxisType.UNROLL))
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if k.unrollable_dims and (upcast_size <= 4 or not k.axes_of(AxisType.UNROLL)) and (upcast_size < 64):
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if (s:=k.full_shape[k.unrollable_dims[-1]]) <= 32:
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k.apply_opt(Opt(OptOps.UNROLL, len(k.unrollable_dims)-1, 0))
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# if it's small, upcast a second reduce dimension too
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if k.unrollable_dims and s <= 3 and k.full_shape[k.unrollable_dims[-1]] <= 3:
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k.apply_opt(Opt(OptOps.UNROLL, len(k.unrollable_dims)-1, 0))
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else:
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for splits in [4]:
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if k.full_shape[axis:=k.unrollable_dims[-1]]%splits == 0:
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k.apply_opt(Opt(OptOps.UNROLL, len(k.unrollable_dims)-1, splits))
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break
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except KernelOptError: pass
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# if nothing at all is upcasted and it's easy to, do an upcast
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for splits in [4]:
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# TODO: somehow this never hits a reduce
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if not k.upcasted and k.upcastable_dims and k.full_shape[k.upcastable_dims[-1]] % splits == 0:
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k.apply_opt(Opt(OptOps.UPCAST, k.upcastable_dims[-1], splits))
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# **** local groups ****
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if k.opts.has_local:
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if NOLOCALS:
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k.apply_opt(Opt(OptOps.NOLOCALS))
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else:
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# prioritize making expand axes local
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local_axis_ranking = [(any(st.views[-1].strides[axis] == 0 for st in k.sts), axis) for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP)]
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to_local: list[tuple[int, int]] = []
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for _, axis in sorted(local_axis_ranking, key=lambda x: (-x[0], -x[1])):
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local_size = prod(sz for _, sz in to_local)
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local_sz: int|None = next((x for x in ([32] * (axis == 0) + [16,8,4,3,2]) if k.full_shape[axis] % x == 0 and local_size * x <= 128), None)
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if local_sz is not None: to_local.append((axis, local_sz))
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deleted_shape = 0
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for axis, local_sz in sorted(to_local[:3]):
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axis = axis - deleted_shape
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will_delete_shape = local_sz == k.full_shape[axis]
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k.apply_opt(Opt(OptOps.LOCAL, axis, local_sz))
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if will_delete_shape: deleted_shape += 1
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return k.applied_opts
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@@ -0,0 +1,496 @@
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from __future__ import annotations
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import itertools, functools, math
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from dataclasses import dataclass
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from collections import defaultdict
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from typing import cast, Final, Callable, Sequence
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from enum import Enum, auto
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from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType
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from tinygrad.uop.spec import type_verify, ast_spec
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from tinygrad.device import Device
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from tinygrad.codegen.opt.tc import TensorCore
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from tinygrad.renderer import Renderer
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from tinygrad.dtype import ImageDType
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from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, TC_SELECT, TC_OPT, AMX
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.view import strides_for_shape, get_contraction
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from tinygrad.codegen.opt.swizzler import view_left, view_left_through_load
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class OptOps(Enum):
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TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
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GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
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def __lt__(self, x:OptOps): return self.value < x.value
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@dataclass(frozen=True, order=True)
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class Opt:
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op: OptOps
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axis: int|None = None
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arg: int|tuple|None = None
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def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
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axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.LOOP: "L", AxisType.UPCAST: "u",
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AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
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axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow",
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AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
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class KernelOptError(Exception): pass
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def check(cond:bool, msg:str=""):
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if not cond: raise KernelOptError(msg)
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@dataclass
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class TensorCoreOptions:
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axes: tuple[int, ...] # the location of the original N and M axes if still in the shape
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axes_exist: tuple[bool, ...] # true if the original N and M axes are still in the shape
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axis_pads: tuple[tuple[int, int], ...]
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def fix_axes(self, removed_axis:int): # adjust the TC axes if necessary when a dimension is removed
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axes, axes_exist = list(self.axes), list(self.axes_exist)
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for tc_dim in [i for i in range(2) if axes_exist[i]]:
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if removed_axis < axes[tc_dim]: axes[tc_dim] -= 1
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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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class Kernel:
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def __init__(self, ast:UOp, opts:Renderer|None=None):
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assert ast.op is Ops.SINK, ast.op
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self.ast = ast
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self.opts = opts if opts is not None else Device[Device.DEFAULT].renderer
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# verify AST matches the spec
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if __debug__: type_verify(list(self.ast.toposort()), ast_spec)
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self.vars: list[Variable] = self.ast.variables()
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# NOTE: this requires a specific order with the [::-1], this is likely a bug
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self.bufs: list[UOp] = [x for x in self.ast.toposort() if x.op in GroupOp.Buffer and x.st is not None][::-1]
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# create new shapetrackers inside this kernel, we will permute them
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self.sts: list[ShapeTracker] = [x.st_arg for x in self.bufs]
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# add the shapetrackers for each reduce
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# we use this to track which axes are reduced in each reduce
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self.reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE_AXIS]
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for x in self.reduceops:
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self.sts.append(unwrap(x.st))
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self.sts.append(unwrap(x.src[0].st))
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# add a shapetracker to the end to track the full shape, with 0 strides so it can merge
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full_shape = ast.full_shape
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self.sts.append(ShapeTracker.from_shape(full_shape, (0,)*len(full_shape)))
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# parameters for optimization
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self.tensor_core: TensorCore|None = None
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self.tensor_core_opts: TensorCoreOptions|None = None
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self.use_tensor_cores: int = 0
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self.applied_opts: list[Opt] = []
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self.dont_use_locals = False
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self.finalized: bool = False
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# group simplifies
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self.simplify_ones()
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self.simplify_merge_adjacent()
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# axis types
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global_loops = AxisType.GLOBAL if self.opts.has_local else AxisType.LOOP
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self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.output_shape, self.full_shape)]
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# confirm all reduce axes are at the end
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if (final_reduces := [x for x in self.axis_types if x == AxisType.REDUCE]) and final_reduces != self.axis_types[-len(final_reduces):]:
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raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}")
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def copy(self):
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ret = type(self).__new__(type(self))
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# base linearizer params
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ret.opts, ret.ast = self.opts, self.ast
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# things downstream of the AST
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ret.reduceops, ret.vars, ret.bufs = self.reduceops, self.vars, self.bufs
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ret.sts = self.sts[:]
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ret.axis_types = self.axis_types[:]
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# parameters for optimizations
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ret.applied_opts, ret.dont_use_locals = self.applied_opts[:], self.dont_use_locals
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ret.tensor_core, ret.tensor_core_opts, ret.use_tensor_cores = self.tensor_core, self.tensor_core_opts, self.use_tensor_cores
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ret.finalized = self.finalized
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return ret
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@property
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def reduceop(self) -> UOp|None: return self.reduceops[0] if len(self.reduceops) > 0 else None
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@property
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def full_shape(self) -> tuple[sint, ...]: return self.sts[-1].shape
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@property
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def output_shape(self) -> tuple[sint, ...]: return self.sts[0].shape
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@property
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def shape_len(self) -> int: return len(self.full_shape)
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def axes_of(self, *axis_type:AxisType) -> list[int]: return [i for i,t in enumerate(self.axis_types) if t in argfix(axis_type)]
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@property
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def upcasted(self) -> int: return len(self.axes_of(AxisType.UPCAST, AxisType.UNROLL))
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@property
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def group_for_reduces(self) -> int: return len(self.axes_of(AxisType.GROUP_REDUCE))
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# heuristic helpers
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@property
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def upcastable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP) \
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if isinstance(s:=self.full_shape[i], int) and s > 1]
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@property
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def unrollable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE) \
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if isinstance(s:=self.full_shape[i], int) and s > 1]
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# ******************** colors and names ********************
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def colors(self) -> list[str]:
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assert len(self.axis_types) == self.shape_len, "colors size mismatch"
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return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
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def colored_shape(self, pad:int|None=None, dense=False) -> str:
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shape_strs = [(s if dense else f"{s:4d}") if isinstance(s, int) else s.render() for s in self.full_shape]
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ret = ' '.join(colored(s, color) for s,color in zip(shape_strs, self.colors()))
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if pad: ret += ' '*(pad-ansilen(ret))
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return ret
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kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int)
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||||
@functools.cached_property
|
||||
def name(self) -> str:
|
||||
# kernel name (before late upcast)
|
||||
kernel_type = "r" if self.reduceop is not None else ("C" if all(x.op is Ops.SINK or x.op in GroupOp.Buffer for x in self.ast.toposort()) else "E")
|
||||
suffix = colored('_', 'BLACK').join([colored(x.render() if isinstance(x, UOp) else str(x), c) for x,c in zip(self.full_shape, self.colors())])
|
||||
name = kernel_type + (f"{len(self.ast.src)}" if len(self.ast.src) > 1 else "") + "_" + suffix
|
||||
|
||||
# name the function something unique
|
||||
Kernel.kernel_cnt[(function_name := to_function_name(name))] += 1
|
||||
num = f"n{Kernel.kernel_cnt[function_name]-1}" if Kernel.kernel_cnt[function_name] > 1 else ""
|
||||
return name + colored(num, 'BLACK')
|
||||
|
||||
# ******************** base simplifiers ********************
|
||||
|
||||
# apply reshape and permute to all shapetrackers
|
||||
def reshape(self, new_shape_fxn:Callable[[tuple[sint, ...]], Sequence[sint]]):
|
||||
self.sts = [st.reshape(tuple(new_shape_fxn(st.shape))) for st in self.sts]
|
||||
def permute(self, new_axes:Sequence[int]): self.sts = [st.permute(tuple(new_axes)) for st in self.sts]
|
||||
|
||||
# axis : the axis to pull from
|
||||
# amount : the amount to take
|
||||
# top : if you want to pull that amount from the top
|
||||
# insert_at : place to insert the new stuff
|
||||
def shift_to(self, axis:int, amount:int, new_type:AxisType, top:bool=False, insert_at:int|None=None) -> int:
|
||||
if insert_at is None: insert_at = self.shape_len
|
||||
self.axis_types.insert(insert_at, new_type)
|
||||
move_axis = axis if top else axis+1
|
||||
if move_axis < insert_at: insert_at += 1
|
||||
def new_shape_fxn(x): return x[0:axis] + (((amount,x[axis]//amount) if top else (x[axis]//amount,amount)) if x[axis] > 1 else (1,1)) + x[axis+1:]
|
||||
new_axes = [i for i in range(insert_at) if i != move_axis]+[move_axis]+[i for i in range(insert_at, self.shape_len+1) if i != move_axis]
|
||||
self.reshape(new_shape_fxn)
|
||||
self.permute(new_axes)
|
||||
return insert_at
|
||||
|
||||
# ******************** complex simplifiers ********************
|
||||
|
||||
def simplify_ones(self) -> bool:
|
||||
# remove places where the shape is all ones
|
||||
if any(all_ones:=[s==1 for s in self.full_shape]):
|
||||
if hasattr(self, 'axis_types'):
|
||||
self.axis_types = [x for i,x in enumerate(self.axis_types) if not all_ones[i]]
|
||||
self.reshape(lambda shape: [x for i,x in enumerate(shape) if not all_ones[i]])
|
||||
return True
|
||||
return False
|
||||
|
||||
def simplify_merge_adjacent(self):
|
||||
assert not hasattr(self, 'axis_types'), "don't call this after init"
|
||||
if self.shape_len == 0: return
|
||||
shapes, strides = [x.shape for x in self.sts], [x.real_strides() for x in self.sts]
|
||||
# NOTE: we can't use self.first_reduce yet
|
||||
first_reduce = [resolve(x!=y) for x,y in zip(self.output_shape+(0,), self.full_shape+(1,))].index(True)
|
||||
|
||||
# if it's an image, insert fake strides such that this fusion doesn't happen across image axes
|
||||
# TODO: remove membufs
|
||||
membufs = dedup([x.src[0].base for x in self.bufs if x.op in {Ops.LOAD, Ops.STORE}])
|
||||
if isinstance(membufs[0].base.dtype, ImageDType):
|
||||
base_shape = membufs[0].base.dtype.shape
|
||||
if shape_idx_groups := get_contraction(self.output_shape, base_shape):
|
||||
special_strides: tuple[sint, ...] = tuple()
|
||||
for i,g in enumerate(shape_idx_groups):
|
||||
shape_piece = tuple(self.output_shape[x] for x in g)
|
||||
assert prod(shape_piece) == base_shape[i], f"get_contraction was wrong? {shape_piece} != {base_shape[i]}"
|
||||
special_strides += strides_for_shape(shape_piece)
|
||||
# adding the fake image shape
|
||||
shapes.append(self.output_shape)
|
||||
strides.append(special_strides)
|
||||
|
||||
# merge dimensions if we can, multi _merge_dims
|
||||
# NOTE: this does not always preserve the reduce dimension
|
||||
# TODO: move this into shapetracker, with tests!
|
||||
# TODO: how does this work with multi-reduce?
|
||||
rets = [[(s[0], st[0])] for s,st in zip(shapes, strides)]
|
||||
for i in range(1, len(shapes[0])):
|
||||
can_merge = []
|
||||
for s,st,ret in zip(shapes, strides, rets):
|
||||
# TODO: added the always mergeability of 1s, is this right? if so, add to shapetracker in the 1 case
|
||||
si, sti, last_st = s[i], st[i], ret[-1][1]
|
||||
can_merge.append((sti is not None) and ((sti != 0 and last_st == si*sti) or (sti == 0 and last_st == 0)))
|
||||
# more can merge than this
|
||||
mergeable = all(can_merge) and i != first_reduce
|
||||
for j,(s,st) in enumerate(zip(shapes, strides)):
|
||||
if mergeable: rets[j][-1] = (rets[j][-1][0] * s[i], st[i])
|
||||
else: rets[j].append((s[i], st[i]))
|
||||
|
||||
# do the reshapes
|
||||
for i,x in enumerate(rets[:len(self.sts)]): self.sts[i] = self.sts[i].reshape(tuple([y[0] for y in x]))
|
||||
|
||||
# ******************** apply optimizations ********************
|
||||
|
||||
def real_axis(self, op:OptOps, axis:int|None):
|
||||
try:
|
||||
if axis is None: return -1
|
||||
if op is OptOps.UNROLL: return self.unrollable_dims[axis]
|
||||
if op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.axes_of(AxisType.REDUCE)[axis]
|
||||
check(axis < self.shape_len, f"invalid axis on {axis=} {op=} {self.shape_len=}")
|
||||
return axis
|
||||
except IndexError as e: raise KernelOptError from e
|
||||
|
||||
def apply_opt(self, opt:Opt, append_opt:bool=True) -> int|None:
|
||||
if self.finalized: raise RuntimeError("can't optimize Kernel after it's finalized")
|
||||
if self.dont_use_locals: check(opt.op not in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}, "not using locals")
|
||||
|
||||
if opt.op is OptOps.TC:
|
||||
check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: things like PADTO might be fine
|
||||
check(len(self.opts.tensor_cores) > 0, "must have tensor cores")
|
||||
check(opt.axis is not None, "tensor core opts must have an axis")
|
||||
check(opt.arg is not None and isinstance(opt.arg, tuple) and len(opt.arg) == 3, "tensor core opts must have valid arg")
|
||||
check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select")
|
||||
check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt")
|
||||
check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid")
|
||||
check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available")
|
||||
self.applied_opts.append(opt)
|
||||
return None
|
||||
|
||||
axis = self.real_axis(opt.op, opt.axis)
|
||||
|
||||
if opt.op is OptOps.SWAP: amt = self.real_axis(opt.op, cast(int, opt.arg)) # arg is an axis in the SWAPs
|
||||
elif opt.arg is not None:
|
||||
check(isinstance(opt.arg, int), "arg should be int")
|
||||
amt = arg if (arg:=cast(int, opt.arg)) != 0 else self.full_shape[axis]
|
||||
check(isinstance(amt, int) and amt != 1, f"shift/padto of {amt=}, 1 or symbolic amount is meaningless")
|
||||
if opt.op is not OptOps.PADTO:
|
||||
# we check both the full_shape and each shape
|
||||
check(self.full_shape[axis] % amt == 0, f"no longer valid shift {self.full_shape[axis]=}, {amt=}")
|
||||
for st in self.sts: check(st.shape[axis] == 1 or st.shape[axis] % amt == 0, f"no longer valid shift {st.shape[axis]=}, {amt=}")
|
||||
else: amt = -1
|
||||
|
||||
if self.reduceop is not None and (opt.op in {OptOps.GROUP, OptOps.GROUPTOP} or \
|
||||
(self.group_for_reduces and opt.op not in {OptOps.NOLOCALS, OptOps.PADTO})):
|
||||
acc_sz = self.reduceop.dtype.itemsize
|
||||
upcast_sz = prod([self.full_shape[a] for a in self.axes_of(AxisType.UPCAST)])
|
||||
local_sz = prod([self.full_shape[a] for a in self.axes_of(AxisType.LOCAL)])
|
||||
smem_sz = amt*acc_sz*upcast_sz*local_sz
|
||||
check(smem_sz <= self.opts.shared_max, f"exceeds maximum shared memory size: needs {smem_sz}, max {self.opts.shared_max}")
|
||||
|
||||
new_axis = None
|
||||
if opt.op is OptOps.LOCAL: # cyan
|
||||
# NOTE: LLVM/CPU can use locals too, but they are treated the same as globals (still helpful for L1 cache)
|
||||
# it's disabled for now since it makes BEAM slow for little gain
|
||||
check(self.opts.has_local, "target does not support local")
|
||||
check(self.axis_types[axis] is AxisType.GLOBAL, "local is for globals")
|
||||
new_axis = self.shift_to(axis, amt, AxisType.LOCAL, insert_at=max(self.axes_of(AxisType.GLOBAL, AxisType.LOCAL))+1)
|
||||
elif opt.op in {OptOps.GROUP, OptOps.GROUPTOP}: # green
|
||||
check(self.opts.has_local and self.opts.has_shared, "target does not support local or shared mem")
|
||||
check(self.axis_types[axis] is AxisType.REDUCE, "must be reduce axis to group")
|
||||
check(not self.tensor_core, "can't group with tensor cores")
|
||||
check(len(reduce_axes:=[i for r in self.reduceops for i in r.axis_arg]) == len(set(reduce_axes)), "can't group with parallel reduces")
|
||||
new_axis = self.shift_to(axis, amt, AxisType.GROUP_REDUCE, top=(opt.op is OptOps.GROUPTOP), insert_at=min(self.axes_of(AxisType.REDUCE)))
|
||||
elif opt.op is OptOps.UNROLL: # purple
|
||||
check(self.axis_types[axis] not in (AxisType.UPCAST, AxisType.UNROLL), "can't upcasted already upcasted")
|
||||
check(amt <= 32, "don't unroll more than 32")
|
||||
new_axis = self.shift_to(axis, amt, AxisType.UNROLL, insert_at=None)
|
||||
elif opt.op is OptOps.UPCAST: # yellow
|
||||
check(axis in self.upcastable_dims, f"{axis=} not in {self.upcastable_dims=}")
|
||||
# NOTE: assume the first get_local_axes() LOCAL are for TC
|
||||
check(not (self.tensor_core and axis in self.axes_of(AxisType.LOCAL)[:len(self.tensor_core.get_local_axes())]), "can't upcast TC locals")
|
||||
check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16")
|
||||
new_axis = self.shift_to(axis, amt, AxisType.UPCAST,
|
||||
insert_at=max(self.axes_of(AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP, AxisType.UPCAST))+1)
|
||||
elif opt.op is OptOps.NOLOCALS:
|
||||
check(self.opts.has_local and not self.dont_use_locals, "NOLOCALS is meaningless if target does not support local or already not using locals")
|
||||
check(AxisType.LOCAL not in self.axis_types and self.group_for_reduces == 0, "can't have no locals with locals")
|
||||
self.dont_use_locals = True
|
||||
elif opt.op is OptOps.SWAP:
|
||||
check(axis < amt, f"swap is only for axis < amt, getting {amt=}, {axis=}")
|
||||
check(self.axis_types[axis]==self.axis_types[amt]==AxisType.GLOBAL, f"swap is for globals {self.axis_types[axis]=}, {self.axis_types[amt]=}")
|
||||
permute = list(range(self.shape_len))
|
||||
permute[axis], permute[amt] = permute[amt], permute[axis]
|
||||
self.permute(tuple(permute))
|
||||
elif opt.op is OptOps.PADTO:
|
||||
check(not self.vars, "does not work with symbolic shape")
|
||||
check(self.axis_types[axis] not in (AxisType.UPCAST, AxisType.UNROLL), "cannot pad upcasted")
|
||||
# ok to pad SUM if all parent ALU ops have f(0) = 0
|
||||
if (r:=self.reduceop) is not None and self.axis_types[axis] in (AxisType.GROUP_REDUCE, AxisType.REDUCE):
|
||||
check(r.arg[0] is Ops.ADD and can_pad(r, {}), f"cannot pad {r}")
|
||||
padded = False
|
||||
for i,st in enumerate(self.sts):
|
||||
if (s:=st.shape[axis]) == 1: continue # reduced
|
||||
check(s > amt//4, f"pad adds more than quadruple the work {st.shape[axis]=} > {amt//4=}")
|
||||
if (ru := round_up(cast(int, s), amt) - s):
|
||||
# pad right seems to be faster
|
||||
self.sts[i] = st.pad(((0,0),) * axis + ((0,ru),) + ((0,0),) * (len(st.shape)-axis-1))
|
||||
padded = True
|
||||
check(padded, "nothing was padded")
|
||||
|
||||
if append_opt: self.applied_opts.append(opt)
|
||||
if self.simplify_ones() and self.tensor_core_opts:
|
||||
self.tensor_core_opts.fix_axes(axis) # fix up axes in TC opts if required after simplify_ones()
|
||||
return new_axis
|
||||
|
||||
def apply_opts(self, opts:Sequence[Opt]) -> Kernel:
|
||||
for opt in opts: self.apply_opt(opt)
|
||||
return self
|
||||
|
||||
# **** kernel outputs, mostly tensor cores ****
|
||||
|
||||
def _create_tc_opts(self, reduceop:UOp, tc:TensorCore, axis:int, opt_level:int) -> TensorCoreOptions|None:
|
||||
has_cast = tc.dtype_in != tc.dtype_out
|
||||
if has_cast and not (reduceop.src[0].op is Ops.CAST and reduceop.src[0].dtype == tc.dtype_out): return None
|
||||
|
||||
mul_op = reduceop.src[0].src[0] if has_cast else reduceop.src[0]
|
||||
if mul_op.op is not Ops.MUL: return None
|
||||
|
||||
def buf_index(src:UOp) -> int|None:
|
||||
# TODO: apply tc even if the sources are not from LOAD
|
||||
if src.op is Ops.LOAD and src.dtype == tc.dtype_in: return self.bufs.index(src)
|
||||
try:
|
||||
if opt_level >= 1 and src.op is Ops.CAST and src.dtype == tc.dtype_in: return self.bufs.index(src.src[0])
|
||||
except ValueError: return None
|
||||
return None
|
||||
if (buf0:=buf_index(mul_op.src[0])) is None or (buf1:=buf_index(mul_op.src[1])) is None: return None
|
||||
|
||||
buf0_strides, buf1_strides = self.sts[buf0].real_strides(), self.sts[buf1].real_strides()
|
||||
axis_buf0 = [(i,self.full_shape[i],buf1_strides[i]) for i in self.upcastable_dims if buf0_strides[i] == 0]
|
||||
axis_buf1 = [(i,self.full_shape[i],buf0_strides[i]) for i in self.upcastable_dims if buf1_strides[i] == 0]
|
||||
if not (axis_buf0 and axis_buf1 and (len(self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)) == 1 or (opt_level >= 1))): return None
|
||||
|
||||
axis_choices = list(itertools.product(axis_buf0, axis_buf1, self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)))
|
||||
if not (axis < len(axis_choices)): return None
|
||||
|
||||
s0, s1, s2 = axis_choices[-(axis+1)][0][0], axis_choices[-(axis+1)][1][0], axis_choices[-(axis+1)][2] # s0 is n, s1 is m, s2 is k
|
||||
axis_pads = tuple((x, tc.dims[i]) for i, x in enumerate([s0, s1, s2]) if resolve(self.full_shape[x]%tc.dims[i] != 0))
|
||||
if axis_pads and (opt_level < 2): return None
|
||||
if DEBUG >= 3: print("TENSOR CORES", axis_buf0, axis_buf1, tc)
|
||||
return TensorCoreOptions(axes=(s0, s1, s2), axes_exist=(True, True), axis_pads=axis_pads)
|
||||
|
||||
def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> bool:
|
||||
if use_tensor_cores and self.reduceop is not None and self.reduceop.arg[0] is Ops.ADD:
|
||||
tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
|
||||
for tc in tensor_cores:
|
||||
tensor_core_opts = [self._create_tc_opts(reduceop, tc, axis, opt_level) for reduceop in self.reduceops]
|
||||
if tensor_core_opts[0] is None: continue
|
||||
# can only fuse reduces with the same tc options
|
||||
assert all_same(tensor_core_opts)
|
||||
self.tensor_core_opts = tc_opts = tensor_core_opts[0]
|
||||
|
||||
# attempt to pad the tensor axes that require it
|
||||
try:
|
||||
for axis, dim in tc_opts.axis_pads: self.apply_opt(Opt(OptOps.PADTO, axis, dim), append_opt=False) # PADTO might fail
|
||||
except KernelOptError: continue
|
||||
# tensor core -- unroll the reduce dim (K), upcast and local the inner and outer dims (N, M)
|
||||
for opt in tc.opts: self.apply_opt(Opt({"u":OptOps.UPCAST, "l":OptOps.LOCAL}[opt[0]], tc_opts.axes[int(opt[1])], 2), append_opt=False)
|
||||
for dim, amt in tc.get_reduce_axes(): self.apply_opt(Opt(OptOps.UNROLL, 0, amt), append_opt=False) # TODO: this should be the reduce, not 0
|
||||
self.tensor_core = tc
|
||||
self.use_tensor_cores = use_tensor_cores # TC=2 will do the shape ops without the WMMA
|
||||
return True
|
||||
return False
|
||||
|
||||
def apply_tensor_cores(self, use_tensor_cores=1, extra_opts:list[Opt]|None=None, axis:int=0, tc_select:int|None=None, tc_opt:int|None=None) -> bool:
|
||||
""" Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
|
||||
Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).
|
||||
|
||||
Keyword arguments:
|
||||
use_tensor_cores -- controls how tensor cores are applied (default 1)
|
||||
0: will disable any tensor core matching
|
||||
1: enable tensor cores
|
||||
2: apply tensor core shape but don't use UOp.WMMA
|
||||
extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None)
|
||||
tc_select -- specifies which tensor core(s) to use for optimization (default -1)
|
||||
-1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes)
|
||||
[0-N]: uses only the n'th tensor core available; useful for search
|
||||
tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise)
|
||||
0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL
|
||||
1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
|
||||
2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
|
||||
"""
|
||||
if tc_select is None: tc_select = TC_SELECT.value
|
||||
if tc_opt is None: tc_opt = TC_OPT.value
|
||||
if not self.opts.tensor_cores: return False
|
||||
try: # check TC first and apply hand-coded opts if successful
|
||||
self.apply_opt(Opt(OptOps.TC, axis, (tc_select, tc_opt, use_tensor_cores)))
|
||||
|
||||
if (tc_opts:=self.tensor_core_opts) is not None:
|
||||
if extra_opts is not None: self.apply_opts(extra_opts)
|
||||
else:
|
||||
if AMX: return True # skip hand-coded TC opts if AMX, upcasting will make kernel slower
|
||||
# hand-coded TC opts
|
||||
for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N
|
||||
szs = [sz for sz in [5,4,3,2] if self.full_shape[tc_opts.axes[tc_dim]] % sz == 0]
|
||||
if szs: self.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0]))
|
||||
|
||||
if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if self.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N
|
||||
self.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0]))
|
||||
return True
|
||||
except KernelOptError:
|
||||
return False
|
||||
|
||||
# strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
|
||||
def shape_str(self) -> list[str]:
|
||||
ret: list[str] = []
|
||||
cnt: dict[AxisType, int] = {}
|
||||
for x in self.axis_types:
|
||||
cnt[x] = (cnt[x] + 1) if x in cnt else 0
|
||||
ret.append(f"{axis_letters[x]}{cnt[x]}")
|
||||
return ret
|
||||
def shape_str_to_axis(self, nms:list[str]) -> tuple[int, ...]: return tuple([self.shape_str().index(x) for x in nms])
|
||||
|
||||
def get_optimized_ast(self, name_override:str|None=None) -> UOp:
|
||||
@functools.cache
|
||||
def fixup_ast(op:UOp) -> UOp:
|
||||
ret = op.replace(src=tuple(fixup_ast(x) for x in op.src)) # noqa: F821
|
||||
if op.op in GroupOp.Buffer and op in self.bufs:
|
||||
st = self.sts[self.bufs.index(op)]
|
||||
# replace the VIEW source
|
||||
return ret.replace(src=(ret.src[0].replace(arg=st),)+ret.src[1:])
|
||||
if op.op is Ops.SINK:
|
||||
# NOTE: should group_for_reduces be added to the local_dims?
|
||||
# TODO: arg.name should be able to be None
|
||||
kernel_name = ret.arg.name if ret.arg is not None and ret.arg.name != "test" else self.name if name_override is None else name_override
|
||||
return ret.replace(arg=KernelInfo(kernel_name, tuple(self.axis_types), self.dont_use_locals, tuple(self.applied_opts)))
|
||||
if op.op is Ops.REDUCE_AXIS:
|
||||
reduce_idx = len(self.bufs) + self.reduceops.index(op) * 2
|
||||
changed = tuple(i for i in range(self.shape_len) if resolve(self.sts[reduce_idx].shape[i] != self.sts[reduce_idx + 1].shape[i]))
|
||||
axes = tuple(i for i in self.axes_of(AxisType.REDUCE, AxisType.GROUP_REDUCE, AxisType.UNROLL) if i in changed)
|
||||
if (tc := self.tensor_core) and self.use_tensor_cores == 1:
|
||||
# get reduce/upcast axes for the tensor cores
|
||||
tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))])
|
||||
base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis(tc.base_upcast_axes())])
|
||||
tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
|
||||
|
||||
# permute the srcs
|
||||
srcs = list((ret.src[0] if ret.src[0].op is not Ops.CAST else ret.src[0].src[0]).src)
|
||||
for i, (src, permaxis) in enumerate(zip(srcs, tc.permutes_for_shape_str(self.shape_str()))):
|
||||
src_st = (src if src.op is Ops.LOAD else src.src[0]).st_arg
|
||||
srcs[i] = src.view(ShapeTracker.from_shape(src_st.shape).permute(permaxis))
|
||||
|
||||
# construct the op
|
||||
wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, self.opts.device, tc.threads, tc_upcast_axes, tc_reduce_axes)
|
||||
wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=(
|
||||
UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0]),
|
||||
UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1]),
|
||||
UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg)
|
||||
tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2])
|
||||
|
||||
# preserve any other reduce
|
||||
return ret.replace(src=(tc_uop,), arg=(Ops.ADD, new_axes)) if (new_axes := tuple(i for i in axes if i not in tc_reduce_axes)) else tc_uop
|
||||
|
||||
ret = ret.replace(arg = (op.arg[0], axes))
|
||||
return ret
|
||||
self.finalized = True
|
||||
fixed_ast = fixup_ast(self.ast)
|
||||
del fixup_ast
|
||||
return graph_rewrite(fixed_ast, view_left+view_left_through_load, name="fixup optimized AST")
|
||||
@@ -0,0 +1,18 @@
|
||||
from dataclasses import replace
|
||||
from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo
|
||||
from tinygrad.helpers import colored
|
||||
from tinygrad.codegen.opt.kernel import axis_colors
|
||||
|
||||
def rename_sink(s:UOp):
|
||||
if s.arg is not None and s.arg.name != "test": return None
|
||||
|
||||
# get all ranges (sorted)
|
||||
rngs = sorted([u for u in s.parents if u.op is Ops.RANGE], key=lambda x: x.arg[0:-1])
|
||||
|
||||
# add name to kernel
|
||||
name = "k" + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), axis_colors[x.arg[-1]]) for x in rngs])
|
||||
return s.replace(arg=KernelInfo(name=name) if s.arg is None else replace(s.arg, name=name))
|
||||
|
||||
pm_postrange_opt = PatternMatcher([
|
||||
(UPat(Ops.SINK, name="s"), rename_sink),
|
||||
])
|
||||
@@ -0,0 +1,203 @@
|
||||
from typing import cast
|
||||
import functools, math, time, multiprocessing, traceback, signal, atexit
|
||||
from collections import defaultdict
|
||||
from dataclasses import replace
|
||||
from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType
|
||||
from tinygrad.device import Device, Buffer, Compiler
|
||||
from tinygrad.helpers import prod, flatten, DEBUG, CACHELEVEL, diskcache_get, diskcache_put, getenv, Context, colored, time_to_str
|
||||
from tinygrad.helpers import IGNORE_BEAM_CACHE, TC_SEARCH_OVER_SHAPE
|
||||
from tinygrad.dtype import ImageDType, PtrDType
|
||||
from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps, KernelOptError
|
||||
from tinygrad.tensor import Tensor
|
||||
from tinygrad.engine.realize import CompiledRunner, get_program
|
||||
from tinygrad.renderer import ProgramSpec
|
||||
|
||||
actions = [Opt(op=OptOps.UPCAST, axis=axis, arg=amt) for amt in [0,2,3,4,5,7] for axis in range(8)]
|
||||
actions += [Opt(op=OptOps.UNROLL, axis=axis, arg=amt) for amt in [0,4,7] for axis in range(5)]
|
||||
actions += [Opt(op=OptOps.LOCAL, axis=axis, arg=amt) for amt in [2,3,4,8,13,16,29] for axis in range(6)]
|
||||
actions += [Opt(op=OptOps.GROUPTOP, axis=axis, arg=amt) for amt in [13,16,28,29,32,49,64,256] for axis in range(3)]
|
||||
actions += [Opt(op=OptOps.GROUP, axis=axis, arg=amt) for amt in [0,4,8,16] for axis in range(3)]
|
||||
if getenv("BEAM_PADTO", 1): actions += [Opt(op=OptOps.PADTO, axis=axis, arg=amt) for amt in [32] for axis in range(7)]
|
||||
actions += [Opt(op=OptOps.LOCAL, axis=0, arg=32), Opt(op=OptOps.LOCAL, axis=6, arg=2)]
|
||||
actions += [Opt(op=OptOps.TC, axis=0, arg=(-1, 0, getenv("TC", 1)))]
|
||||
# covers resnet kernels (3 global * 3 reduce)
|
||||
actions += [Opt(op=OptOps.TC, axis=axis, arg=(-1, getenv("TC_OPT", 2), getenv("TC", 1))) for axis in range(9)]
|
||||
actions += [Opt(op=OptOps.SWAP, axis=axis_0, arg=axis_1) for axis_0 in range(5) for axis_1 in range(axis_0+1, 5)]
|
||||
if getenv("NOLOCALS"): actions += [Opt(op=OptOps.NOLOCALS)]
|
||||
|
||||
def get_test_global_size(global_size, max_global_size, var_vals):
|
||||
test_global_size = [sym_infer(sz, var_vals) for sz in global_size]
|
||||
input_size = prod(test_global_size)
|
||||
while prod(test_global_size) > max_global_size:
|
||||
for j in range(len(global_size)-1,-1,-1):
|
||||
if test_global_size[j] > 16:
|
||||
test_global_size[j] //= 2
|
||||
break
|
||||
return test_global_size, input_size / prod(test_global_size)
|
||||
|
||||
def _time_program(p:ProgramSpec, lib:bytes, var_vals:dict[Variable, int], rawbufs:list[Buffer], early_stop:float|None=None,
|
||||
allow_test_size:int=True, max_global_size:int|None=65536, clear_l2=False, cnt=3, name="test") -> list[float]:
|
||||
factor = 1
|
||||
if allow_test_size and p.global_size is not None and max_global_size is not None:
|
||||
global_size, factor = get_test_global_size(p.global_size, max_global_size, var_vals)
|
||||
p = replace(p, global_size=global_size)
|
||||
try: car = CompiledRunner(p, precompiled=lib)
|
||||
except AssertionError: return [math.inf] * cnt
|
||||
tms = []
|
||||
input_bufs = [rawbufs[i] for i in car.p.globals]
|
||||
for _ in range(cnt):
|
||||
if clear_l2:
|
||||
if hasattr(dev:=Device[p.device], 'invalidate_caches'): dev.invalidate_caches()
|
||||
else:
|
||||
with Context(DEBUG=0, BEAM=0, CAPTURING=0, TRACK_MATCH_STATS=0): Tensor.ones(1024,1024).contiguous().realize(do_update_stats=False)
|
||||
tms.append(cast(float, car(input_bufs, var_vals, wait=True))*factor)
|
||||
if early_stop is not None and early_stop < min(tms): break
|
||||
return tms
|
||||
|
||||
class TimeoutException(Exception): pass
|
||||
def timeout_handler(signum, frame): raise TimeoutException()
|
||||
|
||||
def _try_compile_linearized_w_idx(x:tuple[int,Kernel], compiler:Compiler) -> tuple[int, tuple[ProgramSpec, bytes, float]|None]:
|
||||
if hasattr(signal, "alarm"):
|
||||
signal.signal(getattr(signal, 'SIGALRM'), timeout_handler)
|
||||
# set timeout
|
||||
signal.alarm(getenv("BEAM_TIMEOUT_SEC", 10))
|
||||
ret = None
|
||||
try:
|
||||
p = get_program(x[1].copy().get_optimized_ast(name_override="test"), x[1].opts)
|
||||
assert p.uops is not None, "uop list wasn't generated?"
|
||||
if len(p.uops) >= (uops_max:=getenv("BEAM_UOPS_MAX", 3000)) > 0:
|
||||
if getenv("BEAM_LOG_SURPASS_MAX"): print(f"too many uops. {len(p.uops)=}, {uops_max=}")
|
||||
raise RuntimeError("too many uops")
|
||||
st = time.perf_counter()
|
||||
prog = compiler.compile(p.src)
|
||||
et = time.perf_counter() - st
|
||||
ret = (p, prog, et)
|
||||
except RuntimeError:
|
||||
if DEBUG >= 4: traceback.print_exc()
|
||||
except Exception as e:
|
||||
if getenv("BEAM_STRICT_MODE"): raise e
|
||||
finally:
|
||||
if hasattr(signal, "alarm"): signal.alarm(0)
|
||||
return x[0], ret
|
||||
|
||||
# workers should not open devices and should ignore ctrl c and should not launch VIZ
|
||||
def _init_worker():
|
||||
Context(ALLOW_DEVICE_USAGE=0, VIZ=0, TRACK_MATCH_STATS=0).__enter__()
|
||||
signal.signal(signal.SIGINT, signal.SIG_IGN)
|
||||
|
||||
def _ensure_buffer_alloc(bufs:list[Buffer]) -> list[Buffer]: return [buf.ensure_allocated() if buf is not None else buf for buf in bufs]
|
||||
|
||||
# *** external API ***
|
||||
|
||||
# get (scrap) buffers for timing the linearizer
|
||||
def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
|
||||
bufsts: defaultdict[int, list[UOp]] = defaultdict(list)
|
||||
for x in lin.bufs:
|
||||
if x.src[0].base.op is Ops.DEFINE_GLOBAL: bufsts[x.src[0].base.arg].append(x)
|
||||
# TODO: Nones are staying in here if buffers are optimized out!
|
||||
# TODO: add a test for this
|
||||
rawbufs: list[Buffer|None] = [None]*(max(bufsts)+1)
|
||||
for k,lx in bufsts.items():
|
||||
buf_size = prod(dtype.shape) if isinstance(dtype:=lx[0].src[0].dtype, ImageDType) else max(y.st_arg.real_size() for y in lx)
|
||||
assert isinstance(dtype, (PtrDType, ImageDType))
|
||||
if buf_size == 0: buf_size = 1 # create a size 1 buffer if no cell is accessed in kernel. # TODO: remove from kernel input in this case.
|
||||
buf_dtype = dtype if isinstance(dtype, ImageDType) else dtype.base
|
||||
rawbufs[k] = Buffer(lin.opts.device, buf_size, buf_dtype).allocate() if allocate else Buffer(lin.opts.device, buf_size, buf_dtype)
|
||||
#assert all(r is not None for r in rawbufs)
|
||||
return cast(list[Buffer], rawbufs)
|
||||
|
||||
# get dictionary of all possible actions
|
||||
def get_kernel_actions(lin:Kernel, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel]:
|
||||
acted_lins, max_up, max_lcl = {0:lin} if include_0 else {}, getenv("BEAM_UPCAST_MAX", 256), getenv("BEAM_LOCAL_MAX", 1024)
|
||||
kernel_actions = (actions if candidates is None else candidates).copy()
|
||||
|
||||
if TC_SEARCH_OVER_SHAPE and len(lin.applied_opts) == 0: # tensor core opts must be first
|
||||
for i, action in enumerate(kernel_actions):
|
||||
if action.op == OptOps.TC and (tc_arg := cast(tuple, action.arg))[0] == -1:
|
||||
# replace every tc_action with default tc with one tc_action for each available tc
|
||||
kernel_actions[i:i+1] = \
|
||||
[Opt(op=OptOps.TC, axis=action.axis, arg=(tc_select, tc_arg[1], tc_arg[2])) for tc_select,_ in enumerate(lin.opts.tensor_cores)]
|
||||
|
||||
for i,a in enumerate(kernel_actions):
|
||||
if a.axis is not None and a.op is not OptOps.TC:
|
||||
try: ax = lin.real_axis(a.op, a.axis)
|
||||
except KernelOptError: continue
|
||||
if (ax >= lin.shape_len) or (lin.full_shape[ax] == a.arg and Opt(a.op, a.axis, 0) in kernel_actions): continue
|
||||
lin2 = lin.copy()
|
||||
try:
|
||||
lin2.apply_opt(a)
|
||||
up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if (tc:=lin2.tensor_core) else 1
|
||||
for s,c in zip(lin2.full_shape, lin2.axis_types):
|
||||
if c in (AxisType.UPCAST, AxisType.UNROLL): up *= s
|
||||
elif c in (AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
|
||||
if up//tc_up > max_up or lcl > max_lcl:
|
||||
if getenv("BEAM_LOG_SURPASS_MAX"): print(f"too many upcast/local. {up//tc_up=}, {max_up=}, {lcl=}, {max_lcl=}")
|
||||
continue
|
||||
acted_lins[i+1] = lin2
|
||||
except KernelOptError: pass
|
||||
return acted_lins
|
||||
|
||||
beam_pool, BEAM_DEBUG = None, getenv("BEAM_DEBUG")
|
||||
def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value) -> Kernel:
|
||||
global beam_pool
|
||||
key = {"ast": lin.ast.key, "amt": amt, "allow_test_size": allow_test_size, "device": lin.opts.device, "suffix": lin.opts.suffix}
|
||||
if not disable_cache and CACHELEVEL >= 1 and (val:=diskcache_get("beam_search", key)) is not None:
|
||||
ret = lin.copy()
|
||||
for o in val[len(lin.applied_opts):]: ret.apply_opt(o)
|
||||
return ret
|
||||
|
||||
beam: list[tuple[Kernel, float]] = [(lin, float("inf"))]
|
||||
seen_libs = set()
|
||||
|
||||
default_parallel = multiprocessing.cpu_count() if lin.opts.device in {"CUDA", "AMD", "NV", "METAL", "HIP"} else 0
|
||||
if beam_pool is None and (workers := getenv("PARALLEL", default_parallel)):
|
||||
beam_pool = multiprocessing.get_context("spawn").Pool(workers, _init_worker, (), getenv("BEAM_MAX_TASKS_PER_CHILD", 16))
|
||||
@atexit.register
|
||||
def close_pool(): beam_pool.close()
|
||||
|
||||
min_progress = getenv("BEAM_MIN_PROGRESS", 0.01)/1e6
|
||||
if BEAM_DEBUG: print(f"BEAM_SEARCH:\n{lin.ast}")
|
||||
if DEBUG >= 2: print(f" 0.00s: from 1 -> 1 actions {lin.colored_shape()}")
|
||||
|
||||
try:
|
||||
rawbufs = _ensure_buffer_alloc(rawbufs)
|
||||
var_vals: dict[Variable, int] = {k:int(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
|
||||
exiting, st = False, time.perf_counter()
|
||||
dev = Device[lin.opts.device]
|
||||
while not exiting:
|
||||
acted_lins: list[Kernel] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
|
||||
timed_lins: list[tuple[Kernel, float]] = []
|
||||
_compile_fn = functools.partial(_try_compile_linearized_w_idx, compiler=dev.compiler)
|
||||
least_compute_ops = math.inf
|
||||
for i,proc in (map(_compile_fn, enumerate(acted_lins)) if beam_pool is None else beam_pool.imap_unordered(_compile_fn, enumerate(acted_lins))):
|
||||
if proc is None: continue
|
||||
p, lib, compile_et = proc
|
||||
if lib in seen_libs: continue
|
||||
# filter out kernels that use 1000x more compute than the smallest
|
||||
least_compute_ops = min(this_compute_ops:=sym_infer(p.estimates.ops, var_vals), least_compute_ops)
|
||||
if least_compute_ops*1000 < this_compute_ops: continue
|
||||
seen_libs.add(lib)
|
||||
try: tms = _time_program(p, lib, var_vals, rawbufs, early_stop=beam[0][1]*3 if len(beam) else 1.0,
|
||||
allow_test_size=allow_test_size, clear_l2=hasattr(dev, 'invalidate_caches'))
|
||||
except Exception as e:
|
||||
if BEAM_DEBUG: print(f"BEAM failed for opts: {acted_lins[i].applied_opts}\n{e}")
|
||||
if isinstance(e, RuntimeError): continue
|
||||
raise
|
||||
timed_lins.append((acted_lins[i], min(tms)))
|
||||
if BEAM_DEBUG > 1: print(f"{time.perf_counter() - st:7.2f}s: {i:5d} {len(cast(list, p.uops)):5d} uops {time_to_str(compile_et, w=12)} compile/{time_to_str(timed_lins[-1][1], w=12)} run {len(timed_lins):4d}/{len(acted_lins):4d} {timed_lins[-1][0].colored_shape()}") # noqa: E501
|
||||
elif DEBUG >= 2: print(f"\r{time.perf_counter() - st:7.2f}s: {time_to_str(timed_lins[-1][1], w=12)} {len(timed_lins):4d}/{len(acted_lins):4d} {timed_lins[-1][0].colored_shape()}\033[K", end="") # noqa: E501
|
||||
|
||||
# done
|
||||
opts = sorted(timed_lins, key=lambda x: x[1])
|
||||
exiting = len(opts) == 0 or (opts[0][1] < min_progress) or (len(beam) > 0 and ((beam[0][1]-opts[0][1]) < min_progress))
|
||||
if not exiting: beam = opts[:amt]
|
||||
elif len(opts) > 0 and opts[0][1] < beam[0][1]: beam = opts[:1]
|
||||
if DEBUG >= 2: print(f"\r{time.perf_counter() - st:7.2f}s:", colored(time_to_str(beam[0][1], w=12), "green" if exiting else None), f"from {len(acted_lins):3d} -> {len(opts):3d} actions\033[K", beam[0][0].colored_shape()) # noqa: E501
|
||||
except KeyboardInterrupt as e:
|
||||
if beam_pool is not None: beam_pool.terminate()
|
||||
raise e
|
||||
|
||||
if CACHELEVEL >= 1: diskcache_put("beam_search", key, beam[0][0].applied_opts)
|
||||
if BEAM_DEBUG: print(f"BEAM_SEARCH: final tm={time_to_str(beam[0][1], w=0)}, applied_opts={beam[0][0].applied_opts}")
|
||||
return beam[0][0]
|
||||
@@ -0,0 +1,135 @@
|
||||
from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, resolve, sint
|
||||
from tinygrad.helpers import all_same, prod, unwrap, colored
|
||||
from tinygrad.shape.shapetracker import ShapeTracker
|
||||
from tinygrad.shape.view import View, strides_for_shape, get_contraction_with_reduce
|
||||
from tinygrad.schedule.grouper import ALWAYS_CONTIGUOUS
|
||||
from tinygrad.dtype import ImageDType, dtypes
|
||||
|
||||
merge_views = PatternMatcher([
|
||||
# merge adjacent views
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
|
||||
# replace MovementOps with VIEW
|
||||
(UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
|
||||
# remove NOOP views
|
||||
(UPat.var("x").view(name="view"),
|
||||
lambda x,view: x if x.st is not None and x.op not in GroupOp.Defines and view.st.contiguous and view.shape == x.shape else None),
|
||||
(UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
|
||||
lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
|
||||
# only unmaksed VIEW on CONST replaces the ShapeTracker
|
||||
(UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
|
||||
lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
|
||||
])
|
||||
|
||||
def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
|
||||
# contiguous, expand, and the same with ones removed
|
||||
if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
|
||||
tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
|
||||
new_shape: list[sint] = []
|
||||
new_reduce_axis = []
|
||||
if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
|
||||
for i,pairs in enumerate(contraction):
|
||||
new_shape_chunk = [view.shape[p] for p in pairs]
|
||||
if i in r.arg[1]:
|
||||
# if this is a reduce axis, we need a 1 in the view here to put it
|
||||
assert len(new_shape_chunk) > 0
|
||||
new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
|
||||
new_reduce_axis.append(len(new_shape)-1)
|
||||
else:
|
||||
# otherwise, pass through the new_shape_chunk
|
||||
new_shape += new_shape_chunk
|
||||
ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
|
||||
assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
|
||||
return ret
|
||||
return None
|
||||
|
||||
view_left = merge_views+PatternMatcher([
|
||||
# view before elementwise and buffer ops
|
||||
(UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.STORE, Ops.VALID, Ops.SINK}, name="e"),), name="view"),
|
||||
lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
|
||||
# if there's ones added after reduce, put this before the reduce
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
|
||||
])
|
||||
|
||||
view_left_through_load = PatternMatcher([
|
||||
# view before load
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.LOAD, name="e"),), name="view"),
|
||||
lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
|
||||
])
|
||||
|
||||
def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
|
||||
|
||||
# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
|
||||
def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
|
||||
# contiguous and same size can push to children
|
||||
# if there's a reduce child, shapes match with ones removed
|
||||
if unwrap(view.st).contiguous and view.size == r.size and \
|
||||
(not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
|
||||
tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
|
||||
return None
|
||||
# swizzle the input
|
||||
input_st = ShapeTracker.from_shape(src.shape)
|
||||
tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
|
||||
prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
|
||||
strides = strides_for_shape(rshape)
|
||||
nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
|
||||
v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
|
||||
new_view = tmp + ShapeTracker(tuple(nv))
|
||||
swizzled_input = apply_swizzle(src.view(new_view))
|
||||
# create a new reduceop
|
||||
new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
|
||||
if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
|
||||
else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
|
||||
return red.reshape(view.shape)
|
||||
|
||||
def reduceop_view_right(src:UOp, v:UOp, r:UOp):
|
||||
assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
|
||||
new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
|
||||
return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
|
||||
|
||||
def elementwise_view_right(root:UOp):
|
||||
if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
|
||||
assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
|
||||
# place view after applying the elementwise op
|
||||
new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
|
||||
new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
|
||||
# reshape to match downstream shapes
|
||||
return root.replace(src=tuple(new_src)).reshape(root.shape)
|
||||
|
||||
# push VIEW to children
|
||||
view_right = merge_views+PatternMatcher([
|
||||
# push a non contiguous ShapeTracker through reduceop
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
|
||||
# apply view after reduceops
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
|
||||
# apply view after elementwise ops
|
||||
(UPat(GroupOp.All-{Ops.SINK, Ops.REDUCE_AXIS}, name="root"), elementwise_view_right),
|
||||
# merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
|
||||
lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
|
||||
# remove view from sink
|
||||
(UPat(Ops.VIEW, name="v").sink(name="sink"), lambda v,sink: v.src[0].sink(arg=sink.arg)),
|
||||
])
|
||||
|
||||
def check_load_st(glbl:UOp, view:UOp):
|
||||
if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
|
||||
# if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
|
||||
if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
|
||||
# if it has a single view and it's equal when you shrink a contig, it's fine
|
||||
if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
|
||||
# otherwise, it's not fine
|
||||
raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
|
||||
+colored(" - a += a.T\n", "red")+colored(" + a += a.T.contiguous()", "green"))
|
||||
|
||||
fix_kernel_ops = view_left_through_load+PatternMatcher([
|
||||
# add view to LOAD and STORE
|
||||
(UPat(Ops.DEFINE_GLOBAL, name="g").load(), lambda g: g.view(g.st).load()),
|
||||
(UPat(Ops.DEFINE_GLOBAL, name="g").store(UPat.var('x')), lambda g,x: g.view(g.st).store(x)),
|
||||
# VALID
|
||||
(UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"),
|
||||
lambda self: UOp.where(UOp(Ops.VALID, dtypes.bool, (UOp(Ops.VIEW, arg=self.st),)), self.const_like(self.base.arg), 0)),
|
||||
# no ImageDType after index
|
||||
(UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW, Ops.INDEX}, name="x"),
|
||||
lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
|
||||
# if this kernel also assigns to the loaded buffer, ensure we can index it correctly
|
||||
(UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
|
||||
])
|
||||
@@ -0,0 +1,136 @@
|
||||
import math, functools
|
||||
from dataclasses import dataclass
|
||||
from tinygrad.dtype import DType, dtypes
|
||||
from tinygrad.helpers import getenv
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class TensorCore: # D = A * B + C, A is (M x K), B is (K x N), C and D are (M x N)
|
||||
dims: tuple[int,int,int] # N, M, K
|
||||
threads: int # number of threads that construct the warp
|
||||
elements_per_thread: tuple[int, int, int] # elements per-thread to load/store from A/B/C
|
||||
dtype_in: DType # dtype for A and B
|
||||
dtype_out: DType # dtype for C and D
|
||||
opts: tuple[str, ...] # ordered tuple of "ux" or "lx" specifying kernel opts to perform. "ux" upcasts dim x and "lx" localizes dim x
|
||||
# (local_swizzle, upcast_swizzle, reduce_swizzle)
|
||||
# l<num> is the num axis of the locals, similar for u<num> and upcasts, r<num> and reduces
|
||||
swizzle: tuple[tuple[tuple[str, ...], tuple[str, ...], tuple[str, ...]], tuple[tuple[str, ...], tuple[str, ...], tuple[str, ...]]]
|
||||
@functools.cache # pylint: disable=method-cache-max-size-none
|
||||
def _remaps(self) -> list[dict[str, str]]:
|
||||
local_axes, upcast_axes, reduce_axes = len(self.get_local_axes()), len(self.get_upcast_axes()), len(self.get_reduce_axes())
|
||||
fwd_st = [f"l{i}" for i in range(local_axes)] + [f"u{i}" for i in range(upcast_axes)] + [f"r{i}" for i in range(reduce_axes)]
|
||||
return [dict(zip(fwd_st, sum(s, ()))) for s in self.swizzle]
|
||||
def permutes_for_shape_str(self, shape_str:list[str]) -> tuple[tuple[int, ...], tuple[int, ...]]:
|
||||
ret = [[shape_str.index(remap[ss]) if ss in remap else i for i,ss in enumerate(shape_str)] for remap in self._remaps()]
|
||||
return tuple(ret[0]), tuple(ret[1])
|
||||
@functools.cache # pylint: disable=method-cache-max-size-none
|
||||
def base_shape_str(self) -> list[str]:
|
||||
ret = []
|
||||
cnt = {'u': 0, 'l': 0}
|
||||
for opt in self.opts:
|
||||
ret.append(f"{opt[0]}{cnt[opt[0]]}")
|
||||
cnt[opt[0]] += 1
|
||||
# assumes you do the UNROLL after the opts
|
||||
return ret + [f"r{i}" for i in range(len(self.get_reduce_axes()))]
|
||||
def get_reduce_axes(self): return [(i, 2) for i in range(int(math.log2(self.dims[2])))]
|
||||
def get_upcast_axes(self): return [opt for opt in self.opts if opt[0] == "u"]
|
||||
def get_local_axes(self): return [opt for opt in self.opts if opt[0] == "l"]
|
||||
def base_upcast_axes(self):
|
||||
# this is defined in the swizzle. first we use the upcast axes, then the reduce
|
||||
return ([f"r{i}" for i in range(len(self.get_reduce_axes()))] + [f"u{i}" for i in range(len(self.get_upcast_axes()))])[::-1]
|
||||
def __str__(self): return "_".join(["WMMA"] + list(map(str, self.dims)) + [self.dtype_in.name, self.dtype_out.name])
|
||||
def __post_init__(self):
|
||||
# all axes have size 2, <local> <reduce> <upcast> is the order
|
||||
local_axes, upcast_axes, reduce_axes = len(self.get_local_axes()), len(self.get_upcast_axes()), len(self.get_reduce_axes())
|
||||
assert self.dims[0] * self.dims[1] == 2**(local_axes + upcast_axes), \
|
||||
f"N({self.dims[0]}) x M({self.dims[1]}) != local({2**local_axes}) x upcast({2**upcast_axes}) with opts({self.opts})"
|
||||
assert 2**local_axes == self.threads, f"{self.threads} threads construct the warp but found {2**local_axes} in {self.opts}"
|
||||
assert 2**upcast_axes == self.elements_per_thread[2], \
|
||||
f"{self.elements_per_thread[2]} elements from C are processed per thread but found {2**upcast_axes} in {self.opts}"
|
||||
# check dims match opts
|
||||
assert self.dims[0] == 2**len(gd:=[x for x in self.opts if x[1] == '0']), f"opts wrong on dims[0], {self.dims[0]} vs {gd}"
|
||||
assert self.dims[1] == 2**len(gd:=[x for x in self.opts if x[1] == '1']), f"opts wrong on dims[1], {self.dims[1]} vs {gd}"
|
||||
# NOTE: the K opts is implictly set by the dim
|
||||
# check swizzle
|
||||
assert len(self.swizzle[0]) == 3 and len(self.swizzle[1]) == 3, "swizzle has wrong part count"
|
||||
assert len(self.swizzle[0][0]) == len(self.swizzle[1][0]) == local_axes, "local swizzle size is wrong"
|
||||
assert len(self.swizzle[0][1]) == len(self.swizzle[1][1]) == upcast_axes, "upcast swizzle size is wrong"
|
||||
assert len(self.swizzle[0][2]) == len(self.swizzle[1][2]) == reduce_axes, "reduce swizzle size is wrong"
|
||||
assert all(len(s) == local_axes+upcast_axes+reduce_axes for s in self._remaps()), "remaps are the wrong size"
|
||||
# check elements_per_thread
|
||||
un, ln = 0, 0
|
||||
zero_stride_0 = []
|
||||
zero_stride_1 = []
|
||||
for o in self.opts:
|
||||
if o[1] == '0': zero_stride_0.append(o[0] + str(un if o[0] == 'u' else ln))
|
||||
if o[1] == '1': zero_stride_1.append(o[0] + str(un if o[0] == 'u' else ln))
|
||||
if o[0] == 'u': un += 1
|
||||
if o[0] == 'l': ln += 1
|
||||
# NOTE: all the zero_stride dims can be placed in any order in the swizzle
|
||||
upcasted_0 = [x for x in (self.swizzle[0][1] + self.swizzle[0][2]) if x not in zero_stride_0 and x[0] != 'l']
|
||||
upcasted_1 = [x for x in (self.swizzle[1][1] + self.swizzle[1][2]) if x not in zero_stride_1 and x[0] != 'l']
|
||||
assert 2**len(upcasted_0) == self.elements_per_thread[0], f"mismatch in elements_per_thread[0], {upcasted_0} vs {self.elements_per_thread[0]}"
|
||||
assert 2**len(upcasted_1) == self.elements_per_thread[1], f"mismatch in elements_per_thread[1], {upcasted_1} vs {self.elements_per_thread[1]}"
|
||||
|
||||
# ***** NVIDIA *****
|
||||
|
||||
cuda_tc_opts = ("u0","l0","l0","l1","l1","l1","u1") # shared by all shapes with M=16 N=8
|
||||
|
||||
# https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-matrix-multiply-accumulate-instructions
|
||||
cuda_81616 = [TensorCore(dims=(8,16,16), threads=32, elements_per_thread=(8,4,4), dtype_in=di, dtype_out=do, opts=cuda_tc_opts,
|
||||
swizzle=((('r1', 'r2', 'l2', 'l3', 'l4'), ('u1', 'r3'), ('l0', 'l1', 'u0', 'r0')),
|
||||
(('r1', 'r2', 'u0', 'l0', 'l1'), ('r0', 'r3'), ('l2', 'l3', 'l4', 'u1'))))
|
||||
for di,do in [(dtypes.half,dtypes.float), (dtypes.bfloat16,dtypes.float), (dtypes.half,dtypes.half)]]
|
||||
cuda_8168_f16 = [TensorCore(dims=(8,16,8), threads=32, elements_per_thread=(4,2,4), dtype_in=di, dtype_out=do, opts=cuda_tc_opts,
|
||||
swizzle=((('r1', 'r2', 'l2', 'l3', 'l4'), ('r0', 'u1'), ('l0', 'l1', 'u0')),
|
||||
(('r1', 'r2', 'u0', 'l0', 'l1'), ('u1', 'r0'), ('l2', 'l3', 'l4'))))
|
||||
for di,do in [(dtypes.half,dtypes.float), (dtypes.half,dtypes.half)]]
|
||||
cuda_8168_tf32 = [TensorCore(dims=(8,16,8), threads=32, elements_per_thread=(4,2,4), dtype_in=dtypes.float, dtype_out=dtypes.float, opts=cuda_tc_opts,
|
||||
swizzle=((('r0', 'r1', 'l2', 'l3', 'l4'), ('u1', 'r2'), ('l0', 'l1', 'u0')),
|
||||
(('r0', 'r1', 'u0', 'l0', 'l1'), ('u1', 'r2'), ('l2', 'l3', 'l4'))))]
|
||||
cuda_sm80: list[TensorCore] = cuda_81616 + cuda_8168_f16
|
||||
if getenv("ALLOW_TF32", 0): cuda_sm80 += cuda_8168_tf32
|
||||
cuda_sm75: list[TensorCore] = cuda_8168_f16
|
||||
|
||||
# ***** AMD *****
|
||||
|
||||
# https://gpuopen.com/learn/wmma_on_rdna3/
|
||||
amd_rdna3 = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(16,16,8), dtype_in=di, dtype_out=do,
|
||||
opts=("l0","l0","l0","l0","l1","u1","u1","u1"),
|
||||
swizzle=((('l4', 'u0', 'u1', 'u2', 'l0'), ('r1', 'r2', 'r3'), ('l1', 'l2', 'l3', 'r0')),
|
||||
(('l0', 'l1', 'l2', 'l3', 'l4'), ('r1', 'r2', 'r3'), ('u0', 'u1', 'u2', 'r0'))))
|
||||
for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float)]]
|
||||
amd_rdna4 = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(8,8,8), dtype_in=di, dtype_out=do,
|
||||
opts=("l0","l0","l0","l0","u1","u1","u1","l1"),
|
||||
swizzle=((('u0', 'u1', 'u2', 'l4', 'r2'), ('r0', 'r1', 'r3'), ('l0', 'l1', 'l2', 'l3')),
|
||||
(('l0', 'l1', 'l2', 'l3', 'r2'), ('r0', 'r1', 'r3'), ('l4', 'u0', 'u1', 'u2'))))
|
||||
for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float),(dtypes.bfloat16,dtypes.bfloat16)]]
|
||||
|
||||
# https://gpuopen.com/learn/amd-lab-notes/amd-lab-notes-matrix-cores-readme
|
||||
amd_cdna = [TensorCore(dims=(16,16,16), threads=64, elements_per_thread=(4,4,4), dtype_in=di, dtype_out=do,
|
||||
opts=("l0","l0","l0","l0","u1","u1","l1","l1"),
|
||||
swizzle=((('u0', 'u1', 'l4', 'l5', 'r2', 'r3'), ('r0', 'r1'), ('l0', 'l1', 'l2', 'l3')),
|
||||
(('l0', 'l1', 'l2', 'l3', 'r2', 'r3'), ('r0', 'r1'), ('l4', 'l5', 'u0', 'u1'))))
|
||||
for di,do in [(dtypes.half,dtypes.float),(dtypes.bfloat16,dtypes.float)]]
|
||||
|
||||
# ***** Apple Metal *****
|
||||
|
||||
metal = [TensorCore(dims=(8,8,8), threads=32, elements_per_thread=(2,2,2), dtype_in=di, dtype_out=do,
|
||||
opts=("u0","l0","l1","l1","l0","l1"),
|
||||
swizzle=((('r1', 'l1', 'l2', 'r2', 'l4'), ('r0',), ('u0', 'l0', 'l3')),
|
||||
(('l0', 'r0', 'r1', 'l3', 'r2'), ('u0',), ('l1', 'l2', 'l4'))))
|
||||
for di,do in [(dtypes.float,dtypes.float),(dtypes.half,dtypes.float),
|
||||
(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float),(dtypes.bfloat16,dtypes.bfloat16)]]
|
||||
|
||||
# ***** Apple AMX *****
|
||||
|
||||
amx = [TensorCore(dims=(sz,sz,1), threads=1, elements_per_thread=(sz,sz,sz*sz), dtype_in=dt, dtype_out=dt,
|
||||
swizzle=(((), ('u0', 'u1', 'u2', 'u3', 'u4', 'u5', 'u6', 'u7'), ()),
|
||||
((), ('u4', 'u5', 'u6', 'u7', 'u0', 'u1', 'u2', 'u3'), ())),
|
||||
opts=("u0","u0","u0","u0","u1","u1","u1","u1")) for dt,sz in [(dt, 64 // dt.itemsize) for dt in [dtypes.float]]]
|
||||
|
||||
# ***** Intel ****
|
||||
|
||||
intel = [TensorCore(dims=(8,8,16), threads=8, elements_per_thread=(16,16,8), dtype_in=dtypes.half, dtype_out=dtypes.float,
|
||||
opts=("l0","l0","l0","u1","u1","u1"),
|
||||
swizzle=((('r1', 'r2', 'r3'), ('u0', 'u1', 'u2'), ('l0', 'l1', 'l2', 'r0')),
|
||||
(('l0', 'l1', 'l2'), ('r1', 'r2', 'r3'), ('u0', 'u1', 'u2', 'r0'))))]
|
||||
Reference in New Issue
Block a user