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190 lines
11 KiB
190 lines
11 KiB
import itertools
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from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
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from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS, TC_OPT, TC_SELECT, USE_TC, AMX
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from tinygrad.dtype import ImageDType
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from tinygrad.uop.ops import Ops, resolve, AxisType
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from tinygrad.codegen.opt.postrange import Scheduler
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def hand_coded_optimizations(k:Scheduler) -> Scheduler:
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# first try the tensor cores
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""" Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
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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).
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Keyword arguments:
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use_tensor_cores -- controls how tensor cores are applied (default 1)
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0: will disable any tensor core matching
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1: enable tensor cores
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2: apply tensor core shape but don't use UOp.WMMA
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extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None)
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tc_select -- specifies which tensor core(s) to use for optimization (default -1)
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-1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes)
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[0-N]: uses only the n'th tensor core available; useful for search
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tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise)
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0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL
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1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
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2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
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"""
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# NOTE: unless TC_OPT is > 0, we only trigger tensor cores if there's only one reduce axis
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if USE_TC > 0 and (len(k.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)) == 1 or (TC_OPT.value >= 1)):
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good_tc_opt = False
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tk = k.copy()
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try: # check TC first and apply hand-coded opts if successful
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rngs = tk.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, USE_TC.value)))
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good_tc_opt = True
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except KernelOptError:
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pass
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# skip hand-coded TC opts if AMX, upcasting will make kernel slower
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if good_tc_opt and not AMX:
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if rngs is not None:
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for tc_dim in [1,0]: # attempt to upcast M and N
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szs = [sz for sz in [5,4,3,2] if rngs[tc_dim].src[0].divides(sz) is not None]
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if szs:
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# set it to the replaced range
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rngs[tc_dim] = tk.apply_opt(Opt(OptOps.UPCAST, tk.rngs.index(rngs[tc_dim]), szs[0]))[0]
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if (szs := [sz for sz in [4,2] if rngs[0].src[0].divides(sz) is not None]): # attempt to local N
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tk.apply_opt(Opt(OptOps.LOCAL, tk.rngs.index(rngs[0]), szs[0]))
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return tk
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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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# upcast float4 images, this must be early so we don't accidentally add locals before the upcast
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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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# part of is_expanded
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unit_stride_axes_mul_4 = [k.rngs.index(c) for c in k.bufs[buf_index].src[1].get_idx().split_uop(Ops.ADD) if
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c.op is Ops.RANGE and (c.vmax+1)%4 == 0]
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if len(unit_stride_axes_mul_4):
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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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# 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.ren.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.ren.has_shared and \
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(mulop:=k.reduceop.src[0]).op is Ops.MUL and mulop.src[0].op is Ops.INDEX and mulop.src[1].op is Ops.INDEX:
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idx0, idx1 = mulop.src[0].src[1].get_idx(), mulop.src[1].src[1].get_idx()
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if k.ranges_of(AxisType.REDUCE):
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first_reduce_rng = k.ranges_of(AxisType.REDUCE)[0]
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if any(u is first_reduce_rng for u in idx0.split_uop(Ops.ADD)) and all(r in idx1.ranges for r in idx0.ranges):
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for global_idx in k.axes_of(AxisType.GLOBAL):
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if first_reduce_rng.src[0].divides(MV_THREADS_PER_ROW) is not None 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_rng.render()} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
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try:
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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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except KernelOptError: pass
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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
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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) <= (240 if NOLOCALS else 2048), False):
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for axis, sz in itertools.product((0, 1, 2), (16,)):
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try:
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k.apply_opt(Opt(OptOps.GROUPTOP, axis, sz))
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break
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except KernelOptError: pass
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# no more opt if we are grouping
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if k.group_for_reduces: return k
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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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# for Schedule, we check if the range is used in INDEX gates or WHERE gates
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is_masked = any(any(o is k.rngs[axis] for o in u.src[0].backward_slice) for u in k.ast.backward_slice if u.op is Ops.WHERE)
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if k.full_shape[axis] <= 7 and is_masked and 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.ren is not None and k.ren.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) and (k.upcast_size() < 32):
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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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rng = k.rngs[axis]
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if any(rng not in b.src[1].get_idx().backward_slice and all(r2 in b.src[1].get_idx().backward_slice
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for r2 in k.ranges_of(AxisType.UPCAST, AxisType.UNROLL)) for b in k.bufs):
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num_strides, sum_strides = 0, 0
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for b in k.bufs:
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idx = b.src[1].get_idx()
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if rng in idx.backward_slice: num_strides += 1
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for c in idx.split_uop(Ops.ADD):
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if c is rng: sum_strides += 1
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if c.op is Ops.MUL and c.src[0] is rng and c.src[1].op is Ops.CONST: sum_strides += c.src[1].arg
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if c.op is Ops.MUL and c.src[1] is rng and c.src[0].op is Ops.CONST: sum_strides += c.src[0].arg
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xb_choices.append((num_strides, sum_strides, 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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if k.unrollable_dims and (k.upcast_size() <= 4 or not k.axes_of(AxisType.UNROLL)) and (k.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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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.ren.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(k.rngs[axis] not in b.src[1].get_idx().backward_slice for b in k.bufs), axis) \
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for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP) if k.rngs[axis].src[0].op is Ops.CONST]
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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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# **** threading ****
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if k.ren.has_threads and k.ren.global_max is not None:
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for threads in [32,16,12,8,6,5,4,3,2]:
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# Skip if too many threads. Heuristic: use about 128K ops per thread
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if threads > k.ren.global_max[0] or resolve(prod(k.full_shape) // (128 << 10) < threads): continue
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for axis in k.axes_of(AxisType.LOOP):
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if k.full_shape[axis] % threads == 0:
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try: k.apply_opt(Opt(OptOps.THREAD, axis, threads))
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except KernelOptError: pass
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break
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if k.applied_opts and k.applied_opts[-1].op is OptOps.THREAD: break
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return k
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